The author would like to thank first and foremost Dr. Paul Vitányi for his elaborate feedback and tremendous technical contributions to this work. Next I thank Dr. Peter Grünwald for ample feedback. I also thank my colleagues John Tromp and Ronald de Wolf. I thank my friends Dr. Kaihsu Tai and Ms. Anna Lissa Cruz for extensive feedback and experimental inputs. This thesis is dedicated to my grandparents, Edwin and Dorothy, for their equanimity and support. It is further dedicated in spirit to the memories of my mother, Theresa, for her compassion, and to my father, Salvatore for his deep insight and foresight.
This work was supported in part by the Netherlands BSIK/BRICKS project, and by NWO project 612.55.002, and by the IST Programme of the European Community, under the PASCAL Network of Excellence, IST2002506778.
R. Cilibrasi and P. Vitányi. Clustering by compression. IEEE Transactions on Information Theory, 51(4):15231545, 2005;
R. Cilibrasi, P.M.B. Vitányi, and R. de Wolf. Algorithmic clustering of music based on string compression. Computer Music Journal, pages 4967. A preliminary version appeared as
R. Cilibrasi, R. de Wolf, P. Vitányi, Algorithmic clustering of music, Proc IEEE 4th International Conference on Web Delivering of Music (WEDELMUSIC 2004), IEEE Comp. Soc. Press, 2004, 110117.
This work was reported in among others: “Software to unzip identity of unknown composers, New Scientist, 12 April 2003, by Hazel Muir. “Software sorts tunes,” Technology Research News, April 23/30, 2003, by Kimberly Patch; and “Classer musiques, langues, images, textes et genomes,” Pour La Science, 317(March 2004), 98–103, by JeanPaul Delahaye , (Pour la Science = Edition francaise de Scientific American).
R. Cilibrasi, P.M.B. Vitanyi, Automatic meaning discovery using Google,
http://xxx.lanl.gov/abs/cs.CL/0412098 (2004); followed by conference versions
R. Cilibrasi and P.M.B. Vitányi, Automatic Extraction of Meaning from the Web, 2006 IEEE International Symposium on Information Theory (ISIT 2006), Seattle, 2006; and
R. Cilibrasi, P.M.B. Vitányi, Similarity of objects and the meaning of words, Proc. 3rd Conf. Theory and Applications of Models of Computation (TAMC), 1520 May, 2006, Beijing, China. Lecture Notes in Computer Science, Vol. 3959, JinYi Cai, S. Barry Cooper, and Angsheng Li (Eds.), 2006; to the journal version
R. Cilibrasi and P.M.B. Vitányi. The Google similarity distance, IEEE Transactions on Knowledge and Data Engineering, To appear.
The supporting experimental data for the binary classification
experimental comparison with WordNet can be found at
http://www.cwi.nl/ ~cilibrar/googlepaper/appendix.eps
This work was reported in, among others, “A search for meaning,” New Scientist, 29 January 2005, p.21, by Duncan GrahamRowe; on the Web in “Deriving semantics meaning from Google results,” Slashdot— News for nerds, Stuff that matters, Discussion in the Science section, 29 January, 2005.
But certainly for the present age, which prefers the sign to the thing signified, the copy to the original, representation to reality, the appearance to the essence... illusion only is sacred, truth profane. Nay, sacredness is held to be enhanced in proportion as truth decreases and illusion increases, so that the highest degree of illusion comes to be the highest degree of sacredness. –Feuerbach, Preface to the second edition of The Essence of Christianity
This thesis concerns a remarkable new scientific development that advances the state of the art in the field of data mining, or searching for previously unknown but meaningful patterns in fully or semiautomatic ways. A substantial amount of mathematical theory is presented as well as very many (though not yet enough) experiments. The results serve to test, verify, and demonstrate the power of this new technology. The core ideas of this thesis relate substantially to data compression programs. For more than 30 years, data compression software has been developed and significantly improved with better models for almost every type of file. Until recently, the main driving interests in such technology were to economize on disk storage or network data transmission costs. A new way of looking at data compressors and machine learning allows us to use compression programs for a wide variety of problems.
In this thesis a few themes are important. The first is the use of data compressors in new ways. The second is a new tree visualization technique. And the third is an informationtheoretic connection of a web search engine to the data mining system. Let us examine each of these in turn.
The first theme concerns the statistical significance of compressed file sizes. Most computer users realize that there are freely available programs that can compress text files to about one quarter their original size. The less well known aspect of data compression is that combining two or more files together to create a larger single conglomerate archive file prior to compression often yields better compression in aggregate. This has been used to great advantage in widely popular programs like tar or pkzip, combining archival and compression functionality. Only in recent years have scientists begun to appreciate the fact that compression ratios signify a great deal of important statistical information. All of the experiments in this thesis make use of a group of compressible objects. In each case, the individual compressed sizes of each object are calculated. Then, some or all possible pairs of objects are combined and compressed to yield pairwise compressed sizes. It is the tiny variations in the pairwise compressed sizes that yields the surprisingly powerful results of the following experiments. The key concept to realize is that if two files are very similar in their contents, then they will compress much better when combined together prior to compression, as compared to the sum of the size of each separately compressed file. If two files have little or nothing in common, then combining them together would not yield any benefit over compressing each file separately.
Although the principle is intuitive to grasp, it has surprising breadth of applicability. By using even the simplest stringmatching type compression made in the 1970’s it is possible to construct evolutionary trees for animals fully automatically using files containing their mitochondrial gene sequence. One example is shown in Figure ??. We first construct a matrix of pairwise distances between objects (files) that indicate how similar they are. These distances are based on comparing compressed file sizes as described above. We can apply this to files of widely different types, such as music pieces or genetic codes as well as many other specialized domains. In Figure ??, we see a tree constructed from the similarity distance matrix based on the mitochondrial DNA of several species. The tree is constructed so that species with “similar” DNA are “close by” in the tree. In this way we may lend support to certain evolutionary theories.
Although simple compressors work, it is also easy to use the most advanced modern compressors with the theory presented in this thesis; these results can often be more accurate than simpler compressors in a variety of particular circumstances or domains. The main advantage of this approach is its robustness in the face of strange or erroneous data. Another key advantage is the simplicity and ease of use. This comes from the generality of the method: it works in a variety of different application domains and when using generalpurpose compressors it becomes a generalpurpose inference engine. Throughout this thesis there is a focus on coding theory and data compression, both as a theoretical construct as well as practical approximations thereof through actual data compression programs in current use. There is a connection between a particular code and a probability distribution and this simple theoretical foundation allows one to use data compression programs of all types as statistical inference engines of remarkable robustness and generality. In Chapter 3, we describe the Normalized Compression Distance (NCD), which formalizes the ideas we have just described. We report on a plethora of experiments in Chapter 6 showing applications in a variety of interesting problems in data mining using gene sequences, music, text corpora, and other inputs.
Custom open source software has been written to provide powerful new visualization capabilities. The CompLearn software system (Chapter ??) implements our theory and with it experiments of two types may be carried out: classification or clustering. Classification refers to the application of discrete labels to a set of objects based on a set of examples from a human expert. Clustering refers to arrangement of objects into groups without prior training or influence by a human expert. In this thesis we deal primarily with hierarchical or nested clustering in which a group of objects is arranged into a sort of binary tree. This clustering method is called the quartet method and will be discussed in detail later.
In a nutshell, the quartet method is a way to determine a best matching tree given some data that is to be understood in a hierarchical cluster. It is called the quartet method because it is based on the smallest unrooted binary tree, which happens to be two pairs of two nodes for a total of four nodes comprising the quartet. It adds up many such small binary trees together to evaluate a big tree and then adjusts the tree according to the results of the evaluation. After a time, a best fitting tree is declared and the interpretation of the experimental results is possible. Our compressionbased algorithms output a matrix of pairwise distances between objects. Because such a matrix is hard to interpret, we try to extract some of its essential features using the quartet method. This results in a tree optimized so that similar objects with small distances are placed nearby each other. The trees given in Figures 1.1, 1.2, and 1.3 (discussed below) have all been constructed using the quartet method.
The quartet tree search is nondeterministic. There are compelling theoretical reasons to suppose that the general quartet tree search problem is intractable to solve exactly for every case. But the method used here tries instead to approximate a solution in a reasonable amount of time, sacrificing accuracy for speed. It also makes extensive use of random numbers, and so there is sometimes variation in the results that the tree search produces. We describe the quartet tree method in detail in Chapter 4. In Chapter 6 we show numerous trees based on applying the quartet method and the NCD to a broad spectrum of input files in a wide array of domains.
It is possible to use coding theory to connect the compression approach to the web with the help of a search engine index database. By using a simple formula based on logarithms we can find “compressed sizes” of search terms. This was used in the Chinese tree in Figure 1.2. The tree of Nobel prize winning authors in Figure 1.3 was also made this way. As in the last example, a distance matrix is made, but this time with Google providing page count statistics that are converted to codelengths for use in the distance matrix calculations. We can see English and American writers clearly separated in the tree, as well as many other defensible placements. Another example using prime numbers with Google is in Chapter ??, page ??.
Throughout this thesis the reader will find ample experiments demonstrating the machine learning technology. There are objective experiments based on pure statistics using true data compressors and subjective experiments using statistics from web pages as well. There are examples taken from genetics, linguistics, literature, radio astronomy, optical character recognition, music, and many more diverse areas. Most of the experiments can be found in Chapters 4, 6, and 7.
The examples given above all dealt with clustering. It is also interesting to consider how we can use NCD to solve classification problems. Classification is the task of assigning labels to unknown test objects given a set of labeled training objects from a human expert. The goal is to try to learn the underlying patterns that the human expert is displaying in the choice of labellings shown in the training objects, and then to apply this understanding to the task of making predictions for unknown objects that are in some sense consistent with the given examples. Usually the problem is reduced to a combination of binary classification problems, where all target labels along a given dimension are either 0 or 1. In Chapter 5 we discuss this problem in greater detail, we give some information about a popular classification engine called the Support Vector Machine (SVM), and we connect the SVM to the NCD to create robust binary classifiers.
Each of the three key ideas (compression as learning, quartet tree visualization, and learning from the web) have a common thread: all of them serve to increase the generality and practical robustness of the machine intelligence compared to more traditional alternatives. This goal is not new and has already been widely recognized as fundamental. In this section a brief and subjective overview of the recent history of artificial intelligence is given to provide a broader context for this thesis.
In the beginning, there was the idea of artificial intelligence. As circuit miniaturization took off in the 1970’s, people’s imaginations soared with ideas of a new sort of machine with virtually unlimited potential: a (usually humanoid) metal automaton with the capacity to perform intelligent work and yet ask not one question out of the ordinary. A sort of ultraservant, made able to reason as well as man in most respects, yet somehow reasoning in a sort of rarefied form whereby the more unpredictable sides of human nature are factored out. One of the first big hurdles came as people tried to define just what intelligence was, or how one might codify knowledge in the most general sense into digital form. As Levesque and Brachman famously observed [], reasoning and representation are hopelessly intertwined, and just what intelligence is depends very much on just who is doing the talking.
Immediately upon settling on the question of intelligence one almost automatically must grapple with the concept of language. Consciousness and intelligence is experienced only internally, yet the objects to which it applies are most often external to the self. Thus there is at once the question of communication and experience and this straightaway ends any hope of perfect answers. Most theories on language are not theories in the formal sense []. A notable early exception is Quine’s famous observation that language translation is necessarily a dicey subject: for although you might collect very many pieces of evidence suggesting that a word means “X” or “Y”, you can never collect a piece of evidence that ultimately confirms that your understanding of the word is “correct” in any absolute sense. In a logical sense, we can never be sure that the meaning of a word is as it was meant, for to explain any word we must use other words, and these words themselves have only other words to describe them, in an interminable web of ontological disarray. Kantian empiricism leads us to pragmatically admit we have only the basis of our own internal experience to ground our understanding at the most basic level, and the mysterious results of the reasoning mind, whatever that might be.
It is without a doubt the case that humans throughout the world develop verbal and usually written language quite naturally. Recent theories by Smale [] have provided some theoretical support for empirical models of language evolution despite the formal impossibility of absolute certainty. Just the same it leaves us with a very difficult question: how do we make bits think?
Some twenty years later, progress has been bursty. We have managed to create some amazingly elegant search and optimization techniques including simplex optimization, tree search, curvefitting, and modern variants such as neural networks or support vector machines. We have built computers that can beat any human in chess, but we cannot yet find a computer smart enough to walk to the grocery store to buy a loaf of bread. There is clearly a problem of overspecialization in the types of successes we have so far enjoyed in artificial intelligence. This thesis explores my experience in charting this new landscape of concepts via a combination of pragmatic and principled techniques. It is only with the recent explosion in internet use and internet writing that we can now begin to seriously tackle these problems so fundamental to the original dream of artificial intelligence.
In recent years, we have begun to make headway in defining and implementing universal prediction, arguably the most important part of artificial intelligence. Most notable is Solomonoff prediction [], and the more practical analogs by Ryabko and Astola [] using data compression.
In classical statistical settings, we typically make some observations of a natural (or at the very least, measurable) phenomenon. Next, we use our intuition to “guess” which mathematical model might best apply. This process works well for those cases where the guesser has a good model for the phenomenon under consideration. This allows for at least two distinct modes of freedom: both in the choice of models, and also in the choice of criteria supporting “goodness”.
In the past the uneasy compromise has been to focus attention firstly on those problems which are most amenable to exact solution, to advance the foundation of exact and fundamental science. The next stage of growth was the advent of machineassisted exact sciences, such as the nowfamous fourcolor proof that required input (by hand!) of 1476 different graphs for computer verification (by a complicated program) that all were colorable before deductive extension to the most general case in the plane []. After that came the beginning of modern machine learning, based on earlier ideas of curve fitting and leastsquares regression. Neural networks, and later support vector machines, gave us convenient learning frameworks in the context of continuous functions. Given enough training examples, the theory assured us, the neural network would eventually find the right combination of weightings and multiplicative factors that would miraculously, and perhaps a bit circularly, reflect the underlying meaning that the examples were meant to teach. Just like spectral analysis that came before, each of these areas yielded a whole new broad class of solutions, but were essentially hit or miss in their effectiveness in each domain for reasons that remain poorly understood. The focus of my research has been on the use of data compression programs for generalized inference. It turns out that this modus operandi is surprisingly general in its useful application and yields oftentimes the most expedient results as compared to other more predetermined methods. It is often “one size fits all well enough” and this yields unexpected fruits. From the outset, it must be understood that the approach here is decidedly different than more classical ones, in that we avoid in most ways an exact statement of the problem at hand, instead deferring this until very near the end of the discussion, so that we might better appreciate what can be understood about all problems with a minimum of assumptions.
At this point a quote from Goldstein and Gigerenzer [] is appropriate:
What are heuristics? The Gestalt psychologists Karl Duncker and Wolfgang Koehler preserved the original Greek definition of “serving to find out or discover” when they used the term to describe strategies such as “looking around” and “inspecting the problem” (e.g., Duncker, 1935/1945).For Duncker, Koehler, and a handful of later thinkers, including Herbert Simon (e.g., 1955), heuristics are strategies that guide information search and modify problem representations to facilitate solutions. From its introduction into English in the early 1800s up until about 1970, the term heuristics has been used to refer to useful and indispensable cognitive processes for solving problems that cannot be handled by logic and probability theory (e.g., Polya, 1954; Groner, Groner, & Bischof, 1983). In the past 30 years, however, the definition of heuristics has changed almost to the point of inversion. In research on reasoning, judgment, and decision making, heuristics have come to denote strategies that prevent one from finding out or discovering correct answers to problems that are assumed to be in the domain of probability theory. In this view, heuristics are poor substitutes for computations that are too demanding for ordinary minds to carry out. Heuristics have even become associated with inevitable cognitive illusions and irrationality.
This author sides with Goldstein and Gigerenzer in the view that sometimes “less is more”; the very fact that things are unknown to the naive observer can sometimes work to his advantage. The recognition heuristic is an important, reliable, and conservative general strategy for inductive inference. In a similar vein, the NCD based techniques shown in this thesis provide a general framework for inductive inference that is robust against a wide variety of circumstances.
In this chapter a summary is provided for the remainder of the thesis as well as some historical context. In Chapter 2, an introduction to the technical details and terminology surrounding the methods is given. In chapter 3 we introduce the Normalized Compression Distance (NCD), the core mathematical formula that makes all of these experiments possible, and we establish connections between NCD and other wellknown mathematical formulas. In Chapter 4 a tree search system is explained based on groups of four objects at a time, the socalled quartet method. In Chapter 5 we combine NCD with other machine learning techniques such as Support Vector Machines. In Chapter 6, we provide a wealth of examples of this technology in action. All experiments in this thesis were done using the CompLearn Toolkit, an opensource general purpose data mining toolkit available for download from the http://complearn.org/ website. In Chapter 7, we show how to connect the internet to NCD using the Google search engine, thus providing the advanced sort of subjective analysis as shown in Figure 1.2. In Chapter 8 we use these techniques and others to trace the evolution of the legend of Saint Henry. In Chapter 9 we compare CompLearn against another older tree search software system called PHYLIP. Chapter 10 gives a snapshot of the online documentation for the CompLearn system. After this, a Dutch language summary is provided as well as a bibliography, index, and list of papers by R. Cilibrasi.
The spectacle is the existing order’s uninterrupted discourse about itself, its laudatory monologue. It is the selfportrait of power in the epoch of its totalitarian management of the conditions of existence. The fetishistic, purely objective appearance of spectacular relations conceals the fact that they are relations among men and classes: a second nature with its fatal laws seems to dominate our environment. But the spectacle is not the necessary product of technical development seen as a natural development. The society of the spectacle is on the contrary the form which chooses its own technical content. –Guy Debord, Society of the Spectacle
This chapter will give an informal introduction to relevant background material, familiarizing the reader with notation and basic concepts but omitting proofs. We discuss strings, languages, codes, Turing Machines and Kolmogorov complexity. This material will be extensively used in the chapters to come. For a more thorough and detailed treatment of all the material including a tremendous number of innovative proofs see []. It is assumed that the reader has a basic familiarity with algebra and probability theory as well as some rudimentary knowledge of classical information theory. We first introduce the notions of finite, infinite and string of characters. We go on to discuss basic coding theory. Next we introduce the idea of Turing Machines. Finally, in the last part of the chapter, we introduce Kolmogorov Complexity.
In the domain of mathematical objects discussed in this thesis, there are two broad categories: finite and infinite. Finite objects are those whose extent is bounded. Infinite objects are those that are “larger” than any given precise bound. For example, if we perform 100 flips of a fair coin in sequence and retain the results in order, the full record will be easily written upon a single sheet of A4 size paper, or even a business card. Thus, the sequence is finite. But if we instead talk about the list of all prime numbers greater than 5, then the sequence written literally is infinite in extent. There are far too many to write on any given size of paper no matter how big. It is possible, however, to write a computer program that could, in principle, generate every prime number, no matter how large, eventually, given unlimited time and memory. It is important to realize that some objects are infinite in their totality, but can be finite in a potential effective sense by the fact that every finite but a priori unbounded part of them can be obtained from a finite computer program. There will be more to say on these matters later in Section ??.
A bit, or binary digit, is just a single piece of information representing a choice between one of two alternatives, either 0 or 1.
A character is a symbol representing an atomic unit of written language that cannot be meaningfully subdivided into smaller parts. An alphabet is a set of symbols used in writing a given language. A language (in the formal sense) is a set of permissible strings made from a given alphabet. A string is an ordered list (normally written sequentially) of 0 or more symbols drawn from a common alphabet. For a given alphabet, different languages deem different strings permissible. In English, 26 letters are used, but also the space and some punctuation should be included for convenience, thus increasing the size of the alphabet. In computer files, the underlying base is 256 because there are 256 different states possible in each indivisible atomic unit of storage space, the byte. A byte is equivalent to 8 bits, so the 256symbol alphabet is central to real computers. For theoretical purposes however, we can dispense with the complexities of large alphabets by realizing that we can encode large alphabets into small ones; indeed, this is how a byte can be encoded as 8 bits. A bit is a symbol from a 2symbol, or binary, alphabet. In this thesis, there is not usually any need for an alphabet of more than two characters, so the notational convention is to restrict attention to the binary alphabet in the absence of countervailing remarks. Usually we encode numbers as a sequence of characters in a fixed radix format at the most basic level, and the space required to encode a number in this format can be calculated with the help of the logarithm function. The logarithm function is always used to determine a coding length for a given number to be encoded, given a probability or integer range. Similarly, it is safe for the reader to assume that all log’s are taken base 2 so that we may interpret the results in bits.
We write Σ to represent the alphabet used. We usually work with the binary alphabet, so in that case Σ = {0,1}. We write Σ^{*} to represent the space of all possible strings including the empty string. This notation may be a bit unfamiliar at first, but is very convenient and is related to the wellknown concept of regular expressions. Regular expressions are a concise way of representing formal languages as sets of strings over an alphabet. The curly braces represent a set (to be used as the alphabet in this case) and the * symbol refers to the closure of the set; By closure we mean that the symbol may be repeated 0, 1, 2, 3, 5, or any number of times. By definition, {0,1}^{*} = ∪_{n ≥ 0} {0,1}^{n}. It is important to realize that successive symbols need not be the same, but could be. Here we can see that the number of possible binary strings is infinite, yet any individual string in this class must itself be finite. For a string x, we write x to represent the length, measured in symbols, of that string.
Earlier we said that a string is a sequence of symbols from an alphabet.
It is assumed that the symbols in Σ have a natural or at least
conventional ordering. From this we may inductively create a rule that
allows us to impose an ordering on all strings that are possible in Σ^{*}
in the conventional way: use length first to bring the shorter strings
as early as possible in the ordering, and then use the leftmost different
character in any two strings to determine their relative ordering. This
is just a generalized restatement of the familiar alphabetical or
lexicographic ordering. It is included here because it allows us to
associate a positive integer ordering number with each possible string.
The empty string, є, is the first string
in this list. The next is the string 0, and the next 1. After that
comes 00, then 01, then 10, then 11, then 000, and so on
ad nauseaum. Next to each of these strings
we might list their lengths as well as their orderingnumber position in this
list as follows:
x  x  ord (x) 
є  0  1 
0  1  2 
1  1  3 
00  2  4 
01  2  5 
10  2  6 
11  2  7 
000  3  8 
001  3  9 
010  3  10 
011  3  11 
100  3  12 
... and so on forever ...
Here there are a few things to notice. First is that the second column,
the length of x written x, is related to the ord (x) by the following
relationship:
log( ord (x) ) ≤ x ≤ log( ord (x) ) + 1. (1) 
Thus we can see that the variable x can be interpreted polymorphically: as either a literal string of characters having a particular sequence and length or instead as an integer in one of two ways: either by referring to its length using the  ·  symbol, or by referring to its ordinal number using ord (x). All of the mathematical functions used in this thesis are monomorphic in their argument types: each argument can be either a number (typically an integer) or a string, but not both. Thus without too much ambiguity we will sometimes leave out the ord symbol and just write x and rely on the reader to pick out the types by their context and usage. Please notice that x can either stand for the string x or the number ord (x), but never for the length of x, which we always explicitly denote as x.
A binary string y is a proper prefix of a binary string x if we can write x=yz for z ≠ є. A set {x,y, … } ⊆ {0,1}^{*} is prefixfree if no element is a proper prefix of any other. A prefixfree set can be used to define a prefix code. Formally, a prefix code is defined by a decoding function D, which is a function from a prefix free set to some arbitrary set X. The elements of the prefix free set are called code words. The elements of X are called source words. If the inverse D^{−1} of D exists, we call it the encoding function. An example of a prefix code, that is used later, encodes a source word x=x_{1} x_{2} … x_{n} by the code word
 = 1^{n} 0 x . 
Here X = {0,1}^{*}, D^{−1}(x) = x = 1^{n} 0 x. This prefixfree code is called selfdelimiting, because there is a fixed computer program associated with this code that can determine where the code word x ends by reading it from left to right without backing up. This way a composite code message can be parsed in its constituent code words in one pass by a computer program. ^{1}
In other words, a prefix code is a code in which no codeword is a prefix of another codeword. Prefix codes are very easy to decode because codeword boundaries are directly encoded along with each datum that is encoded. To introduce these, let us consider how we may combine any two strings together in a way that they could be later separated without recourse to guessing. In the case of arbitrary binary strings x,y, we cannot be assured of this prefix condition: x might be 0 while y was 00 and then there would be no way to tell the original contents of x or y given, say, just xy. Therefore let us concentrate on just the x alone and think about how we might augment the natural literal encoding to allow for prefix disambiguation. In real languages on computers, we are blessed with whitespace and commas, both of which are used liberally for the purpose of separating one number from the next in normal output formats. In a binary alphabet our options are somewhat more limited but still not too bad. The simplest solution would be to add in commas and spaces to the alphabet, thus increasing the alphabet size to 4 and the coding size to 2 bits, doubling the length of all encoded strings. This is a needlessly heavy price to pay for the privilege of prefix encoding, as we will soon see. But first let us reconsider another way to do it in a bit more than double space: suppose we preface x with a sequence of x 0’s, followed by a 1, followed by the literal string x. This then takes one bit more than twice the space for x and is even worse than the original scheme with commas and spaces added to the alphabet. This is just the scheme discussed in the beginning of the section. But this scheme has ample room for improvement: suppose now we adjust it so that instead of outputting all those 0’s at first in unary, we instead just output a number of zeros equal to
⌈ log(x) ⌉, 
then a 1, then the binary number x (which satisfies x ≤ ⌈ logx ⌉ + 1, see Eq. (??)), then x literally. Here, ⌈ · ⌉ indicates the ceiling operation that returns the smallest integer not less than its argument. This, then, would take a number of bits about
2 ⌈ loglogx ⌉ + ⌈ logx ⌉ + 1, 
which exceeds ⌈ logx ⌉, the number of bits needed to encode x literally, only by a logarithmic amount. If this is still too many bits then the pattern can be repeated, encoding the first set of 0’s one level higher using the system to get
2 ⌈ logloglogx ⌉ + ⌈ loglogx ⌉ + ⌈ logx ⌉ + 1 . 
Indeed, we can “dial up” as many logarithms as are necessary to create a suitably slowlygrowing composition of however many log’s are deemed appropriate. This is sufficiently efficient for all purposes in this thesis and provides a general framework for converting arbitrary data into prefixfree data. It further allows us to compose any number of strings or numbers for any purpose without restraint, and allows us to make precise the difficult concept of K(x,y), as we shall see in Section ??.
Let X be the set of natural numbers and consider the straightforward nonprefix binary representation with the ith binary string in the lengthincreasing lexicographical order corresponding to the number i. There are two elements of X with a description of length 1, four with a description of length 2 and so on. However, there are less binary prefix code words of each length: if x is a prefix code word then no y = xz with z ≠ є is a prefix code word. Asymptotically there are less prefix code words of length n than the 2^{n} source words of length n. Clearly this observation holds for arbitrary prefix codes. Quantification of this intuition for countable X and arbitrary prefixcodes leads to a precise constraint on the number of codewords of given lengths. This important relation is known as the Kraft Inequality and is due to L.G. Kraft []. Let l_{1} , l_{2} , … be a finite or infinite sequence of natural numbers. There is a prefix code with this sequence as lengths of its binary code words iff
 2^{− ln } ≤ 1. (1) 
We want to code elements of some set X in a way that they can be uniquely reconstructed from the encoding. Such codes are called uniquely decodable. Every prefixcode is a uniquely decodable code. On the other hand, not every uniquely decodable code satisfies the prefix condition. Prefixcodes are distinguished from other uniquely decodable codes by the property that the end of a code word is always recognizable as such. This means that decoding can be accomplished without the delay of observing subsequent code words, which is why prefixcodes are also called instantaneous codes. There is good reason for our emphasis on prefixcodes. Namely, it turns out that Lemma ?? stays valid if we replace “prefixcode” by “uniquely decodable code.” This important fact means that every uniquely decodable code can be replaced by a prefixcode without changing the set of codeword lengths. In this thesis, the only aspect of actual encodings that interests us is their length, because this reflects the underlying probabilities in an associated model. There is no loss of generality in restricting further discussion to prefix codes because of this property.
A uniquely decodable code is complete if the addition of any new code word to its code word set results in a nonuniquely decodable code. It is easy to see that a code is complete iff equality holds in the associated Kraft Inequality. Let l_{1}, l_{2}, … be the code words of some complete uniquely decodable code. Let us define q_{x} = 2^{− lx}. By definition of completeness, we have ∑_{x} q_{x} = 1. Thus, the q_{x} can be thought of as probability mass functions corresponding to some probability distribution Q for a random variable X. We say Q is the distribution corresponding to l_{1},l_{2},…. In this way, each complete uniquely decodable code is mapped to a unique probability distribution. Of course, this is nothing more than a formal correspondence: we may choose to encode outcomes of X using a code corresponding to a distribution q, whereas the outcomes are actually distributed according to some p ≠ q. But, as we argue below, if X is distributed according to p, then the code to which p corresponds is, in an average sense, the code that achieves optimal compression of X. In particular, every probability mass function p is related to a prefix code, the ShannonFano code, such that the expected number of bits per transmitted code word is as low as is possible for any prefix code, assuming that a random source X generates the source words x according to P(X=x)=p(x). The ShannonFano prefix code encodes a source word x by a code word of length l_{x} = ⌈ log1/ p(x) ⌉, so that the expected transmitted code word length equals ∑_{x} p(x) log1/p(x)= H(X), the entropy of the source X, up to one bit. This is optimal by Shannon’s “noiseless coding” theorem []. This is further explained in Section ??.
This section mainly serves as a preparation for the next section, in which we introduce the fundamental concept of Kolmogorov complexity. Roughly speaking, the Kolmogorov complexity of a string is the shortest computer program that computes the string, i.e. that prints it, and then halts. The definition depends on the specific computer programming language that is used. To make the definition more precise, we should base it on programs written for universal Turing machines, which are an abstract mathematical representation of a generalpurpose computer equipped with a generalpurpose or universal computer programming language.
Most popular computer programming languages such as C, Lisp, Java and Ruby, are universal. Roughly speaking, this means that they must be powerful enough to emulate any other computer programming language: every universal computer programming language can be used to write a compiler for any other programming language, including any other universal programming language. Indeed, this has been done already a thousand times over with the GNU (Gnu’s Not Unix) C compiler, perhaps the most successful opensource computer program in the world. In this case, although there are many different assembly languages in use on different CPU architectures, all of them are able to run C programs. So we can always package any C program along with the GNU C compiler which itself is not more than 100 megabytes in order to run a C program anywhere.
The Turing machine is an abstract mathematical representation of the idea of a computer. It generalizes and simplifies all the many specific types of deterministic computing machines into one regularized form. A Turing machine is defined by a set of rules which describe its behavior. It receives as its input a string of symbols, wich may be thought OF as a “program”, and it outputs the result of running that program, which amounts to transforming the input using the given set of rules. Just as there are universal computer languages, there are also universal Turing machines. We say a Turing Machine is universal if it can simulate any other Turing Machine. When such a universal Turing machine receives as input a pair ⟨ x,y ⟩, where x is a formal specification of another Turing machine T_{x}, it outputs the same result as one would get if one would input the string y to the Turing machine T_{x}. Just as any universal programming language can be used to emulate any other one, any universal Turing machine can be used to emulate any other one. It may help intuition to imagine any familiar universal computer programming language as a definition of a universal Turing machine, and the runtime and hardware needed to execute it as a sort of realworld Turing machine itself. It is necessary to remove resource constraints (on memory size and input/output interface, for example) in order for these concepts to be thoroughly equivalent theoretically.
A Turing machine consists of two parts: a finite control and a tape. The finite control is the memory (or current state) of the machine. It always contains a single symbol from a finite set Q of possible states. The tape initially contains the program which the Turing machine must execute. The tape contains symbols from the trinary alphabet A = {0,1,B}. Initially, the entire tape contains the B (blank) symbol except for the place where the program is stored. The program is a finite sequence of bits. The finite control also is always positioned above a particular symbol on the tape and may move left or right one step. At first, the tape head is positioned at the first nonblank symbol on the tape. As part of the formal definition of a Turing machine, we must indicate which symbol from Q is to be the starting state of the machine. At every time step the Turing machine does a simple sort of calculation by consulting a list of rules that define its behavior. The rules may be understood to be a function taking two arguments (the current state and the symbol under the reading head) and returning a Cartesian pair: the action to execute this timestep and the next state to enter. This is to say that the two input arguments are a current state (symbol from Q) of the finite control and a letter from the alphabet A. The two outputs are a new state (also taken from Q) and an action symbol taken from S. The set of possible actions is S = {0,1,B,L,R}. The 0, 1, and B symbols refer to writing that value below the tape head. The L and R symbols refer to moving left or right, respectively. This function defines the behavior of the Turing machine at each step, allowing it to perform simple actions and run a program on a tape just like a real computer but in a very mathematically simple way. It turns out that we can choose a particular set of statetransition rules such that the Turing machine becomes universal in the sense described above. This simulation is plausible given a moment of reflection on how a Turing Machine is mechanically defined as a sequence of rules governing state transitions etc. The endpoint in this line of reasoning is that a universal Turing Machine can run a sort of Turing Machine simulation system and thereby compute identical results as any other Turing Machine.
We typically use the Greek letter Φ to represent a Turing machine T as a partially defined function. When the Turing machine T is not clear from the context, we write Φ_{T}. The function is supposed to take as input a program encoded as a finite binary string and outputs the results of running that program. Sometimes it is convenient to define the function as taking integers instead of strings; this is easy enough to do when we remember that each integer is identified with a given finite binary string given the natural lexicographic ordering of finite strings, as in Section ?? The function Φ need only be partially defined; for some input strings it is not defined because some programs do not produce a finite string as output, such as infinite looping programs. We say that Φ is defined only for those programs that halt and therefore produce a definite output. We introduce a special symbol ∞ that represents an abstract object outside the space of finite binary strings and unequal to any of them. For those programs that do not halt we say Φ(x) = ∞ as a shorthand way of indicating this infinite loop: x is thus a nonhalting program like the following:
x = while true ; do ; done
Here we can look a little deeper into the x program above and see that although its runtime is infinite, its definition is quite finite; it is less than 30 characters. Since this program is written in the ASCII codespace, we can multiply this figure by 8 to reach a size of 240 bits.
In this thesis we look at Turing Machines whose set of halting programs is prefix free: that is to say that the set of such programs form a prefix code (Section ??), because no halting program is a prefix of another halting program. We can realize this by slightly changing the definition of a Turing machine, equipping it with a oneway input or ‘data’ tape, a separate working tape, and a oneway output tape. Such a Turing Machine is called a prefix machine. Just as there are universal “ordinary” Turing Machines, there are also universal prefix machines that have identical computational power.
Now is where things begin to become tricky. There is a very special function K called Kolmogorov Complexity. Intuitively, the Kolmogorov complexity of a finite string x is the shortest computer program that prints x and then halts. More precisely, K is usually defined as a unary function that maps strings to integers and is implicitly based (or conditioned) on a concrete reference Turing machine represented by function Φ. The complete way of writing it is K_{Φ}(x). In practice, we want to use a Turing Machine that is as general as possible. It is convenient to require the prefix property. Therefore we take Φ to be a universal prefix Turing Machine.^{2} Because all universal Turing Machines can emulate one another reasonably efficiently, it does not matter much which one we take. We will say more about this later. For our purposes, we can suppose a universal prefix Turing machine is equivalent to any formal (implemented, real) computer programming language extended with a potentially unlimited memory. Recall that Φ represents a particular Turing machine with particular rules, and remember Φ is a partial function that is defined for all programs that terminate. If Φ is the transformation that maps a program x to its output o, then K_{Φ}(z) represents the length of the minimum program size (in bits) x over all valid programs x such that Φ(x) = z .
We can think of K as representing the smallest quantity of information required to recreate an object by any reliable procedure. For example, let x be the first 1000000 digits of π. Then K(x) is small, because there is a short program generating x, as explained further below. On the other hand, for a random sequence of digits, K(x) will usually be large because the program will probably have to hardcode a long list of abitrary values.
There is another form of K which is a bit harder to understand but still important to our discussions called conditional Kolmogorov Complexity and written
K(z  y). 
The notation is confusing to some because the function takes two arguments. Its definition requires a slight enhancement of the earlier model of a Turing machine. While a Turing machine has a single infinite tape, Kolmogorov complexity is defined with respect to prefix Turing machines, which have an infinite working tape, an output tape and a restricted input tape that supports only one operation called “read next symbol”. This input tape is often referred to as a data tape and is very similar to an input data file or stream read from standard input in Unix. Thus instead of imagining a program as a single string we must imagine a total runtime environment consisting of two parts: an input program tape with read/write memory, and a data tape of extremely limited functionality, unable to seek backward with the same limitations as POSIX standard input: there is getchar but no fseek. In the context of this slightly more complicated machine, we can define K(z  y) as the size, in bits, of the smallest program that outputs z given a prefixfree encoding of y, say ȳ, as an initial input on the data tape. The idea is that if y gives a lot of information about z then K(zy) << K(z), but if z and y are completely unrelated, then K(z ∣ y) ≈ K(z). For example, if z = y, then y provides a maximal amount of information about z. If we know that z = y then a suitable program might be the following:
while true ; do c = getchar() if (c == EOF) ; then halt else putchar(c) done
Here, already, we can see that K(x  x) < 1000 given the program above and a suitable universal prefix Turing machine. Note that the number of bits used to encode the whole thing is less than 1000. The more interesting case is when the two arguments are not equal, but related. Then the program must provide the missing information through morecomplicated translation, preprogrammed results, or some other device.
As it turns out, K provides a convenient means for characterizing random sequences. Contrary to popular belief, random sequences are not simply sequences with no discernible patterns. Rather, there are a great many statistical regularities that can be proven and observed, but the difficulty lies in simply expressing them. As mentioned earlier, we can very easily express the idea of randomness by first defining different degrees of randomness as follows: a string x is k−random if and only if K(x) > x − k. This simple formula expresses the idea that random strings are incompressible. The vast majority of strings are 1random in this sense. This definition improves greatly on earlier definitions of randomness because it provides a concrete way to show a given, particular string is nonrandom by means of a simple computer program.
At this point, an example is appropriate. Imagine the following sequence of digits:
1, 4, 1, 5, 9, 2, 6, 5, 3, ...
and so on. Some readers may recognize the aforementioned sequence as the first digits of the Greek letter π with the first digit (3) omitted. If we extend these digits forward to a million places and continue to follow the precise decimal approximation of π, we would have a sequence that might appear random to most people. But it would be a matter of some confusing debate to try to settle a bet upon whether or not the sequence were truly random, even with all million of the digits written out in several pages. However, a clever observer, having noticed the digits corresponded to π, could simply write a short computer program (perhaps gotten off the internet) of no more than 10 kilobytes that could calculate the digits and print them out. What a surprise it would be then, to see such a short program reproduce such a long and seemingly meaningless sequence perfectly. This reproduction using a much shorter (less than one percent of the literal size) program is itself direct evidence that the sequence is nonrandom and in fact implies a certain regularity to the data with a high degree of likelihood. Simple counting arguments show that there can be no more than a vanishingly small number of highly compressible programs; in particular, the proportion of programs that are compressible by even k bits is no more than 2^{−k}. This can be understood by remembering that there are just two 1bit strings (0 and 1), four 2bit strings, and 2^{m} mbit strings. So if we consider encodings of length m for source strings of length n with n>m, then at most 2^{m} different strings out of the total of 2^{n} source strings can be encoded in m bits. Thus, the ratio of strings compressible by n−m bits is at most a 2^{m−n} proportion of all strings.
We have remarked earlier how universal Turing machines may emulate one another using finite simulation programs. In talking about the asymptotic behavior of K, these finite simulation programs do not matter more than an additive constant. For example, if we take x^{n} to mean the first n digits of π, then K(x^{n}) = O(logn) no matter which universal Turing machine is in use. This is because it will be possible to calculate any number of digits of π using a fixedsize program that reads as input the number of digits to output. The length of this input cannot be encoded in shorter than logn bits by a counting argument as in the previous section.
This implies that all variations of K are in some sense equivalent, because any two different variants of K given two different reference universal Turing machines will never differ by more than a fixedsize constant that depends only on the particular Turing machines chosen and not on the sequence. It is this universal character that winds up lending credence to the idea that K can be used as an absolute measure of the information contained in a given object. This is quite different from standard Shannon Information Theory based on the idea of average information required to communicate an object over a large number of trials and given some sort of generating source []. The great benefit of the Kolmogorov Complexity approach is that we need not explicitly define the generating source nor run the many trials to see desired results; just one look at the object is enough. Section ?? provides an example that will serve to illustrate the point.
There is now one more form of the K function that should be addressed, though it is perhaps the most complicated of all. It is written as follows:
K(x,y). 
This represents the size in bits of the minimum program that outputs x followed by y, provided the output is given by first outputting x in a selfdelimitting way (as explained earlier) and then outputting y. Formally, we define K(x,y) as K(⟨ x,y ⟩), where ⟨ ·,· ⟩ is defined as the pairing operation that takes two numbers and returns a pair: x y.
Suppose we flip a fair coin. The type of sequence generated by the series of N flips of a fair coin is unpredictable in nature by assumption in normal probability theory. To define precisely what this means presents a bewildering array of possibilities. In the simplest, we might say the sequence is generated by a Bernoulli process where X takes on value 0 or 1 with probability
P(X = 0)_{fair} = 
 = P(X = 1)_{fair}. 
The notation P(·) represents the chance that the event inside occurs. It is expressed as a ratio between 0 and 1 with 0 meaning never, 1 meaning always, and every number inbetween representing the proportion of times the event will be true given a large enough number of independent trials. In such a setting, we may use a single bit to represent either possibility efficiently, and can always store N coin flips in just N bits regardless of the outcomes.
What if, instead of a fair coin, we use a biased one? For instance, if
P(X = 0)_{biased} = 
 , 
and therefore since our simplified coins always turn up 0 or 1,
P(X = 1)_{biased} = 
 . 
Then we may use the scheme above to reliably transmit N flips in N bits. Alternatively, we may decide to encode the 1’s more efficiently by using the following simple rule. Assume that N is even. Divide the N flips into pairs, and encode the pairs so that a pair of 1’s takes just a single 1 bit to encode. If both are not 1, then instead output a 0 and then two more bits to represent the actual outcomes in order. Then continue with the next pair of two. One can quickly calculate that “49/64 of the time” the efficient 1bit codeword will be output in this scheme which will save a great deal of space. Some of this savings will be lost in the cases where the 3bit codeword is emitted, 15/64 of the time. The average number of bits needed per outcome transmitted is then the codelength c:
c = 
 + 
 = 
 . 
This can also be improved somewhat down to the Shannon entropy H(X) [] of the source X with longer blocks or smarter encoding such as arithmetic codes [] over an alphabet Σ:
H(X) = 
 − P(X = i) logP(X = i), 
c = − 
 · log( 
 ) − 
 · log( 
 ). 
By Shannon’s famous coding theorem, this is essentially the smallest average code length that can be obtained under the assumption that the coin is independently tossed according to P_{biased}. Here though, there is already a problem, as we now cannot say, unconditionally, at least, that this many bits will be needed for any actual sequence of bits; luck introduces some variation in the actual space needed, though it is usually near the average. We know that such a coin is highly unlikely to repeatedly emit 0’s, yet we cannot actually rule out this possibility. More to the point, in abstract terms the probability, while exponentially decaying with the greatest haste, still never quite reaches zero. It is useful to think carefully about this problem. All the laws of classical probability theory cannot make claims about a particular sequence but instead only about ensembles of experiments and expected proportions. Trying to pin down uncertainty in this crude way often serves only to make it appear elsewhere instead. In the Kolmogorov Complexity approach, we turn things upsidedown: we say that a string is random if it is uncompressible. A string is c−random if K(x) > x − c. This then directly addresses the question of how random a given string is by introducing different grades of randomness and invoking the universal function K to automatically rule out the possibility of any short programs predicting a random string defined in this way. Returning to the fair coin example, the entropy is 1 bit per outcome. But we cannot say with certainty that a sequence coming from such a coin cannot be substantially compressed. This is only true with high probability.
Some have made the claim that Kolmogorov Complexity is objective. In theory, it is. But in practice it is difficult to say; one major drawback of K is that it is uncomputable. Trying to compute it leads one to try immediately the shortest programs first, and as shown above it does not take many characters in a reasonable language to produce an infinite loop. This problem is impossible to protect against in general, and any multithreaded approach is doomed to failure for this reason as it bumps up against the Halting Problem. []
A more fruitful approach has been to apply Kolmogorov Complexity by approximating it with data compressors. We may consider the problem of efficiently encoding a known biased random source into a minimum number of bits in such a way that the original sequence, no matter what it was, can once again be reconstructed, but so that also for certain sequences a shorter code is output. This is the basic idea of a data compression program. The most commonly used data compression programs of the last decade include gzip, bzip2, and PPM.
gzip is an old and reliable LempelZiv type compressor with a 32kilobyte window []. It is the simplest and fastest of the three compressors.
bzip2 is a wonderful new compressor using the blocksort algorithm []. It provides good compression and an expanded window of 900 kilobytes allowing for longerrange patterns to be detected. It is also reasonably fast.
PPM stands for Prediction by Partial Matching []. It is part of a new generation of powerful compressors using a pleasing mix of statistical models arranged by trees, suffix trees or suffix arrays. It usually achieves the best performance of any real compressor yet is also usually the slowest and most memory intensive.
Although restricted to the research community, a new challenger to PPM has arisen called context mixing compression. It is often the best compression scheme for a variety of file types but is very slow; further, it currently uses a neural network to do the mixing of contexts. See the paq series of compressors on the internet for more information on this exciting development in compression technology.
We use these data compressors to approximate from above the Kolmogorov Complexity function K. It is worth mentioning that all of the real compressors listed above operate on a bytewide basis, and thus all will return a multiple of 8 bits in their results. This is unfortunate for analyzing small strings, because the granularity is too coarse to allow for fine resolution of subtle details. To overcome this problem, the CompLearn system – the piece of software using which almost all experiments in later chapters have been carried out – supports the idea of a virtual compressor (originally suggested by Steven de Rooij): a virtual compressor is one that does not actually output an encoded compressed form, but instead simply accumulates the number of bits necessary to encode the results using a hypothetical arithmetic (or entropy) encoder. This frees us from the bytewide restriction and indeed eliminates the need for rounding to a whole number of bits. Instead we may just return a real or floatingpoint value. This becomes quite useful when analyzing very similar strings of less than 100 bytes.
We have introduced the notion of universal computation and the K function indicating Kolmogorov Complexity. We have introduced Turing Machines and prefix codes as well as prefix machines. We have discussed a definition of a random string using K. We use these concepts in the next few chapters to explain in great detail our theory and experimental results.
You may very appropriately want to ask me how we are going to resolve the ever acceleratingly dangerous impasse of worldopposed politicians and ideological dogmas. I answer, it will be resolved by the computer. Man has everincreasing confidence in the computer; witness his unconcerned landings as airtransport passengers coming in for a landing in the combined invisibility of fog and night. While no politician or political system can ever afford to yield understandably and enthusiastically to their adversaries and opposers, all politicians can and will yield enthusiastically to the computers safe flightcontrolling capabilities in bringing all of humanity in for a happy landing. –Buckminster Fuller in Operating Manual for Spaceship Earth
In this chapter the Normalized Compression Distance (NCD) and the related Normalized Information Distance (NID) are presented and investigated. NCD is a similarity measure based on a data compressor. NID is simply the instantiation of NCD using the theoretical (and uncomputable) Kolmogorov compressor. Below we first review the definition of a metric. In Section ??, we explain precisely what is meant by universality in the case of NID. We discuss compressor axioms in Section ??, and properties of NCD in Section 3.4. At the end of the chapter, we connect NCD with a classical statistical quantity called KullbackLeibler divergence. In Section ?? we connect arithmetic compressors to entropy, and in Section ?? we relate them to KLdivergence.
In mathematics, different distances arise in all sorts of contexts, and one usually requires these to be a “metric”. We give a precise formal meaning to the loose distance notion of “degree of similarity” used in the pattern recognition literature.
Metric: Let Ω be a nonempty set and R^{+} be the set of nonnegative real numbers. A metric on Ω is a function D: Ω × Ω → R^{+} satisfying the metric (in)equalities:
The value D(x,y) is called the distance between x,y ∈ Ω. A familiar example of a metric is the Euclidean metric, the everyday distance e(a,b) between two objects a,b expressed in, say, meters. Clearly, this distance satisfies the properties e(a,a)=0, e(a,b)=e(b,a), and e(a,b) ≤ e(a,c) + e(c,b) (for instance, a= Amsterdam, b= Brussels, and c= Chicago.) We are interested in “similarity metrics”. For example, if the objects are classical music pieces then the function D(a,b)= 0 if a and b are by the same composer and D(a,b) = 1 otherwise, is a similarity metric. This metric captures only one similarity aspect (feature) of music pieces, presumably an important one because it subsumes a conglomerate of more elementary features.
Density: In defining a class of admissible distances (not necessarily metric distances) we want to exclude unrealistic ones like f(x,y) = 1/2 for every pair x ≠ y. We do this by restricting the number of objects within a given distance of an object. As in [] we do this by only considering effective distances, as follows.
Let Ω = Σ^{*}, with Σ a finite nonempty alphabet and Σ^{*} the set of finite strings over that alphabet. Since every finite alphabet can be recoded in binary, we choose Σ = {0,1}. In particular, “files” in computer memory are finite binary strings. A function D: Ω × Ω → R^{+} is an admissible distance if for every pair of objects x,y ∈ Ω the distance D(x,y) satisfies the density condition (a version of the Kraft Inequality (??)):
 2^{−D(x,y)} ≤ 1, (1) 
is computable, and is symmetric, D(x,y)=D(y,x).
If D is an admissible distance, then for every x the set {D(x,y): y ∈ {0,1}^{*}} is the length set of a prefix code, since it satisfies (??), the Kraft inequality. Conversely, if a distance is the length set of a prefix code, then it satisfies (??), see [].
In representing the Hamming distance d between two strings of equal length n differing in positions i_{1}, … , i_{d}, we can use a simple prefixfree encoding of (n,d,i_{1}, … , i_{d}) in 2 logn + 4 loglogn +2 + d logn bits. We encode n and d prefixfree in logn+2 loglogn +1 bits each, see e.g. [], and then the literal indexes of the actual flippedbit positions. Adding an O(1)bit program to interpret these data, with the strings concerned being x and y, we have defined H_{n}(x,y)=2 logn + 4 loglogn + d logn+O(1) as the length of a prefix code word (prefix program) to compute x from y and vice versa. Then, by the Kraft inequality (Chapter 2, Section ??), ∑_{y} 2^{−Hn(x,y)} ≤ 1. It is easy to verify that H_{n} is a metric in the sense that it satisfies the metric (in)equalities up to O(logn) additive precision.
Normalization: Large objects (in the sense of long strings) that differ by a tiny part are intuitively closer than tiny objects that differ by the same amount. For example, two whole mitochondrial genomes of 18,000 bases that differ by 9,000 are very different, while two whole nuclear genomes of 3 × 10^{9} bases that differ by only 9,000 bases are very similar. Thus, absolute difference between two objects does not govern similarity, but relative difference seems to. A compressor is a lossless encoder mapping Ω into {0,1}^{*} such that the resulting code is a prefix code. “Lossless” means that there is a decompressor that reconstructs the source message from the code message. For convenience of notation we identify “compressor” with a “code word length function” C: Ω → N, where N is the set of nonnegative integers. That is, the compressed version of a file x has length C(x). We only consider compressors such that C(x) ≤ x+O(logx). (The additive logarithmic term is due to our requirement that the compressed file be a prefix code word.) We fix a compressor C, and call the fixed compressor the reference compressor. Let D be an admissible distance. Then we may make the definition D^{+}(x)=max{ D(x,z): C(z) ≤ C(x)}, and D^{+}(x,y) is D^{+}(x,y) = max{ D^{+}(x),D^{+}(y) }. Note that since D(x,y)=D(y,x), also D^{+}(x,y)=D^{+}(y,x).
Let D be an admissible distance. The normalized admissible distance, also called a similarity distance, d(x,y), based on D relative to a reference compressor C, is defined by
d(x,y) = 
 . 
It follows from the definitions that a normalized admissible distance is a function d: Ω × Ω → [0,1] that is symmetric: d(x,y)=d(y,x).
For every x ∈ Ω, and constant e ∈ [0,1], a normalized admissible distance satisfies the density constraint
 {y: d(x,y) ≤ e, C(y) ≤ C(x) }  < 2^{e D+(x)+1} . (2) 
Proof. Assume to the contrary that d does not satisfy (??). Then, there is an e ∈ [0,1] and an x ∈ Ω, such that (??) is false. We first note that, since D(x,y) is an admissible distance that satisfies (??), d(x,y) satisfies a “normalized” version of the Kraft inequality:
 2^{− d(x,y) D+(x)} ≤ 
 2^{− d(x,y) D+(x,y)} ≤ 1 . (3) 
Starting from (??) we obtain the required contradiction:

If d(x,y) is the normalized version of an admissible distance D(x,y) then (??) is equivalent to (??). We call a normalized distance a “similarity” distance, because it gives a relative similarity according to the distance (with distance 0 when objects are maximally similar and distance 1 when they are maximally dissimilar) and, conversely, for every welldefined computable notion of similarity we can express it as a metric distance according to our definition. In the literature a distance that expresses lack of similarity (like ours) is often called a “dissimilarity” distance or a “disparity” distance.
As far as the authors know, the idea of normalized metric is, surprisingly, not wellstudied. An exception is [], which investigates normalized metrics to account for relative distances rather than absolute ones, and it does so for much the same reasons as in the present work. An example there is the normalized Euclidean metric x−y/(x+y), where x,y ∈ R^{n} ( R denotes the real numbers) and  ·  is the Euclidean metric—the L_{2} norm. Another example is a normalized symmetricsetdifference metric. But these normalized metrics are not necessarily effective in that the distance between two objects gives the length of an effective description to go from either object to the other one. Our definition of normalized admissible distance is more direct than in [], and the density constraints (??) and (??) follow from the definition. In [] we put a stricter density condition in the definition of “admissible” normalized distance, which is, however, harder to satisfy and maybe too strict to be realistic. The purpose of this stricter density condition was to obtain a stronger “universality” property than the present Theorem ??, namely one with α = 1 and є = O(1/max{C(x),C(y)}). Nonetheless, both definitions coincide if we set the length of the compressed version C(x) of x to the ultimate compressed length K(x), the Kolmogorov complexity of x.
To obtain a normalized version of the Hamming distance of Example ??, we define h_{n}(x,y) = H_{n}(x,y) / H_{n}^{+}(x,y). We can set H_{n}^{+}(x,y) = H_{n}^{+}(x) = (n+2) ⌈ logn ⌉ + 4 ⌈ loglogn ⌉ +O(1) since every contemplated compressor C will satisfy C(x)=C(x), where x is x with all bits flipped (so H_{n}^{+}(x,y) ≥ H_{n}^{+}(z, z) for either z=x or z=y). By (??), for every x, the number of y with C(y) ≤ C(x) in the Hamming ball h_{n}(x,y) ≤ e is less than 2^{e Hn+ (x)+1}. This upper bound is an obvious overestimate for e ≥ 1/ logn. For lower values of e, the upper bound is correct by the observation that the number of y’s equals ∑_{i=0}^{en} n en ≤ 2^{n H(e)}, where H(e) = e loge + (1−e) log(1−e), Shannon’s entropy function. Then, eH_{n}^{+} (x) > en logn > en H(e) since e logn > H(e).
We give axioms determining a large family of compressors that both include most (if not all) realworld compressors and ensure the desired properties of the NCD to be defined later.
A compressor C is normal if it satisfies, up to an additive O(logn) term, with n the maximal binary length of an element of Ω involved in the (in)equality concerned, the following:
Idempotency: A reasonable compressor will see exact repetitions and obey idempotency up to the required precision. It will also compress the empty string to the empty string.
Monotonicity: A real compressor must have the monotonicity property, at least up to the required precision. The property is evident for streambased compressors, and only slightly less evident for blockcoding compressors.
Symmetry: Streambased compressors of the LempelZiv family, like gzip and pkzip, and the predictive PPM family, like PPMZ, are possibly not precisely symmetric. This is related to the streambased property: the initial file x may have regularities to which the compressor adapts; after crossing the border to y it must unlearn those regularities and adapt to the ones of y. This process may cause some imprecision in symmetry that vanishes asymptotically with the length of x,y. A compressor must be poor indeed (and will certainly not be used to any extent) if it doesn’t satisfy symmetry up to the required precision. Apart from streambased, the other major family of compressors is blockcoding based, like bzip2. They essentially analyze the full input block by considering all rotations in obtaining the compressed version. It is to a great extent symmetrical, and real experiments show no departure from symmetry.
Distributivity: The distributivity property is not immediately intuitive. In Kolmogorov complexity theory the stronger distributivity property
C(xyz)+C(z) ≤ C(xz)+C(yz) (1) 
holds (with K=C). However, to prove the desired properties of NCD below, only the weaker distributivity property
C(xy)+C(z) ≤ C(xz)+C(yz) (2) 
above is required, also for the boundary case were C=K. In practice, realworld compressors appear to satisfy this weaker distributivity property up to the required precision.
Define
C(yx)=C(xy)−C(x). (3) 
This number C(yx) of bits of information in y, relative to x, can be viewed as the excess number of bits in the compressed version of xy compared to the compressed version of x, and is called the amount of conditional compressed information. In the definition of compressor the decompression algorithm is not included (unlike the case of Kolmorogov complexity, where the decompressing algorithm is given by definition), but it is easy to construct one: Given the compressed version of x in C(x) bits, we can run the compressor on all candidate strings z—for example, in lengthincreasing lexicographical order, until we find the compressed string z_{0}=x. Since this string decompresses to x we have found x=z_{0}. Given the compressed version of xy in C(xy) bits, we repeat this process using strings xz until we find the string xz_{1} of which the compressed version equals the compressed version of xy. Since the former compressed version decompresses to xy, we have found y=z_{1}. By the unique decompression property we find that C(yx) is the extra number of bits we require to describe y apart from describing x. It is intuitively acceptable that the conditional compressed information C(xy) satisfies the triangle inequality
C(xy) ≤ C(xz)+C(zy). (4) 
Both (??) and (??) imply (??).
Proof. ((??) implies (??):) By monotonicity.
((??) implies (??):) Rewrite the terms in (??) according to (??), cancel C(y) in the left and righthand sides, use symmetry, and rearrange.
A normal compressor satisfies additionally subadditivity: C(xy) ≤ C(x)+C(y).
Proof. Consider the special case of distributivity with z the empty word so that xz=x, yz=y, and C(z)=0.
Subadditivity: The subadditivity property is clearly also required for every viable compressor, since a compressor may use information acquired from x to compress y. Minor imprecision may arise from the unlearning effect of crossing the border between x and y, mentioned in relation to symmetry, but again this must vanish asymptotically with increasing length of x,y.
Technically, the Kolmogorov complexity of x given y is the length of the shortest binary program, for the reference universal prefix Turing machine, that on input y outputs x; it is denoted as K(xy). For precise definitions, theory and applications, see []. The Kolmogorov complexity of x is the length of the shortest binary program with no input that outputs x; it is denoted as K(x)=K(xλ) where λ denotes the empty input. Essentially, the Kolmogorov complexity of a file is the length of the ultimate compressed version of the file. In [] the information distance E(x,y) was introduced, defined as the length of the shortest binary program for the reference universal prefix Turing machine that, with input x computes y, and with input y computes x. It was shown there that, up to an additive logarithmic term, E(x,y) = max{K(xy),K(yx)}. It was shown also that E(x,y) is a metric, up to negligible violations of the metric inequalties. Moreover, it is universal in the sense that for every admissible distance D(x,y) as in Definition ??, E(x,y) ≤ D(x,y) up to an additive constant depending on D but not on x and y. In [], the normalized version of E(x,y), called the normalized information distance, is defined as
NID (x,y) = 
 . (1) 
It too is a metric, and it is universal in the sense that this single metric minorizes up to an negligible additive error term all normalized admissible distances in the class considered in []. Thus, if two files (of whatever type) are similar (that is, close) according to the particular feature described by a particular normalized admissible distance (not necessarily metric), then they are also similar (that is, close) in the sense of the normalized information metric. This justifies calling the latter the similarity metric. We stress once more that different pairs of objects may have different dominating features. Yet every such dominant similarity is detected by the NID . However, this metric is based on the notion of Kolmogorov complexity. Unfortunately, the Kolmogorov complexity is noncomputable in the Turing sense. Approximation of the denominator of (??) by a given compressor C is straightforward: it is max{C(x),C(y)}. The numerator is more tricky. It can be rewritten as
max{ K(x,y)−K(x), K(x,y)−K(y) }, (2) 
within logarithmic additive precision, by the additive property of Kolmogorov complexity []. The term K(x,y) represents the length of the shortest program for the pair (x,y). In compression practice it is easier to deal with the concatenation xy or yx. Again, within logarithmic precision K(x,y)=K(xy)=K(yx). Following a suggestion by Steven de Rooij, one can approximate (??) best by min{C(xy),C(yx)} − min{C(x),C(y)}. Here, and in the later experiments using the CompLearn Toolkit, we simply use C(xy) rather than min{C(xy),C(yx)}. This is justified by the observation that blockcoding based compressors are symmetric almost by definition, and experiments with various streambased compressors (gzip, PPMZ) show only small deviations from symmetry.
The result of approximating the NID using a real compressor C is called the normalized compression distance (NCD ), formally defined in (??). The theory as developed for the Kolmogorovcomplexity based NID in [], may not hold for the (possibly poorly) approximating NCD . It is nonetheless the case that experiments show that the NCD apparently has (some) properties that make the NID so appealing. To fill this gap between theory and practice, we develop the theory of NCD from first principles, based on the axiomatics of Section ??. We show that the NCD is a quasiuniversal similarity metric relative to a normal reference compressor C. The theory developed in [] is the boundary case C=K, where the “quasiuniversality” below has become full “universality”.
We define a compression distance based on a normal compressor and show it is an admissible distance. In applying the approach, we have to make do with an approximation based on a far less powerful realworld reference compressor C. A compressor C approximates the information distance E(x,y), based on Kolmogorov complexity, by the compression distance E_{C}(x,y) defined as
E_{C}(x,y) = C(xy)− min{C(x),C(y)}. (1) 
Here, C(xy) denotes the compressed size of the concatenation of x and y, C(x) denotes the compressed size of x, and C(y) denotes the compressed size of y.
If C is a normal compressor, then E_{C} (x,y)+O(1) is an admissible distance.
Proof. Case 1: Assume C(x) ≤ C(y). Then E_{C} (x,y) = C(xy)− C(x). Then, given x and a prefixprogram of length E_{C}(x,y) consisting of the suffix of the Ccompressed version of xy, and the compressor C in O(1) bits, we can run the compressor C on all xz’s, the candidate strings z in lengthincreasing lexicographical order. When we find a z so that the suffix of the compressed version of xz matches the given suffix, then z=y by the unique decompression property.
Case 2: Assume C(y) ≥ C(x). By symmetry C(xy)=C(yx). Now follow the proof of Case 1.
If C is a normal compressor, then E_{C}(x,y) satisfies the metric (in)equalities up to logarithmic additive precision.
Proof. Only the triangular inequality is nonobvious. By (??) C(xy)+C(z) ≤ C(xz)+C(yz) up to logarithmic additive precision. There are six possibilities, and we verify the correctness of the triangular inequality in turn for each of them. Assume C(x) ≤ C(y) ≤ C(z): Then C(xy) − C(x) ≤ C(xz) − C(x) + C(yz)−C(y). Assume C(y) ≤ C(x) ≤ C(z): Then C(xy) − C(y) ≤ C(xz) − C(y) + C(yz)−C(x). Assume C(x) ≤ C(z) ≤ C(y): Then C(xy) − C(x) ≤ C(xz) − C(x) + C(yz)−C(z). Assume C(y) ≤ C(z) ≤ C(x): Then C(xy) − C(y) ≤ C(xz) − C(z) + C(yz)−C(y). Assume C(z) ≤ C(x) ≤ C(y): Then C(xy) − C(x) ≤ C(xz) − C(z) + C(yz)−C(z). Assume C(z) ≤ C(y) ≤ C(x): Then C(xy) − C(y) ≤ C(xz) − C(z) + C(yz)−C(z).
If C is a normal compressor, then E_{C}^{+} (x,y)= max{C(x), C(y) }.
Proof. Consider a pair (x,y). The max{C(xz)−C(z): C(z) ≤ C(y) } is C(x) which is achieved for z = λ, the empty word, with C(λ) = 0. Similarly, the max{C(yz)−C(z): C(z) ≤ C(x) } is C(y). Hence the lemma.
The normalized version of the admissible distance E_{C}(x,y), the compressor C based approximation of the normalized information distance (??), is called the normalized compression distance or NCD :
NCD (x,y) = 
 . (1) 
This NCD is the main concept of this work. It is the realworld version of the ideal notion of normalized information distance NID in (??).
In practice, the NCD is a nonnegative number 0 ≤ r ≤ 1 + є representing how different the two files are. Smaller numbers represent more similar files. The є in the upper bound is due to imperfections in our compression techniques, but for most standard compression algorithms one is unlikely to see an є above 0.1 (in our experiments gzip and bzip2 achieved NCD ’s above 1, but PPMZ always had NCD at most 1).
There is a natural interpretation to NCD (x,y): If, say, C(y) ≥ C(x) then we can rewrite
NCD (x,y) = 
 . 
That is, the distance NCD (x,y) between x and y is the improvement due to compressing y using x as previously compressed “data base,” and compressing y from scratch, expressed as the ratio between the bitwise length of the two compressed versions. Relative to the reference compressor we can define the information in x about y as C(y)−C(yx). Then, using (??),
NCD (x,y) = 1 − 
 . 
That is, the NCD between x and y is 1 minus the ratio of the information x about y and the information in y.
If the compressor is normal, then the NCD is a normalized admissible distance satsifying the metric (in)equalities, that is, a similarity metric.
Proof. If the compressor is normal, then by Lemma ?? and Lemma ??, the NCD is a normalized admissible distance. It remains to show it satisfies the three metric (in)equalities.
 ≤ 
 . 

 ≤ 
 + 
 . 
 ≤ 
 + 
 . 
QuasiUniversality: We now digress to the theory developed in [], which formed the motivation for developing the NCD . If, instead of the result of some real compressor, we substitute the Kolmogorov complexity for the lengths of the compressed files in the NCD formula, the result is the NID as in (??). It is universal in the following sense: Every admissible distance expressing similarity according to some feature, that can be computed from the objects concerned, is comprised (in the sense of minorized) by the NID . Note that every feature of the data gives rise to a similarity, and, conversely, every similarity can be thought of as expressing some feature: being similar in that sense. Our actual practice in using the NCD falls short of this ideal theory in at least three respects:
(i) The claimed universality of the NID holds only for indefinitely long sequences x,y. Once we consider strings x,y of definite length n, it is only universal with respect to “simple” computable normalized admissible distances, where “simple” means that they are computable by programs of length, say, logarithmic in n. This reflects the fact that, technically speaking, the universality is achieved by summing the weighted contribution of all similarity distances in the class considered with respect to the objects considered. Only similarity distances of which the complexity is small (which means that the weight is large), with respect to the size of the data concerned, kick in.
(ii) The Kolmogorov complexity is not computable, and it is in principle impossible to compute how far off the NCD is from the NID . So we cannot in general know how well we are doing using the NCD .
(iii) To approximate the NCD we use standard compression programs like gzip, PPMZ, and bzip2. While better compression of a string will always approximate the Kolmogorov complexity better, this may not be true for the NCD . Due to its arithmetic form, subtraction and division, it is theoretically possible that while all items in the formula get better compressed, the improvement is not the same for all items, and the NCD value moves away from the NID value. In our experiments we have not observed this behavior in a noticable fashion. Formally, we can state the following:
Let d be a computable normalized admissible distance and C be a normal compressor. Then, NCD (x,y) ≤ α d(x,y) + є, where for C(x) ≥ C(y), we have α = D^{+}(x)/ C(x) and є = (C(xy)−K(xy))/C(x), with C(xy) according to (??).
Proof. Fix d,C,x,y in the statement of the theorem. Since the NCD is symmetrical, we can, without loss of generality, let C(x) ≥ C(y). By (??) and the symmetry property C(xy)=C(yx) we have C(xy) ≥ C(yx). Therefore, NCD (x,y)= C(xy)/C(x). Let d(x,y) be the normalized version of the admissible distance D(x,y); that is, d(x,y)= D(x,y)/D^{+}(x,y). Let d(x,y)=e. By (??), there are < 2^{eD+(x)+1} many (x,v) pairs, such that d(x,v) ≤ e and C(y) ≤ C(x). Since d is computable, we can compute and enumerate all these pairs. The initially fixed pair (x,y) is an element in the list and its index takes ≤ eD^{+}(x)+1 bits. Therefore, given x, the y can be described by at most eD^{+}(x)+O(1) bits—its index in the list and an O(1) term accounting for the lengths of the programs involved in reconstructing y given its index in the list, and algorithms to compute functions d and C. Since the Kolmogorov complexity gives the length of the shortest effective description, we have K(yx) ≤ eD^{+}(x)+O(1). Substitution, rewriting, and using K(xy) ≤ E(x,y) ≤ D(x,y) up to ignorable additive terms (Section ??), yields NCD (x,y) = C(xy)/C(x) ≤ α e + є , which was what we had to prove.
Clustering according to NCD will group sequences together that are similar according to features that are not explicitly known to us. Analysis of what the compressor actually does, still may not tell us which features that make sense to us can be expressed by conglomerates of features analyzed by the compressor. This can be exploited to track down unknown features implicitly in classification: forming automatically clusters of data and see in which cluster (if any) a new candidate is placed.
Another aspect that can be exploited is exploratory: Given that the NCD is small for a pair x,y of specific sequences, what does this really say about the sense in which these two sequences are similar? The above analysis suggests that close similarity will be due to a dominating feature (that perhaps expresses a conglomerate of subfeatures). Looking into these deeper causes may give feedback about the appropriateness of the realized NCD distances and may help extract more intrinsic information about the objects, than the oblivious division into clusters, by looking for the common features in the data clusters.
NCD is sometimes considered a mysterious and obscure measure of information distance. In fact, as we explain in this section, in some cases it can be thought of as a generalization and extension of older and wellestablished methods. The Normalized Information Distance is a purely theoretical concept and cannot be exactly computed for even the simplest files due to the inherent incomputability of Kolmogorov Complexity. The Normalized Compression Distance, however, replaces the uncomputable K with an approximation based on a particular data compressor. Different data compression algorithms lead to different varieties of NCD. Modern data compression programs use highly evolved and complicated schemes that involve stochastic and adaptive modelling of the data at many levels simultaneously. These are all but impossible to analyze from a precise mathematical viewpoint, and thus many consider modern data compression as much an art as a science. If, however, we look instead at the compressors popular in UNIX in the 1970’s, we can begin to understand how NCD achieves its results. As we show in this section, it turns out that with such simple compressors, the NCD calculates the total KLdivergence to the mean. Below we first (Section ??) connect such compressors to entropy, and then (Section ??) relate them to KLdivergence.
The UNIX System V pack command uses a static (nonadaptive) Huffman coding scheme to compress files. The method works in two passes. First, the input file is considered as a sequence of 8bit bytes, and a histogram is constructed to represent the frequency of each byte. Next, an optimal Huffman code is constructed according to this histogram, and is represented in memory as a Huffman tree. This Huffman tree is written out as a variablelength header in the compressed file. Finally, the algorithm makes a second pass through the file and encodes it using the Huffman code it has constructed. Notice that the coding scheme does not change throughout the duration of the file. It is this failure to adapt that makes this compressor amenable to mathematical analysis. In the following example, we analyze a hypothetical static arithmetic coder which yields simpler codelength equations. The simpler Huffman pack encoder will perform similarly but must round upwards the codelengths of each symbol to a whole number of bits and thus can lose at most 1 bit per symbol as compared to the arithmetic coder described below.
Consider therefore the particular case of a simple static arithmetic coder S. Let S(D) represent the function mapping a file, D, to the number of bits needed to encode D with S. A static arithmetic encoder really models its input file as an i.i.d. (independently, identically distributed) Bernoulli process. For distributions of this type, the code length that is achieved very closely approximates the empirical Shannon entropy of the file [, ] multiplied by the file size N_{D}. Thus, if data are indeed distributed according to a Bernoulli process, then this encoder almost achieves the theoretically ideal Shannon limit. Let us explain this in more detail. Let D be a file over an alphabet Σ. Let n(D,i) denote the number of occurrences of symbol i in file D, and let N_{D} denote the total number of symbols in file D. Then
N_{D} = 
 n(D,i). (1) 
The empirical distribution corresponding to file D is defined as the probability distribution that assigns a probability to each symbol in the alphabet given by
P_{D}(i) = 
 . (2) 
The empirical distribution P_{D} is just the histogram of relative frequencies of the symbols of Σ occurring in D.
It turns out that, when provided the empirical distribution P_{D}, the theoretical arithmetic coder S requires just this many bits:
Z(D) = N_{D} H(P_{D}), (3) 
with H representing Shannon entropy:
H(P_{D}) = 
 − P_{D}(i) logP_{D}(i). (4) 
For a real static arithmetic coding implementation S, there is a need to transmit a small fixed sized header as a preamble to the arithmetic coding. This header provides an encoding of the histogram P_{D} corresponding to the file D to be encoded. This quantity is termed hdr. So:
S(D) = N_{D} H(P_{D}) + hdr. (5) 
To be fully precise, in a real implementation, the number of bits needed is always an integer, so (??) really needs to be rounded up; but the effects of this change is negligible, so we will ignore it. Let us give a little bit more explanation of (??). From the Kraft inequality (Section ??), we know that for any distribution P on strings D ∈ Σ^{n} of length N, there exists a compressor Z such that for all D ∈ Σ^{N}, Z(D) = − logP(D), where again we ignore rounding issues. Now let us model D according to a Bernoulli process, where each element of D is distributed independently according to the empirical distribution P_{D}. Under this distribution, setting D = x_{1}… x_{n},
Such a compressor Z makes use of the empirical distribution P_{D}, so the encoding of D with length Z(D) can only be decoded by a decoder who already knows P_{D}. Thus, to turn Z into a compressor S that can be used on all sequences (and not only those with a given, fixed P_{D}), it suffices to first encode P_{D} using some previously agreedupon code, which takes hdr bits, and then encode D using Z(D) bits. By (??) this is equal to (??).
We now connect the NCD based on a static arithmetic encoder with the KLdivergence []. Consider two files F and G with empirical distributions P_{F} and P_{G}:
P_{F}(i) = 
 ; P_{G}(i) = 
 . (9) 
There is a file B that is the concatenation of F followed by G and has empirical distribution
P_{B}(i) = 
 . (10) 
The size for S run on B is just the size of the histogram hdr and the entropy of P_{B} times the number of symbols:

Recall that KullbackLeibler divergence [] is defined upon two distributions P,Q as
KL(P ∥ Q) = 
 P(i)log 
 , (12) 
so that
S(B) = hdr + N_{F}H(P_{F}) + N_{G}H(P_{G}) + N_{F}KL(P_{F} ∥ P_{B}) + N_{G}KL(P_{G}∥ P_{B}). (13) 
At this point we determine a formula for NCD_{S}. Recall that the NCD is defined in (??) as a function to determine an information distance between two input files:
NCD_{C}(x,y) = 
 . 
Here, C(xy) denotes the compressed size of the concatenation of x and y, C(x) denotes the compressed size of x, and C(y) denotes the compressed size of y. C is a function returning a length, usually measured in bits, and realized by a particular data compression program. Different algorithms will yield different behaviors for this quotient. For our simple compressor S, we get
NCD_{S}(F,G) = 
 . (14) 
Assume without loss of generality that S(F) ≤ S(G), then NCD_{S}(F,G) =


Notice that 0 ≤ N_{F}/N_{G} < 1. It is apparent that P_{B} represents an aggregate distribution formed by combining P_{F} and P_{G}. When N_{F} = N_{G},
 NCD_{S}(F,G) = 1 + 
 , (17) 
or using A(P_{F}, P_{G}) to represent information radius [] or total KLdivergence to the mean [], then
 NCD_{S}(F,G) = 1 + 
 . (18) 
We may interpret NCD_{S} to behave as a ratio of information radius to maximum individual entropy. The static arithmetic coder S severely violates the subadditivity assumption of a normal compressor (Section ??) and causes a positive offset bias of +1. In general, NCD_{S} behaves locally quadratically when at least one of the two files involved has high entropy. This fact can be demonstrated by use of Taylor series to approximate the logarithms in NCD_{S} about P_{B} (we omit the details). When both H(P_{F}) and H(P_{G}) are small, NCD_{S} can grow hyperbolically without bound.
Let us now turn to a new compressor, T. T is a firstorder static arithmetic coder. It maintains a table of Σ separate contexts, and corresponds to modelling data as a firstorder Markov chain. In fact, it can be shown that the correspondence between NCD, KL, and H continues for any finiteorder Markov chain (we omit the details).
We have done an experiment to verify these relations. In this experiment, we create files of exact empirical distributions. The alphabet is Σ = {0,1}. The "fixed" file F is set to a θ = 0.55 Bernoulli binary distribution, i.e. F consists of 55% 1s. The other file G is allowed to vary from 0.30 ≤ θ ≤ 0.80. We have used Michael Schindler’s Range encoder, R, as a fast and simple arithmetic coder. The results are in Figure ??. The graph’s horizontal axis represents empirical bias in file G. The hdr addend is necessary to allow for the empirical discrete finite probability distribution for the input file to be encoded so that during decompression the arithmetic decoder statistics can accurately represent those of the original encoding. There is good agreement between the NCD_{R} and the prediction based on information radius and maximum entropy. In these experiments, 200000 symbol files were used for F and G. The deviation between the NCD_{R} and the information radius (both shown in the graph, with nearly overlapping curves) is on the order of 0.001 bit, and this can be attributed to imperfections in compression, header length, etc. It seems clear that many simple compressors yield simple closedform formulas for specific variants of NCD. It is not clear whether such a close correspondence between the NCD and KLdivergence (or other simple analytic quantities) still holds in realistic situations, where a sophisticated compressor (such as gzip or ppm, or paq) is used on realworld data. The Shannon entropy and KLdivergence are expected codelengths, i.e. theoretical averages taken with respect to some hypothesized distribution. The NCD is based on actual, individual sequence codelengths, obtained with some compressor Z. By the Kraft inequality (Chapter 2), Z must correspond to some distribution P such that for all data sequences D of given length,
Z(D) = − logP(D). 
In case of the static arithmetic encoder, it turned out that this expression could be rewritten as
Z(D) = hdr − logP_{D}(D) = hdr + N_{D} E_{PD}[ − logP_{D}(D)], 
where the latter equality follows from (??) and (??). These two crucial steps, which replace a logprobability of an actually realized sequence by its expectation, allow us to connect NCD with Shannon entropy, and then, KLdivergence. It can readily be seen that a similar replacement can still be done if a fixedorder arithmetic coder is used (corresponding, to, say, kth order Markov chains). However, the larger k, the larger the size of the header will be, and thus the more data are needed before the size of the header becomes negligible. With real data, not generated by any finite order chain, and modern compressors (which are not fixedorder), it is therefore not clear whether an analogue of (??) still holds. This would be an interesting topic for future research.
In this chapter we have introduced the idea of a mathematical distance function and discussed the notion of a similarity metric. We defined NCD and the related NID function, and talked about some properties of each. A strategy for calculating these functions using real compressors was outlined, and a mathematical connection was made between a particular case of NCD and the familiar statistical quantity called KLdivergence.
This chapter is about the quartet method for hierarchical clustering. We introduce the notion of hierarchical clustering in Section ??, and then proceed to explain the quartet method in Section ??. We address computational complexity issues in Section ??. Our line of reasoning leads naturally to a simple but effective nondeterministic algorithm to monotonically approximate a bestfitting solution to a given input quartet cost list. We describe the algorithm in Section ??, with performance analysis in Section ??. In the remainder of the chapter we present a series of experiments demonstrating the tree building system.
We consider the problem of constructing an optimalweight tree from the 3n 4 weighted quartet topologies on n objects, where optimality means that the summed weight of the embedded quartet topologies is optimal (so it can be the case that the optimal tree embeds all quartets as nonoptimal topologies). We present a heuristic for reconstructing the optimalweight tree, and a canonical manner to derive the quartettopology weights from a given distance matrix. The method repeatedly transforms a bifurcating tree, with all objects involved as leaves, achieving a monotonic approximation to the exact single globally optimal tree. This contrasts to other heuristic search methods from biological phylogeny, like DNAML or quartet puzzling, which, repeatedly, incrementally construct a solution from a random order of objects, and subsequently add agreement values. We do not assume that there exists a true bifurcating supertree that embeds each quartet in the optimal topology, or represents the distance matrix faithfully—not even under the assumption that the weights or distances are corrupted by a measuring process. Our aim is to hierarchically cluster the input data as faithfully as possible, both phylogenetic data and data of completely different types. In our experiments with natural data, like genomic data, texts or music, the global optimum appears to be reached. Our method is capable of handling over 100 objects, possibly up to 1000 objects, while no existing quartet heuristic can computationally approximate the exact optimal solution of a quartet tree of more than about 20–30 objects without running for years. The method is implemented and AVAILABle as public software.
We present a method of hierarchical clustering based on a novel fast randomized hillclimbing heuristic of a new global optimization criterion. Given a the weights of all quartet topologies, or a matrix of the pairwise distances between the objects, we obtain an output tree with the objects as leaves, and we score how well the tree represents the information in the distance matrix on a scale of 0 to 1. As proof of principle, we experiment on three data sets, where we know what the final answer should be: (i) reconstruct a tree from a distance matrix obtained from a randomly generated tree; (ii) reconstruct a tree from files containing artificial similarities; and (iii) reconstruct a tree from natural files of heterogeneous data of vastly different types. We give examples in wholegenome phylogeny using the whole mitochondrial DNA of the species concerned, in SARS virus localization among other virri, and in analyzing the spreading of the birdflu H5N1 virus mutations. We compare the hierarchical clustering of our method with a more standard method of twodimensional clustering (to show that our dendrogram method of depicting the clusters is more informative). The new method was developed as an auxiliary tool for [, , ], since the available quartet tree methods were too slow when they were exact, and too inaccurate or uncertain when they were statistical incremental. Our new quartet tree heuristic runs orders of magnitudes faster than any other exact quartet tree method, and gives consistently good results in practice.
Relation with Previous Work: The Minimum Quartet Tree Cost (MQTC) problem below for which we give a new computational heuristic is related to the Quartet Puzzling problem, []. There, the quartet topologies are provided with a probability value, and for each quartet the topology with the highest probability is selected (randomly, if there are more than one) as the maximumlikelihood optimal topology. The goal is to find a bifurcating tree that embeds these optimal quartet topologies. In the biological setting it is assumed that the observed genomic data are the result of an evolution in time, and hence can be represented as the leaves of an evolutionary tree. Once we obtain a proper probabilistic evolutionary model to quantify the evolutionary relations between the data we can search for the true tree. In a quartet method one determines the most likely quartet topology under the given assumptions, and then searches for a tree that represents as many of such topologies as is possible. If the theory and data were perfect then there was a tree that represented precisely all most likely quartet topologies. Unfortunately, in real life the theory is not perfect, the data are corrupted, and the observation pollutes and makes errors. Thus, one has to settle for embedding as many most likely quartet topologies as possible, do error correction on the quartet topologies, and so on. For n objects, there are (2n−5)!! = (2n−5) × (2n−3) × ⋯ × 3 unrooted bifurcating trees. For n large, exhaustive search for the optimal tree is impossible, and turns out to be NPhard, and hence infeasible in general. There are two main avenues that have been taken:
(i) Incrementally grow the tree in random order by stepwise addition of objects in the current optimal way, repeat this for different object orders, and add agreement values on the branches, like DNAML [], or quartet puzzling [].
(ii) Approximate the global optimum monotonically or compute it, using geometric algorithm or dynamic programming [], and linear programming [].
These methods, other methods, as well as methods related to the MQT problem, cannot handle more than 15–30 objects [, , , ] directly, even while using farms of desktops. To handle more objects one needs to construct a supertree from the constituent quartet trees for subsets of the original data sets, [], as in [, ].
In 2003 in [, , ] we considered a new approach, like [], and possibly predating it. Our goal was to use a quartet method to obtain highquality hierarchical clustering of data from arbitrary (possibly heterogeneous) domains, not necessarily phylogeny data. We thus do not assume that there exists a true evolutionary tree, and our aim is not to just embed as many optimal quartet topologies as is possible. Instead, for n objects we consider all 3n 4 possible quartet topologies, each with a given weight, and our goal is to find the tree such that the summed weights of the embedded quartet topologies is optimal. We develop an heuristic that monotonically approximates this optimum, a figure of merit that quantifies the quality of the best current candidate tree. We show that the problem is NPhard, but we give evidence that the natural data sets we consider have qualities of smoothness so that the monotonic heuristic obtains the global optimum in a feasible number of steps.
Materials and Methods: Some of the experiments reported are taken from [, , ] where many more can be found. The data samples we used were obtained from standard data bases accessible on the worldwide web, generated by ourselves, or obtained from research groups in the field of investigation. We supply the details with each experiment. The clustering heuristic generates a tree with an optimality quantification, called standardized benefit score or S(T) value in the sequel. Contrary to other phylogeny methods, we do not have agreement or confidence values on the branches: we generate the best tree possible, globally balancing all requirements. Generating trees from the same distance matrix many times resulted in the same tree in case of high S(T) value, or a similar tree in case of moderately high S(T) value, for all distance matrices we used, even though the heuristic is randomized. That is, there is only one way to be right, but increasingly many ways to be increasingly wrong which can all be realized by different runs of the randomized algorithm. The quality of the results depends on how well the hierarchical tree represents the information in the matrix. That quality is measured by the S(T) value, and is given with each experiment. In certain natural data sets, such as H5N1 genomic sequences, consistently high S(T) values are returned even for large sets of objects of 100 or more nodes. In other discordant natural data sets however, the S(T) value deteriorates more and more with increasing number of elements being put in the same tree. The reason is that with increasing size of a discordant natural data set the projection of the information in the distance matrix into a ternary tree gets necessarily increasingly distorted because the underlying structure in the data is incommensurate with any tree shape whatsoever. In this way, larger structures may induce additional “stress” in the mapping that is visible as lower and lower S(T) scores.
Figures: We use two styles to display the hierarchical clusters. In the case of genomics of Eutherian orders, it is convenient to follow the dendrograms that are customary in that area (suggesting temporal evolution) for easy comparison with the literature. In the other experiments (even the genomic SARS experiment) it is more informative to display an unrooted ternary tree (or binary tree if we think about incoming and outgoing edges) with explicit internal nodes. This facilitates identification of clusters in terms of subtrees rooted at internal nodes or contiguous sets of subtrees rooted at branches of internal nodes.
Given a set of objects as points in a space provided with a (not necessarily metric) distance measure, the associated distance matrix has as entries the pairwise distances between the objects. Regardless of the original space and distance measure, it is always possible to configure n objects in ndimensional Euclidean space in such a way that the associated distances are identical to the original ones, resulting in an identical distance matrix. This distance matrix contains the pairwise distance relations according to the chosen measure in raw form. But in this format that information is not easily usable, since for n > 3 our cognitive capabilities rapidly fail. Just as the distance matrix is a reduced form of information representing the original data set, we now need to reduce the information even further in order to achieve a cognitively acceptable format like data clusters. To extract a hierarchy of clusters from the distance matrix, we determine a dendrogram (ternary tree) that agrees with the distance matrix according to a cost measure. This allows us to extract more information from the data than just flat clustering (determining disjoint clusters in dimensional representation).
Clusters are groups of objects that are similar according to our metric. There are various ways to cluster. Our aim is to analyze data sets for which the number of clusters is not known a priori, and the data are not labeled. As stated in [], conceptually simple, hierarchical clustering is among the best known unsupervised methods in this setting, and the most natural way is to represent the relations in the form of a dendrogram, which is customarily a directed binary tree or undirected ternary tree. With increasing number of data items, the projection of the distance matrix information into the tree representation format may get distorted. Not all natural data sets exhibit this phenomenon; but for some, the tree gets increasingly distorted as more objects are added. A similar situation sometimes arises in using alignment cost in genomic comparisons. Experience shows that in both cases the hierarchical clustering methods seem to work best for small sets of data, up to 25 items, and to deteriorate for some (but not all) larger sets, say 40 items or more. This deterioration is directly observable in the S(T) score and degrades solutions in two common forms: tree instability when different or very different solutions are returned on successive runs or tree “overlinearization” when some data sets produce caterpillarlike structures only or predominantly. In case a large set of objects, say 100 objects, clusters with high S(T) value this is evidence that the data are of themselves treelike, and the quartettopology weights, or underlying distances, truly represent to similarity relationships between the data.
Given a set N of n objects, we consider every set of four elements from our set of n elements; there are n 4 such sets. From each set {u,v,w,x} we construct a tree of arity 3, which implies that the tree consists of two subtrees of two leaves each. Let us call such a tree a quartet topology. The set of 3 n 4 quartet topologies induced by N is denoted by Q. We denote a partition {u,v},{w,x} of {u,v,w,x} by uv  wx. There are three possibilities to partition {u,v,w,x} into two subsets of two elements each: (i) uv  wx, (ii) uw  vx, and (iii) ux  vw. In terms of the tree topologies: a vertical bar divides the two pairs of leaf nodes into two disjoint subtrees (Figure ??).
For the moment we consider the class T of undirected trees of arity 3 with n ≥ 4 leaves, labeled with the elements of N. Such trees have n leaves and n−2 internal nodes. For any given tree T from this class, and any set of four leaf labels u,v,w,x ∈ N, we say T is consistent with uv  wx if and only if the path from u to v does not cross the path from w to x. It is easy to see that precisely one of the three possible quartet topologies for any set of 4 labels is consistent for a given tree from the above class, and therefore a tree from T contains precisely n 4 different quartet topologies. We may think of a large tree having many smaller quartet topologies embedded within its structure. Commonly the goal in the quartet method is to find (or approximate as closely as possible) the tree that embeds the maximal number of consistent (possibly weighted) quartet topologies from a given set P ⊆ Q of quartet topologies [] (Figure ??). A weight function W: P → R, with R the set of real numbers determines the weights. The unweighted case is when W(uvwx)=1 for all uvwx ∈ P. The (weighted) Maximum Quartet Consistency (MQC) is defined as follows:
QUESTION: Find T_{0} = max_{T} ∑{ W(uvwx): uvwx ∈ P and uvwx is consistent with T }.
The rationale for the MQC optimization problem is the assumption that there is exists a tree T_{0} as desired in the class T under consideration, and our only problem is to find it. This assumption reflects the genesis of the method in the phylogeny community. Under the assumption that biological species developed by evolution in time, and N is a subset of the now existing species, there is a phylogeny P (tree in T) that represents that evolution. The set of quartet topologies consistent with this tree, has one quartet topology per quartet which is the true one. The quartet topologies in P are the ones which we assume to be among the true quartet topologies, and weights are used to express our relative certainty about this assumption concerning the individual quartet topologies in P.
However, the data may be corrupted so that this assumption is no longer true. In the general case of hierarchical clustering we do not even have a priori knowledge that certain quartet topologies are objectively true and must be embedded. Rather, we are in the position that we can somehow assign a relative importance to the different quartet topologies. Our task is then to balance the importance of embedding different quartet topologies against one another, leading to a tree that represents the concerns as well as possible. We start from a costassignment to the quartet topologies; the method by which we assign costs to the 3n 4 quartet topologies is for now immaterial to our problem. Given a set N of n objects, let Q be the set of quartet topologies, and let C:Q → R be a cost function assigning a real valued cost C_{uvwx} to each quartet uvwx ∈ Q. The cost C_{T} of a tree T with a set N of leaves (external nodes of degree 1) is defined by C_{T} =∑_{{u,v,w,x} ⊆ N} {C_{uvwx}: T is consistent with uv wx}—the sum of the costs of all its consistent quartet topologies.
Given N and C, the Minimum Quartet Tree Cost (MQTC) is min_{T} {C_{T}: T is a tree with the set N labeling its leaves}.
We normalize the problem of finding the MQTC as follows: Consider the list of all possible quartet topologies for all fourtuples of labels under consideration. For each group of three possible quartet topologies for a given set of four labels u,v,w,x, calculate a best (minimal) cost
m(u,v,w,x) = min{ C_{uvwx}, C_{uwvx}, C_{uxvw} } 
and a worst (maximal) cost M(u,v,w,x) = max{ C_{uvwx}, C_{uwvx}, C_{uxvw} }. Summing all best quartet topologies yields the best (minimal) cost m = ∑_{{u,v,w,x} ⊆ N} m(u,v,w,x). Conversely, summing all worst quartet topologies yields the worst (maximal) cost M = ∑_{{u,v,w,x} ⊆ N} M(u,v,w,x). For some distance matrices, these minimal and maximal values can not be attained by actual trees; however, the score C_{T} of every tree T will lie between these two values. In order to be able to compare the scores of quartet trees for different numbers of objects in a uniform way, we now rescale the score linearly such that the worst score maps to 0, and the best score maps to 1:
The normalized tree benefit score S(T) is defined by
S(T) = (M−C_{T})/(M−m). 
Our goal is to find a full tree with a maximum value of S(T), which is to say, the lowest total cost. Now we can rephrase the MQTC problem in such a way that solutions of instances of different sizes can be uniformly compared in terms of relative quality:
Definition of the MQTC problem:
QUESTION: Find a tree T_{0} with S(T_{0})=max{S(T): T is a tree with the set N labeling its leaves}.
The hardness of Quartet Puzzling is informally mentioned in the literature [, , ], but we provide explicit proofs. To express the notion of computational difficulty one uses the notion of “nondeterministic polynomial time (NP)”. If a problem concerning n objects is NPhard this means that the best known algorithm for this (and a wide class of significant problems) requires computation time exponential in n. That is, it is infeasible in practice. The MQC decision problem is the following: Given a set N of n objects, let T be a tree of which the n leaves are labeled by the objects, and let Q be the set of quartet topologies and Q_{T} be the set of quartet topologies embedded in T. Given a set of quartet topologies P ⊆ Q, and an integer k, the problem is to decide whether there is a binary tree T such that P ∩Q_{T} > k. In [] it is shown that the MQC decision problem is NPhard. We have formulated the NPhardness of the socalled incomplete MQC decision problem, the less general complete MQC decision problem requires P to contain precisely one quartet topology per quartet out of N, and is proven to be NPhard as well in [].
The MQTC decision problem is NPhard.
Proof. By reduction from the MQC decision problem. For every MQC decision problem one can define a corresponding MQTC decision problem that has the same solution: give the quartet topologies in P cost 0 and the ones in Q − P cost 1. Consider the MQTC decision problem: is there a tree T with the set N labeling its leaves such that C_{T} < n 4 −k? An alternative equivalent formulation is: is there a tree T with the set N labeling its leaves such that
S(T) > 
 ? 
Note that every tree T with the set N labeling its leaves has precisely one out of the three quartet topologies of every of the n 4 quartets embedded in it. Therefore, the cost C_{T} = n 4−P ∩Q_{T} . If the answer to the above question is affirmative, then the number of quartet topologies in P that are embedded in the tree exceeds k; if it is not then there is no tree such that the number of quartet topologies in P embedded in it exceeds k. This way the MQC decision problem can be reduced to the MQTC decision problem, which shows also the latter to be NPhard.
Is it possible that the best S(T) value is always one, that is, there always exists a tree that embeds all quartets at minimum cost quartet topologies? Consider the case n=N=4. Since there is only one quartet, we can set T_{0} equal to the minimum cost quartet topology, and have S(T_{0})=1. A priori we cannot exclude the possibility that for every N and C there always is a tree T_{0} with S(T_{0})=1. In that case, the MQTC Problem reduces to finding that T_{0}. However, the situation turns out to be more complex. Note first that the set of quartet topologies uniquely determines a tree in T, [].
Let T,T′ be different labeled trees in T and let Q_{T},Q_{T′} be the sets of embedded quartet topologies, respectively. Then, Q_{T} ≠ Q_{T′}.
A complete set of quartet topologies on N is a set containing precisely one quartet topology per quartet. There are 3^{n 4} such combinations, but only 2^{n 2} labeled undirected graphs on n nodes (and therefore  T ≤ 2^{n 2}). Hence, not every complete set of quartet topologies corresponds to a tree in T. This already suggests that we can weight the quartet topologies in such a way that the full combination of all quartet topologies at minimal costs does not correspond to a tree in T, and hence S(T_{0}) < 1 for T_{0} ∈ T realizing the MQTC optimum. For an explicit example of this, we use that a complete set corresponding to a tree in T must satisfy certain transitivity properties, [, ]:
Let T be a tree in the considered class with leaves N, Q the set of quartet topologies and Q_{0} ⊆ Q. Then Q_{0} uniquely determines T if
(i) Q_{0} contains precisely one quartet topology for every quartet, and
(ii) For all {a,b,c,d,e} ⊆ N, if abbc, abde ∈ Q then abce ∈ Q, as well as if abcd, bcde ∈ Q then abde ∈ Q.
There are N (with n=N=5) and a cost function C such that, for every T ∈ T, S(T) does not exceed 4/5.
Proof. Consider N={u,v,w,x,y} and C(uvwx) = 1−є (є > 0), C(uwxv)= C(uxvw)=0, C(xyuv)=C(wyuv)=C(uywx)=C(vywx)=0, and C(abcd)=1 for all remaining quartet topologies abcd ∈ Q. We see that M= 5 − є, m=0. The tree T_{0} = (y,((u,v),(w,x))) has cost C_{T0}= 1−є, since it embeds quartet topologies uwxv, xyuv, wyuv, uywx, vywx. We show that T_{0} achieves the MQTC optimum. Case 1: If a tree T ≠ T_{0} embeds uvwx, then it must by Item (i) of Lemma ?? also embed a quartet topology containing y that has cost 1.
Case 2: If a tree T ≠ T_{0} embeds uwxv and xyuv, then it must by Item (ii) of the Lemma ?? also embed uwxy, and hence have cost C_{T} ≥ 1. Similarly, all other remaining cases of embedding a combination of a quartet topology not containing y of 0 cost with a quartet topology containing y of 0 cost in T, imply an embedded quartet topology of cost 1 in T.
Altogether, the MQTC optimization problem is infeasible in practice, and natural data can have an optimal S(T)< 1. In fact, it follows from the above analysis that to determine S(T) in general is NPhard. In [] a polynomial time approximation scheme for complete MQC is exhibited, a theoretical approximation scheme allowing the approximation of the optimal solution up to arbitrary precision, with running time polynomial in the inverse of that precision. We say “theoretical” since that algorithm would run in something like n^{19}. For incomplete MQC it is shown that even such a theoretical algorithm does not exist, unless P=NP. Hence, computation of the MQTC optimum, and even its approximation with given precision, requires superpolynomial time unless P=NP. Therefore, any practical approach to obtain or approximate the MQTC optimum requires heuristics.
Our algorithm is essentially randomized hillclimbing, using parallellized Genetic Programming, where undirected trees evolve in a random walk driven by a prescribed fitness function. We are given a set N of n objects and a weighting function W.
We define a simple mutation on a labeled undirected ternary tree as one of three possible transformations:
Each of these simple mutations keeps the number of leaf nodes and internal nodes in the tree invariant; only the structure and placements change. A kmutation is a sequence of k simple mutations. Thus, a simple mutation is a 1mutation.
Step 1: First, a random tree T ∈ T with 2n−2 nodes is created, consisting of n leaf nodes (with 1 connecting edge) labeled with the names of the data items, and n−2 nonleaf or internal nodes labeled with the lowercase letter “k” followed by a unique integer identifier. Each internal node has exactly three connecting edges.
Step 2: For this tree T, we calculate the total cost of all embedded quartet topologies, compute S(T).
Comment: A tree is consistent with precisely 1/3 of all quartet topologies, one for every quartet. A random tree is likely to be consistent with about 1/3 of the best quartet topologies—but this is necessarily imprecise because of dependencies.
Step 3: The currently best known tree variable T_{0} is set to T: T_{0} ← T.
Comment: This T_{0} is used as the basis for further searching.
Step 4: Pick a number k with probability p(k) = c /(k (logk)^{2}) where 1/c = ∑_{k=1}^{∞} 1/(k (logk)^{2}).
Comment: This number k is the number of simple mutations that we will perform in the next kmutation. The probability distribution p(k) is easily generated by running a random fair bit generator and set k to the length of the first selfdelimiting sequence generated. That is, if x=x_{1} … x_{k} ∈ {0,1}^{k} (x=k ≥ 1), then x= 1^{k−1} 0 x, x′= xx, and x″ = x′x′. Thus, the length x″=k + logk + 2 loglogk . The probability of generating x″ corresponding to a given x of length k by fair coin flips is 2^{−x″}= 2^{−k− logk − 2 loglogk} = 2^{−k}/(k (logk)^{2}). The probability of generating x″ corresponding to some x of length k is 2^{k} times as large, that is, 1/(k (logk)^{2}). In practice, we used a “shifted” fat tail distribution 1/((k+2) (logk+2)^{2})
Step 5: Compose a kmutation by, for each such simple mutation, choosing one of the three types listed above with equal probability. For each of these simple mutations, we uniformly at random select leaves or internal nodes, as appropriate.
Comment: Notice that trees which are close to the original tree (in terms of number of simple mutation steps in between) are examined often, while trees that are far away from the original tree will eventually be examined, but not very frequently.
Step 6: In order to search for a better tree, we simply apply the kmutation constructed in Step 5 on T_{0} to obtain T′, and then calculate S(T′). If S(T′) ≥ S(T_{0}), then replace the current candidate in T_{0} by T (as the new best tree): T_{0} ← T.
Step 7: If S(T_{0}) =1 or a termination condition to be discussed below holds, then output the tree in T_{0} as the best tree and halt. Otherwise, go to Step 4.
We have chosen p(k) to be a “fattail” distribution, with the fattest tail possible, so that we may concentrate maximal probability also on the larger values of k. That way, the likelihood of getting trapped in local minima is minimized. In contrast, if one would choose an exponential scheme, like q(k)=c e^{−k}, then the larger values of k would arise so scarcely that practically speaking the distinction between being absolutely trapped in a local optimum, and the very low escape probability, would be insignificant. Considering positivevalued probability mass functions q: N → (0,1], with N the natural numbers, as we do here, we note that such a function (i) lim_{k → ∞} q(k) =0, and (ii) ∑_{k=1}^{∞} q(k) =1. Thus, every function of the natural numbers that has strictly positive values and converges can be normalized to such a probability mass function. For smooth analytic functions that can be expressed a series of fractional powers and logarithms, the borderline between converging and diverging is as follows: ∑1/k, ∑1/(k logk), ∑1/(k logk loglogk) and so on diverge, while ∑1/k^{2}, ∑1/(k (logk)^{2}),∑1/(k logk (loglogk)^{2}) and so on converge. Therefore, the maximal fat tail of a “smooth” function f(x) with ∑f(x) < ∞ arises for functions at the edge of the convergence family. The distribution p(k)= c /(k (logk)^{2}) is as close to the edge as is reasonable, and because the used coding x → x″ is a prefix code we have ∑1/(k (logk)^{2}) ≤ 1 by the Kraft Inequality (see for example []) and therefore c ≥ 1. Let us see what this means for our algorithm using the chosen distribution p(k). For N=64, say, we can change any tree in T to any other tree in T with a 64mutation. The probability of such a complex mutation occurring is quite large with such a fat tail: 1/(64 · 6^{2}) = 1/2304, that is, more than 40 times in 100,000 generations. If we can get out of a local minimum with already a 32mutation, then this occurs with probability at least 1/800, so 125 times, and with a 16mutation with probability at least 1/196, so 510 times.
The main problem with hillclimbing algorithms is that they can get stuck in a local optimum. However, by randomly selecting a sequence of simple mutations, longer sequences with decreasing probability, we essentially run a Metropolis Monte Carlo algorithm [], reminiscent of simulated annealing [] at random temperatures. Since there is a nonzero probability for every tree in T being transformed into every other tree in T, there is zero probability that we get trapped forever in a local optimum that is not a global optimum. That is, trivially: (i) The algorithm approximates the MQTC optimal solution monotonically in each run.
(ii) The algorithm without termination condition solves the MQTC optimization problem eventually with probability 1 (but we do not in general know when the optimum has been reached in a particular run).
The main question therefore is the convergence speed of the algorithm on natural data, and a termination criterion to terminate the algorithm when we have an acceptable approximation. From the impossibility result in [] we know that there is no polynomial approximation scheme for MQTC optimization, and whether our scheme is expected polynomial time seems to require proving that the involved Metropolis chain is rapidly mixing [], a notoriously hard and generally unsolved problem. In practice, in our experiments there is unanimous evidence that for the natural data and the weighting function we have used, convergence is always fast. We have to determine the cost of n 4 quartets to determine each S(T) value. Hence the algorithm runs in time at least that much. In experiments we found that for the same data set different runs consistently showed the same behavior, for example Figure ?? for a 60object computation. There the S(T) value leveled off at about 70,000 examined trees, and the termination condition was “no improvement in 5,000 trees.” Different random runs of the algorithm nearly always gave the same behavior, returning a tree with the same S(T) value, albeit a different tree in most cases with here S(T) ≈ 0.865, a relatively low value. That is, since there are many ways to find a tree of optimal S(T) value, and apparently the algorithm never got trapped in a lower local optimum. For problems with high S(T) value, as we see later, the algorithm consistently returned the same tree. This situation is perhaps similar to the behavior of the Simplex method in linear programming, that can be shown to run in exponential time on a badly chosen problem instance, but in practice on natural problems consistently runs in linear time.
Note that if a tree is ever found such that S(T) = 1, then we can stop because we can be certain that this tree is optimal, as no tree could have a lower cost. In fact, this perfect tree result is achieved in our artificial tree reconstruction experiment (Section ??) reliably in a few minutes. For realworld data, S(T) reaches a maximum somewhat less than 1, presumably reflecting distortion of the information in the distance matrix data by the best possible tree representation, as noted above, or indicating getting stuck in a local optimum or a search space too large to find the global optimum. On many typical problems of up to 40 objects this treesearch gives a tree with S(T) ≥ 0.9 within half an hour. For large numbers of objects, tree scoring itself can be slow: as this takes order n^{4} computation steps. Current single computers can score a tree of this size in about a minute. Additionally, the space of trees is large, so the algorithm may slow down substantially. For larger experiments, we used the C program called partree (part of the CompLearn package []) with MPI (Message Passing Interface, a common standard used on massively parallel computers) on a cluster of workstations in parallel to find trees more rapidly. We can consider the graph mapping the achieved S(T) score as a function of the number of trees examined. Progress occurs typically in a sigmoidal fashion towards a maximal value ≤ 1, Figure ??.
The termination condition is of two types and which type is used determines the number of objects we can handle.
Simple termination condition: We simply run the algorithm until it seems no better trees are being found in a reasonable amount of time. Here we typically terminate if no improvement in S(T) value is achieved within 100,000 examined trees. This criterion is simple enough to enable us to hierarchically cluster data sets up to 80 objects in a few hours. This is way above the 15–30 objects in the previous exact (nonincremental) methods (see Introduction).
Agreement termination condition: In this more sophisticated method we select a number 2 ≤ r ≤ 6 of runs, and we run r invocations of the algorithm in parallel. Each time an S(T) value in run i=1, …, r is increased in this process it is compared with the S(T) values in all the other runs. If they are all equal, then the candidate trees of the runs are compared. This can be done by simply comparing the ordered lists of embedded quartet topologies, in some standard order, since the set of embedded quartet topologies uniquely determines the quartet tree by []. If the r candidate trees are identical, then terminate with this quartet tree as output, otherwise continue the algorithm.
This termination condition takes (for the same number of steps per run) about r times as long as the simple termination condition. But the termination condition is much more rigorous, provided we choose r appropriate to the number n of objects being clustered. Since all the runs are randomized independently at startup, it seems very unlikely that with natural data all of them get stuck in the same local optimum with the same quartet tree instance, provided the number n of objects being clustered is not too small. For n = 5 and the number of invocations r=2, there is a reasonable probability that the two different runs by chance hit the same tree in the same step. This phenomenon leads us to require more than two successive runs with exact agreement before we may reach a final answer for small n. In the case of 4≤ n ≤ 5, we require 6 dovetailed runs to agree precisely before termination. For 6 ≤ n ≤ 9, r = 5. For 10 ≤ n ≤ 15, r = 4. For 16 ≤ n ≤ 17, r = 3. For all other n ≥ 18, r = 2. This yields a reasonable tradeoff between speed and accuracy.
It is clear that there is only one tree with S(T)=1 (if that is possible for the data), and random trees (the majority of all possible quartet trees) have S(T) ≈ 1/3 (above). This gives evidence that the number of quartet trees with large S(T) values is much smaller than the number of trees with small S(T) values. It is furthermore evident that the precise relation depends on the data set involved, and hence cannot be expressed by a general formula without further assumptions on the data. However, we can safely state that small data sets, of say ≤ 15 objects, that in our experience often lead to S(T) values close to 1 have very few quartet trees realizing the optimal S(T) value. On the other hand, those large sets of 60 or more objects that contain some inconsistency and thus lead to a low final S(T) value also tend to exhibit more variation as one might expect. This suggests that in the agreement termination method each run will get stuck in a different quartet tree of a similar S(T) value, so termination with the same tree is not possible. Experiments show that with the rigorous agreement termination we can handle sets of up to 40 objects, and with the simple termination up to at least 80 objects on a single computer or 100200 objects using a cluster of computers in parallel.
We used the CompLearn package, (further described in Chapter 10) [], to analyze a “10mammals” example with zlib compression yielding a 10 × 10 distance matrix, similar to the examples in Section ??. The algorithm starts with four randomly initialized trees. It tries to improve each one randomly and finishes when they match. Thus, every run produces an output tree, a maximum score associated with this tree, and has examined some total number of trees, T, before it finished. Figure ?? shows a graph displaying a histogram of T over one thousand runs of the distance matrix. The xaxis represents a number of trees examined in a single run of the program, measured in thousands of trees and binned in 1000wide histogram bars. The maximum The graph suggests a Poisson distribution. About 2/3rd of the trials take less than 4000 trees. In the thousand trials above, 994 ended with the optimal S(T) = 0.999514 . The remaining six runs returned 5 cases of the secondhighest score, S(T) = 0.995198 and one case of S(T) = 0.992222 . It is important to realize that outcome stability is dependent on input matrix particulars.
Another interesting distribution is the mutation stepsize. Recall that the mutation length is drawn from a shifted fattail distribution. But if we restrict our attention to just the mutations that improve the S(T) value, then we may examine these statistics to look for evidence of a modification to this distribution due to, for example, the presence of very many isolated areas that have only longdistance ways to escape. Figure ?? shows the histogram of successful mutation lengths (that is, number of simple mutations composing a single complex mutation) and rejected lengths (both normalized) which shows that this is not the case. Here the xaxis is the number of mutation steps and the yaxis is the normalized proportion of times that step size occurred. This gives good empirical evidence that in this case, at least, we have a relatively easy search space, without large gaps.
With natural data sets, say music data, one may have the preconception (or prejudice) that music by Bach should be clustered together, music by Chopin should be clustered together, and so should music by rock stars. However, the preprocessed music files of a piece by Bach and a piece by Chopin, or the Beatles, may resemble one another more than two different pieces by Bach—by accident or indeed by design and copying. Thus, natural data sets may have ambiguous, conflicting, or counterintuitive outcomes. In other words, the experiments on natural data sets have the drawback of not having an objective clear “correct” answer that can function as a benchmark for assessing our experimental outcomes, but only intuitive or traditional preconceptions. We discuss three experiments that show that our program indeed does what it is supposed to do—at least in artificial situations where we know in advance what the correct answer is.
Given a distance matrix, with entries giving the pairwise distances between the objects, we want to form a hierarchical cluster by representing the objects as leaves of a ternary tree representing the distances in the matrix as faithfully as possible. It is important that we do not assume that there is a true tree; rather, we want to model the data as well as possible. The cost of a quartet topology is defined as the sum of the distances between each pair of neighbors; that is, C_{uvwx} = d(u,v) + d(w,x). This seems most natural given a distance matrix.
Recall that the problem of clustering data consists of two parts: (i) extracting a distance matrix from the data, and (ii) constructing a tree from the distance matrix using our novel quartet based heuristic. To check the new quartet tree method in action we use a new compression based distance, called NCD. This metric distance was codeveloped by us in [, , ], as a normalized version of the “information metric” of [, ]. Roughly speaking, two objects are deemed close if we can significantly “compress” one given the information in the other, the idea being that if two pieces are more similar, then we can more succinctly describe one given the other. The mathematics used is based on Kolmogorov complexity theory []. In [] we defined a new class of (possibly nonmetric) distances, taking values in [0,1] and appropriate for measuring effective similarity relations between sequences, say one type of similarity per distance, and vice versa. It was shown that an appropriately “normalized” information metric minorizes every distance in the class. It discovers all effective similarities in the sense that if two objects are close according to some effective similarity, then they are also close according to the normalized information distance. Put differently, the normalized information distance represents similarity according to the dominating shared feature between the two objects being compared. In comparisons of more than two objects, different pairs may have different dominating features. The normalized information distance is a metric and takes values in [0,1]; hence it may be called “the” similarity metric. To apply this ideal precise mathematical theory in real life, we have to replace the use of the uncomputable Kolmogorov complexity by an approximation using a standard realworld compressor, resulting in the NCD, see []. This has been used in the first completely automatic construction of the phylogeny tree based on whole mitochondrial genomes, [, , ], a completely automatic construction of a language tree for over 50 EuroAsian languages [], detects plagiarism in student programming assignments [], gives phylogeny of chain letters [], and clusters music [, ], Analyzing network traffic and worms using compression [], and many more topics []. The method turns out to be robust under change of the underlying compressortypes: statistical (PPMZ), LempelZiv based dictionary (gzip), block based (bzip2), or special purpose (Gencompress).
Oblivious to the problem area concerned, simply using the distances according to the NCD above, the method described in this thesis fully automatically classifies the objects concerned. The method has been released in the public domain as opensource software: The CompLearn Toolkit [] is a suite of simple utilities that one can use to apply compression techniques to the process of discovering and learning patterns in completely different domains, and hierarchically cluster them using the new quartet method described in this thesis. In fact, this method is so general that it requires no background knowledge about any particular subject area. There are no domainspecific parameters to set, and only a handful of general settings.
We first test whether the quartetbased tree construction heuristic is trustworthy: We generated a ternary tree T with 18 leaves, using the pseudorandom number generator “rand” of the Ruby programming language, and derived a metric from it by defining the distance between two nodes as follows: Given the length of the path from a to b, in an integer number of edges, as L(a,b), let
d(a,b) = 
 , 
except when a = b, in which case d(a,b) = 0. It is easy to verify that this simple formula always gives a number between 0 and 1, and is monotonic with path length. Given only the 18× 18 matrix of these normalized distances, our quartet method exactly reconstructed the original tree T represented in Figure ??, with S(T)=1.
Given that the tree reconstruction method is accurate on clean consistent data, we tried whether the full procedure works in an acceptable manner when we know what the outcome should be like. We used the “rand” pseudorandom number generator from the C programming language standard library under Linux. We randomly generated 11 separate 1kilobyte blocks of data where each byte was equally probable and called these tags. Each tag was associated with a different lowercase letter of the alphabet. Next, we generated 22 files of 80 kilobyte each, by starting with a block of purely random bytes and applying one, two, three, or four different tags on it. Applying a tag consists of ten repetitions of picking a random location in the 80kilobyte file, and overwriting that location with the globally consistent tag that is indicated. So, for instance, to create the file referred to in the diagram by “a,” we start with 80 kilobytes of random data, then pick ten places to copy over this random data with the arbitrary 1kilobyte sequence identified as tag a. Similarly, to create file “ab,” we start with 80 kilobytes of random data, then pick ten places to put copies of tag a, then pick ten more places to put copies of tag b (perhaps overwriting some of the a tags). Because we never use more than four different tags, and therefore never place more than 40 copies of tags, we can expect that at least half of the data in each file is random and uncorrelated with the rest of the files. The rest of the file is correlated with other files that also contain tags in common; the more tags in common, the more related the files are. The compressor used to compute the NCD matrix was bzip2. The resulting tree is given in Figure ??; it can be seen that the clustering has occurred exactly as we would expect. The S(T) score is 0.905.
We will provide more examples of natural data later in this thesis.
We test gross classification of files based on heterogeneous data of markedly different file types: (i) Four mitochondrial gene sequences, from a black bear, polar bear, fox, and rat obtained from the GenBank Database on the worldwide web; (ii) Four excerpts from the novel The Zeppelin’s Passenger by E. Phillips Oppenheim, obtained from the Project Gutenberg Edition on the WorldWide web; (iii) Four MIDI files without further processing; two from Jimi Hendrix and two movements from Debussy’s Suite Bergamasque, downloaded from various repositories on the worldwide web; (iv) Two Linux x86 ELF executables (the cp and rm commands), copied directly from the RedHat 9.0 Linux distribution; and (v) Two compiled Java class files, generated by ourselves. The compressor used to compute the NCD matrix was bzip2. As expected, the program correctly classifies each of the different types of files together with like near like. The result is reported in Figure ?? with S(T) equal to the very high confidence value 0.984. This experiment shows the power and universality of the method: no features of any specific domain of application are used. We believe that there is no other method known that can cluster data that is so heterogeneous this reliably. This is borne out by the massive experiments with the method in [].
Like most hierarchical clustering methods for natural data, the quartet tree method has been developed in the biological setting to determine phylogeny trees from genomic data. In that setting, the data are (parts of) genomes of currently existing species, and the purpose is to reconstruct the evolutionary tree that led to those species. Thus, the species are labels of the leaves, and the tree is traditionally binary branching with each branching representing a split in lineages. The internal nodes and the root of the tree correspond with extinct species (possibly a still existing species in a leaf directly connected to the internal node). The case is roughly similar for the language tree reconstruction mentioned in the Introduction. The root of the tree is commonly determined by adding an object that is known to be less related to all other objects than the original objects are with respect to each other. Where the unrelated object joins the tree is where we put the root. In these settings, the direction from the root to the leaves represents an evolution in time, and the assumption is that there is a true tree we have to discover. However, we can also use the method for hierarchical clustering, resulting an unrooted ternary tree, and the assumption is not that there is a true tree we must discover. To the contrary, there is no true tree, but all we want is to model the similarity relations between the objects as well as possible, given the distance matrix. The interpretation is that objects in a given subtree are pairwise closer (more similar) to each other than any of those objects is with respect to any object in a disjoint subtree.
As an application of our methods we clustered the SARS virus after its sequenced genome was made publicly available, in relation to potential similar virii. The 15 virus genomes were downloaded from The Universal Virus Database of the International Committee on Taxonomy of Viruses, available on the worldwide web. The SARS virus was downloaded from Canada’s Michael Smith Genome Sciences Centre which had the first public SARS Coronavirus draft whole genome assembly available for download (SARS TOR2 draft genome assembly 120403). The NCD distance matrix was computed using the compressor bzip2. The relations in Figure ?? are very similar to the definitive tree based on medicalmacrobiogenomics analysis, appearing later in the New England Journal of Medicine, []. We depicted the figure in the ternary tree style, rather than the genomicsdendrogram style, since the former is more precise for visual inspection of proximity relations.
More recently, we downloaded 100 different H5N1 sample genomes from the NCBI/NIH database online. We simply concatenated all data together directly, ignoring problems of data cleanup and duplication. We were warned in advance that certain coding regions in the viral genome were sometimes listed twice and also many sequences are incomplete or missing certain proteins. In this case we sought to test the robustness at the high end and at the same time verify, contextualize, and expand on the many claims of genetic similarity and diversity in the virology community. We used the CompLearn package, [], with the ppmd compressor for this experiment and performed no alignment step whatsoever. We used order 15 with 250 megabytes memory maximum.
We have abbreviated Ck for Chicken and Dk for duck. Samples are named with species, location, sequence number, followed by the year double digits at the end. Naming is not 100% consistent. We can see the following features in Figure ??, that are possibly significant:
First, there is nearperfect temporal separation by branch and year, going all the way back to HongKong and GuangDong in 1997. Next, there is nearperfect regional separation with clear delineation of Japan and the crucial Qinghai, Astrakhan, Mongolia, and Novosibirsk, as well as nearperfect separation of Vietnam and Thailand. The placement CkVietnamC5804 and Vietnam306204 is interesting in that they are both near Thailand branches and suggest that they may be for example the migratory bird links that have been hypothesized or some other genetic intermediate. There is also throughout the tree substantial agreement with (and independent verification of) independent experts like Dr. Henry L. Niman [] on every specific point regarding genetic similarity. The technique provides here an easy verification procedure without much work.
The amount of digitized music available on the internet has grown dramatically in recent years, both in the public domain and on commercial sites. Napster and its clones are prime examples. Websites offering musical content in some form or other (MP3, MIDI, …) need a way to organize their wealth of material; they need to somehow classify their files according to musical genres and subgenres, putting similar pieces together. The purpose of such organization is to enable users to navigate to pieces of music they already know and like, but also to give them advice and recommendations (“If you like this, you might also like…”). Currently, such organization is mostly done manually by humans, but some recent research has been looking into the possibilities of automating music classification. In [, ] we cluster music using the CompLearn Toolkit []. One example is a small set of classical piano sonatas, consisting of the 4 movements from Debussy’s “Suite Bergamasque,” 4 movements of book 2 of Bach’s “Wohltemperierte Klavier,” and 4 preludes from Chopin’s “Opus 28.” As one can see in Figure ??, our program does a pretty good job at clustering these pieces. The S(T) score is also high: 0.968. The 4 Debussy movements form one cluster, as do the 4 Bach pieces. The only imperfection in the tree, judged by what one would intuitively expect, is that Chopin’s Prélude no. 15 lies a bit closer to Bach than to the other 3 Chopin pieces. This Prélude no 15, in fact, consistently forms an oddoneout in our other experiments as well. This is an example of pure data mining, since there is some musical truth to this, as no. 15 is perceived as by far the most eccentric among the 24 Préludes of Chopin’s opus 28.
As the complete genomes of various species become available, it has become possible to do whole genome phylogeny (this overcomes the problem that using different targeted parts of the genome, or proteins, may give different trees []). Traditional phylogenetic methods on individual genes depended on multiple alignment of the related proteins and on the model of evolution of individual amino acids. Neither of these is practically applicable to the genome level. In absence of such models, a method which can compute the shared information between two sequences is useful because biological sequences encode information, and the occurrence of evolutionary events (such as insertions, deletions, point mutations, rearrangements, and inversions) separating two sequences sharing a common ancestor will result in the loss of their shared information. Our method (in the form of the CompLearn Toolkit) is a fully automated software tool based on such a distance to compare two genomes. In evolutionary biology the timing and origin of the major extant placental clades (groups of organisms that have evolved from a common ancestor) continues to fuel debate and research.
The full experiment on mammalian evolution is discussed in Section ??. Here we just want to point out issues relevant for hierarchical clustering versus nonhierarchical clustering, and to our quartet tree method. We demonstrate that a whole mitochondrial genome phylogeny of the Eutherians (placental mammals) can be reconstructed automatically from a set of unaligned complete mitochondrial genomes by use of our compression method.
The whole mitochondrial genomes of the total of 24 species we used were downloaded from the GenBank Database on the worldwide web. Each is around 17,000 bases. The NCD distance matrix was computed using the compressor PPMZ. The resulting phylogeny, with an almost maximal S(T) score of 0.996 supports anew the currently accepted grouping (Rodents, (Primates, Ferungulates)) of the Eutherian orders, and additionally the Marsupionta hypothesis ((Prototheria, Metatheria), Eutheria), see Figure ??. The NCD distance matrix is given in Figure ??, so the reader can get a feeling on what distances the quartet tree is based. For more explanation and details see Section ??.
This is a good place to contrast the informativeness of hierarchical clustering with multidimensional clustering using the same NCD matrix, exhibited in Figure ??. The entries give a good example of typical NCD values; we truncated the number of decimals from 15 to 3 significant digits to save space. Note that the majority of distances bunches in the range [0.9,1]. This is due to the regularities the compressor can perceive. The diagonal elements give the selfdistance, which, for PPMZ, is not actually 0, but is off from 0 only in the third decimal. In Figure ?? we clustered the 24 animals using the NCD matrix by multidimenional scaling as points in 2dimensional Euclidean space. In this method, the NCD matrix of 24 animals can be viewed as a set of distances between points in ndimensional Euclidean space (n ≤ 24), which we want to project into a 2dimensional Euclidean space, trying to distort the distances between the pairs as little as possible. This is akin to the problem of projecting the surface of the earth globe on a twodimensional map with minimal distance distortion. The main feature is the choice of the measure of distortion to be minimized, []. Let the original set of distances be d_{1}, … , d_{k} and the projected distances be d′_{1}, … , d′_{k}. In Figure ?? we used the distortion measure Kruskall’s stress1, [], which minimizes √(∑_{i ≤ k} (d_{i} −d′_{i})^{2})/ ∑_{i ≤ k} d_{i}^{2}. Kruskall’s stress1 equal 0 means no distortion, and the worst value is at most 1 (unless you have a really bad projection). In the projection of the NCD matrix according to our quartet method one minimizes the more subtle distortion S(T) measure, where 1 means perfect representation of the relative relations between every 4tuple, and 0 means minimal representation. Therefore, we should compare distortion Kruskall stress1 with 1−S(T). Figure ?? has a very good 1−S(T)=0.04 and Figure ?? has a poor Kruskall stress 0.389. Assuming that the comparison is significant for small values (close to perfect projection), we find that the multidimensional scaling of this experiment’s NCD matrix is formally inferior to that of the quartet tree. This conclusion formally justifies the impression conveyed by the figures on visual inspection.
This chapter explores the concept of classification. In rough terms, classification refers to the placement of unknown test objects into one of several categories based on a training set of objects. It is different from the hierarchical clustering problem that has been the primary focus in this thesis up to this point, yet it is no less fundamental. Combining NCD with a trainable machine learning module yields wonderfully effective and often surprising results, showing that in certain situations, we may in essence learn by example with the help of human experts. In Section ?? below, classification is first addressed from a general perspective. In Section ??, it is shown how to combine NCD in combination with trainable classifiers based on socalled anchors. Section ?? discusses two options for such trainable classifiers: neural networks and support vector machines.
The classification problem setting as considered in this thesis is given as follows. A human expert has prepared n training examples. Each training example consists of a ddimensional input vector x and a target training label y. y must be chosen from a discrete set L of labels. A human expert supplies this information; the accuracy of the trained system would of course be limited by the accuracy of the labels in the training set. A training session computes a model M from the input or training data (x_{i},y_{i}) for 1 ≤ i ≤ n. The goal of a classification algorithm is to make good predictions based on the input training data. After M has been calculated by some classifier learning algorithm, henceforth called trainable classifier system, it is used to classify unknown test objects x_{test}, also of dimension d. It will output an element from L for each such object. Note that M can be thought of as a function that maps test input vectors x to labels in the set L. We refer to such a function as a classifier. Learning good classifiers is considered one of the most important problems in machine learning and pattern recognition in current times. One of the most important early successes has been in the field of optical character recognition, or OCR. This is the problem of taking a rasterized bitmap image and converting it to a sequence of ASCII characters according to some symbol recognition algorithm. Typical OCR software can work on as little as ten classes for applications such as numerical digit recognition, or many dozens of characters for scanning full text. They can operate in a highly specific way, for instance recognizing only one text font of a particular size, or they can be more generic, for instance learning handwritten letters and digits. In this situation, a typical setup would be to first use some preprocessing to try to split the large image into smaller images each containing just one symbol (or glyph). Then pad these boxes so that they are all squares of the same size and the glyph is roughly centered. Next each pixel may be read off in order and converted to a successive dimension in x = (x_{1}, …, x_{d}): pixel i corresponds to dimension x_{i}. For each pixel, a background color would be represented by 0 and a foreground color by 1. Thus each x input would be a binary vector with dimension equal to the number of pixels in the boxes surrounding each symbol. The output from such a classification system would be a single character from the range of possible characters under consideration. In this context, a learning algorithm would be given as input a training sequence ((x_{1}, y_{1}),…,(x_{n}, y_{n})), and then output a “learned” classifier M. This learned classifier M could then be used, for any new example x (pixel vector representing a character), to make a prediction of the corresponding class y (the actual character). The situation just described corresponds to a typical setup, however in the following experiments we take a somewhat different approach using NCD.
The simplest kind of classifier is called the binary classifier. This type of classifier can only produce two labels as output for each test case. The labels are usually written +1 and −1 or 0 and 1 depending on the problem. A common example of binary classification is the spamfiltering problem. This problem is to determine if a given email message is an unwanted commercial advertisement (spam) or not automatically before being brought to the attention of an email user. Another important kind of classifier is the multiclass classifier. This type of classifier applies more than two different types of labels. This is useful in cases like handwritten digit recognition, where there are at least ten different labels for handwritten digits that must sometimes be output.
It is usually simpler mathematically to consider only the binary classification case. This is justified by the fact that there are two well known ways to create a multiclass classifier using several binary classifiers acting cooperatively together. The two traditional ways of doing this are called oneofk style combination and pairwise combination. The oneofk style is simple to explain. A classifier is trained, one per class, for each of the k classes in the multiclass classification problem. Each classifier is trained to distinguish only members of its own class and reject (return 0) members of any other class.
In contrast, pairwise combination trains a separate binary classifier to distinguish each unique unordered pair of classes under consideration. Then a voting scheme is used to determine the winning class by running through all classifiers for each input sample. This results in O(k^{2}) classifiers for k classes.
The oneofk style combination yields the worst accuracy typically but is the simplest and fastest. The pairwise combination is usually the most accurate.
The simplest (and undoubtedly popular) way to use NCD to classify is to choose, for each of k classes, a single prototype object of the class that somehow captures the essence of the category. So, for example, if the task were to distinguish English and Chinese, we might consider using an English dictionary as the prototype object for the English class, and a Chinese dictionary for the Chinese class. In each case, we simplify a class down to a single example. Then the classification is done by calculating the NCD of the test object with each of the k prototype objects, and selecting the class corresponding to the object with the minimum NCD value. This approach seems intuitively obvious and is usually the first method people new to NCD invent. In some domains it works well, but in many more subtle classification problems it suffers a problem of uncorrectable bias. This relates to the fact that the different classes of most problems (such as the character classes in OCR) do not usually balance well under any particular available compressors. For example, the pixelated character class of the numeral “1” is sufficiently bland as to have a high NCD when compared to most other scribblings, even other members of the “1” class. This contrasts with the numeral “8” which has a rich combination of shapes that tends to compress well with most other outline images due to the wide variety of possible matches. This type of bias leads to a large constant error margin that cannot readily be improved within this framework as there are no natural adjustments available. In the next section, we explore a solution to this problem.
The simplest solution to the problem of how to use NCD for high accuracy classification is to combine it with a trainable classifier system by using NCD as a feature extraction technique. A trainable classifier system tries to pick up functional relationships in the input and output quantities. While trainable classifier systems output functions with a discrete range (the set of classes), some of the most successful ones, such as neural network– and SVM– based algorithms, are built on top of continuous learning algorithms. The continuous learners are a broad and important class of algorithms in machine learning. Given a training sequence (x_{1},y_{1}), …, (x_{n},y_{n}), they learn/output a continuous function M mapping ddimensional input vectors to the onedimensional reals. Such learners can be transformed into learning algorithms for binary classification, by classifying test vector x as 1 if M(x) > 0, and x as −1 if M(x) < 0.
In order to apply this idea, one must set up a problem so that unknown objects are somehow converted to fixeddimensional vectors using some sort of projection using NCD. One of the easiest techniques is to designate some d objects as anchors and to use them to convert all other objects in the experiment into ddimensional vectors. This can be achieved by using anchor object a_{i} to calculate vector dimension i for 1 ≤ i ≤ d. That is, for object o we calculate the corresponding x = (x_{1}, …, x_{d}) using x_{i} = NCD(o,a_{i}).
For example, in handwritten digit recognition, our training data may originally consist of pairs ((o_{1},y_{1}) …, (o_{n},y_{n})), where o_{i} represents an image (represented with one character per pixel, and one character as a line terminator) of a handwritten digit and y_{i} ∈ {0, …, 9 } is the corresponding digit. One then takes 80 images o_{1}′, …, o_{80}′, so that each handwritten digit is represented eight times. The training data (o_{1}, …, o_{n}) are then converted to (x_{1}, …, x_{n}), where each x_{i} is the 80dimensional vector (NCD(o_{i}, o_{1}′), …, NCD(o_{i},o_{80}′)).
The anchors may be chosen by a human expert or be chosen randomly from a large pool of training objects. Intuitively, it may seem very important which objects are chosen as anchors. While this is sometimes the case, more often it is not. Random anchors usually work well. But most practitioners agree that picking at least one anchor from each significant category under consideration is advisable to avoid falling into lowaccuracy performance due to insufficient variation in the anchor set. The anchor can be used with NCD. It can also be used with the Normalized Google Distance (NGD), which we discuss in Chapter ??. We will give details using NGD terms in Section ??, and details using Optical Character Recognition with NCD in Section ??.
In choosing a learning system to use with the anchor features extracted above, it seems best to choose as general a learning system as possible. One particularly important class of learning algorithms are called the universal continuous function learners. These include neural networks and Support Vector Machines. The universal learning property in this context means that they can approximate any continuous function to an arbitrarily high degree of accuracy given enough training data in the neighborhood of the point under consideration. Using a learner with this property ensures a certain degree of reliability and optimality to the results that it generates as compared to other more specialized learning algorithms.
There are at least two good choices for trainable learner components for use with NCD. These are called the neural network and the Support Vector Machine. Both of these techniques take as input the set of labeled training data as well as some specialized model parameters that must be set through some means. Usually the specialized parameters are set by a human expert or set through an automatic procedure using crossvalidation based parameter scanning [].
Both learner systems produce a single label out of the set L as a result given any input ddimensional test vector. Each have many strengths and weaknesses, and they each give unique performance profiles. They should both be considered when deciding to do classification using NCD, although all experiments in this thesis are based on support vector machines.
The older and considerably popular choice is artificial neural networks []. As mentioned previously, these have the advantage of being universal classifier systems: they have provable learning capability for any continuous function. Additionally, they have a host of decent training algorithms as well as learning modes. Popular choices here are Fast Backpropagation and Self Organizing Maps. SOM’s have not been designed explicitly for classification, but can easily adapted to be used for classification tasks when used in combination with compression metrics like NCD.
There is one major difficulty in all types of neural network investigated; they have some rather tricky parameter settings. A neural network of any sophistication must have a nonlinear component (transfer function) of some sort, and common choices are transcendental sigmoid functions like arctangent. There are several others in use. Another hard issue with many types of neural networks is to decide how many layers should be used. For each layer a specific number of neurons must be set in advance as well. Similarly, there is usually a learning rate and sometimes a momentum parameter that can radically affect the network’s gross behavior and overall success or failure. Altogether this implies at least four hard choices before one can train a neural network. There is no simple rule to guide how to choose these parameters because there are all sorts of bad behavior possible from wrong choices. The most common are overlearning and underlearning. Informally, overlearning occurs when there are too many neurons for the task at hand. Underlearning is when there are too few. In the case of overlearning, the neural network will appear to have great accuracy in the training data yet terrible accuracy in the testing phase and show almost no generalization capability. In the underlearning case, accuracy will simply be capped at a number far worse than what may otherwise be achieved. It’s quite difficult to tell what the best parameters are for a neural network and in real commercial systems there is usually a considerable amount of sophisticated semiautomatic machinery in place to assist the user in setting these parameters. For example, genetic algorithms are sometimes employed combined with crossvalidation to adjust parameters in a semiautomatic fashion. This heavy emphasis on parametersetting in advance makes neural networks a difficult component to integrate into an easytouse parameterfree learning system.
More recently another type of continuous universal learner has come into great popularity. They have the same sort of universal properties as artificial neural networks but with substantially less hassle []. They are called support vector machines. They take advantage of dot products in a high dimensional space to efficiently find solutions to a convex optimization problem that winds up bending a decision surface around training points in the least stressful way. In order to use an SVM, one needs to first choose a kernel function and this corresponds roughly to the transfer function in neural networks. Although a linear kernel is an option and polynomials are sometimes used, they do not yield the same (typically desirable) infinitedimensional properties that the exponential Radial Basis Function kernels do. These are described further below.
For all of our experiments we have found RBF kernels to be about as good as any other choice and often substantially better. There are only two other parameters for SVM’s. In our context, these are called C and g. C is the cost for wrong answers, or training points that fall on the wrong side of the decision boundary. Adjusting this can compensate for noisy or mislabeled training data. The g parameter represents kernel width and determines the rate of exponential decay around each of the training points. The very wonderful property of both of these parameters is that they can be determined through a simple two dimensional grid search.
This procedure was found to be much simpler to automate and far more robust than neural networks. Therefore, most of our experiments have focused on SVM as the trainable learning component. In the next section technical details are presented regarding SVM’s. It is important to realize that we still have not exhausted the possibilities for combining NCD with other machine learning algorithms; there are in fact many interesting options. For example, Gaussian processes could also serve as a good next phase learner but have not yet been investigated. Similarly, the previously mentioned quartettree search is another option at this same stage in the pipeline, as is multidimensional scaling, nearest neighbor search and most other classical machine learning algorithms. In this light we may consider NCD as a particularly convenient and sometimes highly intelligent featureextraction (or dimensionality reduction) technique suitable for dropin replacement in a number of larger automatic learning systems.
Many classification systems, including both of those mentioned above, are able to be used in a special mode called regression mode. In this mode, the output is not a member of the set of labels L but instead is a real (scalar) value, typically between 0 and 1 or between 1 and 1. This mode allows prediction of continuous variables such as temperature or duration. It is again a fundamental problem in machine learning and comes up often and provides yet more reason to use one of the two universal learners mentioned above with NCD.
This section provides a brief glimpse at relevant mathematical theory surrounding Support Vector Machines. For more information please see []. Support Vector Machines represent a way to learn a classification or regression problem by example, and are comparable to neural networks in that they have the capacity to learn any function. They are largemargin classifiers []. They take as input a list of kdimensional vectors, and output a single scalar value. In order to learn, an SVM solves a convex optimization problem that is closely related to a simpler classification engine termed the separating hyperplane. In this setting, we are given a set of kdimensional training vectors x_{i} each labeled y_{i} which is 1 or −1 according to the classification. For a particular problem, a discriminating hyperplane w is one that creates a decision function that satisfies the constraint for all i:
y_{i} (x_{i} · w + b) − 1 ≥ 0. 
If no such separating hyperplane exists, then we term the learning problem linearly inseparable. The constants used in this equation are more fully explained in []. In rough terms, the equation represents a linear programming problem that tries to find a simple (fewer support vectors chosen) and accurate (less disagreements with training data) model using a convex optimization technique.
Many realworld learning problems, such as the famous exclusiveor function, are linearly inseparable. This is problematic because a hyperplane can only separate spaces into linearly separable components. There are many strategies for dealing with this issue. Support vector machines use a nonlinear kernel function to address this issue. By creating a kernel function, k(x,y) that satisfies the Mercer condition, we may substantially enhance the power of the separating hyperplane. A kernel function defines an innerproduct on the input space, and then this inner product may be used to calculate many higherpower terms of combinations of samples in the input space. This forms a higherdimensional space, and it is wellknown that once this space is made large enough, there will be a separating hyperplane. In our SVM experiments, we use a Radial Basis Function (RBF) kernel. This allows the SVM to learn any function given enough training data.
There are two parameters that control the learning of the SVM. The first relates to the kernel function. An RBF, or Radial Basis Function, kernel assumes the value 1 whenever the two input vectors are equal. If they are unequal, it decays slowly towards 0 in a radially symmetric way:
K(x_{i}, x_{j}) = e^{−xi − xj 2/ 2 g2 } . 
Here, g is a parameter that controls the rate of decay or width of the kernel function. Because of the exponential form, the effective dimension of an RBF kernel is potentially infinite and thus this kernel can be used to approximate any continuous function to an arbitrary degree of accuracy. This parameter must be set before the learning can begin with an SVM. Another parameter relates to how misclassified points are handled in the training data; Though it is always possible to simply continue to make the kernel width smaller and the expanded space larger until the SVM becomes essentially a lookuptable, this is often not the best strategy for learning. An alternative is to define a cost parameter and allow this to adjust the tolerance for misclassified points in the training data. This allows the SVM to generalize well even in the presence of noisy data. This cost parameter, often called c, must also be defined before training can begin.
We select g and c using a grid searching technique. For each of these parameters, it is appropriate to search dozens of powers of two. Together, this creates a grid with hundreds of different parameter settings. We use fivefold crossvalidation to select which of these grid points defines the optimal parameter setting. First the data is divided into five random partitions: A, B, C, D, E. Then, for each candidate parameter setting or grid point, we run five different training runs. On the first run, we train on B, C, D, and E, and then we determine an accuracy using part A. Next, we train on A, C, D, E and test with B. We continue in this way and then average all five test scores to arrive at an estimate of how well the learning process performed. Once this process is done for all training data, we may just choose one of the grid points that attains a maximum accuracy.
These parameters (C and g) do not usually need to be exact; instead one can simply do a stepwise search along a grid of points in log space; thus it is fine to try just 64 points for C ranging from 2^{−31} to 2^{32} doubling each time. A similar procedure works to choose g. In each case one may use fivefold crossvalidation to estimate the accuracy of the given parameter setting and then just choose the maximum at the end. Fivefold cross validation is a popular procedure that tries to estimate how well a model (of any type) that is trained on a set of training data will perform on unknown testing data. The basic assumption is simply that the training data and the testing data are similar. In order to estimate the testing accuracy using only the training algorithm and the training data, first label the training set randomly with five labels, numbered 1 through 5. Next, train a model (SVM, neural network, or otherwise) on four of the five parts, but leave part 1 out. Use this part 1 for testing the model made from the other 4 parts. Tally this score and repeat the procedure, but this time withhold part 2 for testing. Repeat this procedure five times, once for each part, and then average the accuracy scores to arrive at an estimate of the testing accuracy to be expected for a given set of parameters. This entire fivefold crossvalidation procedure is repeated for each point (particular parameter setting) in the parameter grid space in order to find the optimal settings using just the training data.
This chapter demonstrates the surprisingly general and robust nature of the methods so far discussed through many real examples from diverse areas such as music and evolution. The combination of NCD and the quartet method yield interesting results.
In Section ??, we introduce the general concept of featurebased similarity, and explain how NCD can be used with it. In Section ?? we present experimental validation that our method works. Starting in Section ??, we introduce a plethora of experiments in a wide array of fields, beginning with automatic music analysis. Next, we study evolutionary genomics, literature and language analysis, radio astronomy, and optical character recognition. At the end of this chapter the reader will have encountered a highly serviceable survey of experimental results using objective data compressors based on files, without external information input from the internet.
We are presented with unknown data and the question is to determine the similarities among them and group like with like together. Commonly, the data are of a certain type: music files, transaction records of ATM machines, credit card applications, genomic data. In these data there are hidden relations that we would like to get out in the open. For example, from genomic data one can extract letter or block frequencies (the blocks are over the fourletter alphabet); from music files one can extract various specific numerical features, related to pitch, rhythm, harmony etc. One can extract such features using for instance Fourier transforms [] or wavelet transforms []. The feature vectors corresponding to the various files are then classified or clustered using existing classification software, based on various standard statistical pattern recognition classifiers [], Bayesian classifiers [], hidden Markov models [], ensembles of nearest neighbor classifiers [] or neural networks [, ]. For example, in music one feature would be to look for rhythm in the sense of beats per minute. One can make a histogram where each histogram bin corresponds to a particular tempo in beatsperminute and the associated peak shows how frequent and strong that particular periodicity was over the entire piece. In [] we see a gradual change from a few high peaks to many low and spreadout ones going from hiphip, rock, jazz, to classical. One can use this similarity type to try to cluster pieces in these categories. However, such a method requires specific and detailed knowledge of the problem area, since one needs to know what features to look for.
NonFeature Similarities: Our aim is to capture, in a single similarity metric, every effective metric: effective versions of Hamming distance, Euclidean distance, edit distances, alignment distance, LempelZiv distance [], and so on. This metric should be so general that it works in every domain: music, text, literature, programs, genomes, executables, natural language determination, equally and simultaneously. It would be able to simultaneously detect all similarities between pieces that other effective metrics can detect.
Compressionbased Similarity: Such a “universal” metric was codeveloped by us in [, , ], as a normalized version of the “information metric” of [, ], see Chapter 3. Recall that two objects are deemed close if we can significantly “compress” one given the information in the other, the idea being that if two pieces are more similar, then we can more succinctly describe one given the other. Recall from Chapter 3 that an appropriately “normalized” information distance minorizes every metric in the class of effective similarity metrics. It discovers all effective similarities in the sense that if two objects are close according to some effective similarity, then they are also close according to the normalized information distance. Put differently, the normalized information distance represents similarity according to the dominating shared feature between the two objects being compared. The normalized information distance too is a metric and takes values in [0,1]; hence it may be called “the” similarity metric. To apply this ideal precise mathematical theory in real life, we have to replace the use of the uncomputable Kolmogorov complexity by an approximation using a standard realworld compressor. Approaches predating this thesis include the first completely automatic construction of the phylogeny tree based on whole mitochondrial genomes, [, , ], a completely automatic construction of a language tree for over 50 EuroAsian languages [], detects plagiarism in student programming assignments [], gives phylogeny of chain letters [], and clusters music [, ]. Moreover, the method turns out to be robust under change of the underlying compressortypes: statistical (PPMZ), LempelZiv based dictionary (gzip), block based (bzip2), or special purpose (Gencompress).
Related Work: In view of the simplicity and naturalness of our proposal, it is perhaps surprising that compression based clustering and classification approaches did not arise before. But recently there have been several partially independent proposals in that direction: [, ] for building language trees—while citing [, ]—is by essentially more ad hoc arguments about empirical Shannon entropy and KullbackLeibler distance. This approach is used to cluster music MIDI files by Kohonen maps in []. Another recent offshoot based on our work is [] hierarchical clustering based on mutual information. In a related, but considerably simpler feature based approach, one can compare the word frequencies in text files to assess similarity. In [] the word frequencies of words common to a pair of text files are used as entries in two vectors, and the similarity of the two files is based on the distance between those vectors. The authors attribute authorship to Shakespeare plays, the Federalist Papers, and the Chinese classic “The Dream of the Red Chamber.” The approach to similarity distances based on block occurrence statistics is standard in genomics, and in an experiment below it gives inferior phylogeny trees compared to our compression method (and wrong ones according to current biological wisdom). The possibly new feature in the cited work is that it uses statistics of only the words that the files being compared have in common. A related, opposite, approach was taken in [], where literary texts are clustered by author gender or fact versus fiction, essentially by first identifying distinguishing features, like gender dependent word usage, and then classifying according to those features.
Apart from the experiments reported here, the clustering by compression method reported in this paper has recently been used to analyze network traffic and cluster computer worms and viruses []. Finally, recent work [] reports experiments with our method on all time sequence data used in all the major datamining conferences in the last decade. Comparing the compression method with all major methods used in those conferences they established clear superiority of the compression method for clustering heterogenous data, and for anomaly detection.
To substantiate our claim of universality, we apply the method to different areas, not using any feature analysis at all. We first give an example in wholegenome phylogeny using the whole mitochondrial DNA of the species concerned. We compare the hierarchical clustering of our method with a more standard method of twodimensional clustering (to show that our dendrogram method of depicting the clusters is more informative). We give a wholegenome phylogeny of fungi and compare this to results using alignment of selected proteins (alignment being often too costly to perform on the wholemitochondrial genome, but the disadvantage of protein selection being that different selections usually result in different phylogenies—so which is right?). We identify the virii that are closest to the sequenced SARS virus; we give an example of clustering of language families; Russian authors in the original Russian, the same pieces in English translation (clustering partially follows the translators); clustering of music in MIDI format; clustering of handwritten digits used for optical character recognition; and clustering of radio observations of a mysterious astronomical object, a microquasar of extremely complex variability. In all these cases the method performs very well in the following sense: The method yields the phylogeny of 24 species precisely according to biological wisdom. The probability that it randomly would hit this one outcome, or anything reasonably close, is very small. In clustering 36 music pieces taken equally many from pop, jazz, classic, so that 121212 is the grouping we understand is correct, we can identify convex clusters so that only six errors are made. (That is, if three items get dislodged then six items get misplaced.) The probability that this happens by chance is extremely small. The reason why we think the method does something remarkable is concisely put by Laplace []:
“If we seek a cause wherever we perceive symmetry, it is not that we regard the symmetrical event as less possible than the others, but, since this event ought to be the effect of a regular cause or that of chance, the first of these suppositions is more probable than the second. On a table we see letters arranged in this order C o n s t a n t i n o p l e, and we judge that this arrangement is not the result of chance, not because it is less possible than others, for if this word were not employed in any language we would not suspect it came from any particular cause, but this word being in use among us, it is incomparably more probable that some person has thus arranged the aforesaid letters than that this arrangement is due to chance.”
Materials and Methods: The data samples we used were obtained from standard data bases accessible on the worldwide web, generated by ourselves, or obtained from research groups in the field of investigation. We supply the details with each experiment. The method of processing the data was the same in all experiments. First, we preprocessed the data samples to bring them in appropriate format: the genomic material over the fourletter alphabet {A,T,G,C} is recoded in a fourletter alphabet; the music MIDI files are stripped of identifying information such as composer and name of the music piece. Then, in all cases the data samples were completely automatically processed by our CompLearn Toolkit, rather than as is usual in phylogeny, by using an eclectic set of software tools per experiment. Oblivious to the problem area concerned, simply using the distances according to the NCD below, the method described in this thesis fully automatically classifies the objects concerned. The CompLearn Toolkit is a suite of simple utilities that one can use to apply compression techniques to the process of discovering and learning patterns in completely different domains. In fact, this method is so general that it requires no background knowledge about any particular subject area. There are no domainspecific parameters to set, and only a handful of general settings.
The CompLearn Toolkit using NCD and not, say, alignment, can cope with full genomes and other large data files and thus comes up with a single distance matrix. The clustering heuristic generates a tree with a certain confidence, called standardized benefit score or S(T) value in the sequel. Generating trees from the same distance matrix many times resulted in the same tree or almost the same tree, for all distance matrices we used, even though the heuristic is randomized. The differences that arose are apparently due to early or late termination with different S(T) values. This is a great difference with previous phylogeny methods, where because of computational limitations one uses only parts of the genome, or certain proteins that are viewed as significant []. These are run through a tree reconstruction method like neighbor joining [], maximum likelihood, maximum evolution, maximum parsimony as in [], or quartet hypercleaning [], many times. The percentagewise agreement on certain branches arising are called “bootstrap values.” Trees are depicted with the best bootstrap values on the branches that are viewed as supporting the theory tested. Different choices of proteins result in different best trees. One way to avoid this ambiguity is to use the full genome, [, ], leading to wholegenome phylogeny. With our method we do wholegenome phylogeny, and end up with a single overall best tree, not optimizing selected parts of it.
The quality of the results depends on (a) the NCD distance matrix, and (b) how well the hierarchical tree represents the information in the matrix. The quality of (b) is measured by the S(T) value, and is given with each experiment. In general, the S(T) value deteriorates for large sets. We believe this to be partially an artifact of a lowresolution NCD matrix due to limited compression power, and limited file size. The main reason, however, is the fact that with increasing size of a natural data set the projection of the information in the NCD matrix into a binary tree can get increasingly distorted as explained in Chapter 5, page ??. Another aspect limiting the quality of the NCD matrix is more subtle. Recall that the method knows nothing about any of the areas we apply it to. It determines the dominant feature as seen through the NCD filter. The dominant feature of likeness between two files may not correspond to our a priori conception but may have an unexpected cause. The results of our experiments suggest that this is not often the case: In the natural data sets where we have preconceptions of the outcome, for example that works by the same authors should cluster together, or music pieces by the same composers, musical genres, or genomes, the outcomes conform largely to our expectations. For example, in the music genre experiment the method would fail dramatically if genres were evenly mixed, or mixed with little bias. However, to the contrary, the separation in clusters is almost perfect. The few misplacements that are discernible are either errors (the method was not powerful enough to discern the dominant feature), or the dominant feature between a pair of music pieces is not the genre but some other aspect. The surprising news is that we can generally confirm expectations with few misplacements, indeed, that the data does not contain unknown rogue features that dominate to cause spurious (in our preconceived idea) clustering. This gives evidence that where the preconception is in doubt, like with phylogeny hypotheses, the clustering can give true support of one hypothesis against another one.
Figures: We use two styles to display the hierarchical clusters. In the case of genomics of Eutherian orders and fungi, language trees, it is convenient to follow the dendrograms that are customary in that area (suggesting temporal evolution) for easy comparison with the literature. Although there is no temporal relation intended, the dendrogram representation looked also appropriate for the Russian writers, and translations of Russian writers. In the other experiments (even the genomic SARS experiment) it is more informative to display an unrooted ternary tree (or binary tree if we think about incoming and outgoing edges) with explicit internal nodes. This facilitates identification of clusters in terms of subtrees rooted at internal nodes or contiguous sets of subtrees rooted at branches of internal nodes.
Testing the similarity machine on natural data: We test gross classification of files based on markedly different file types. Here, we chose several files: (i) Four mitochondrial gene sequences, from a black bear, polar bear, fox, and rat obtained from the GenBank Database on the worldwide web; (ii) Four excerpts from the novel The Zeppelin’s Passenger by E. Phillips Oppenheim, obtained from the Project Gutenberg Edition on the World Wide web; (iii) Four MIDI files without further processing; two from Jimi Hendrix and two movements from Debussy’s Suite Bergamasque, downloaded from various repositories on the worldwide web; (iv) Two Linux x86 ELF executables (the cp and rm commands), copied directly from the RedHat 9.0 Linux distribution; and (v) Two compiled Java class files, generated by ourselves. The compressor used to compute the NCD matrix was bzip2. As expected, the program correctly classifies each of the different types of files together with like near like. The result is reported in Figure ?? with S(T) equal to the very high confidence value 0.984. This experiment shows the power and universality of the method: no features of any specific domain of application are used.
We developed the CompLearn Toolkit, and performed experiments in vastly different application fields to test the quality and universality of the method. The success of the method as reported below depends strongly on the judicious use of encoding of the objects compared. Here one should use common sense on what a real world compressor can do. There are situations where our approach fails if applied in a straightforward way. For example: comparing text files by the same authors in different encodings (say, Unicode and 8bit version) is bound to fail. For the ideal similarity metric based on Kolmogorov complexity as defined in [] this does not matter at all, but for practical compressors used in the experiments it will be fatal. Similarly, in the music experiments below we use symbolic MIDI music file format rather than wave format music files. The reason is that the strings resulting from straightforward discretizing the wave form files may be too sensitive to how we discretize.
We show the method is truly featurefree, or, anyway, as featurefree as we could possibly want, by showing once more its success in clustering data from truly different domains. No other method can apparently do so with so much success, since all other methods rfely on some definite features they analyze. In contrast, we just compress. Onr may say that the used compressor determines the features analyzed, but this seems illtergeted at generalpurpose compressors which simply aim at analyzing general features as well as is possible. We test gross classification of files based on markedly different file types, as on page ??, recalling Figure ?? displayed there.
As expected, the program correctly classifies each of the different types of files together with like near like. The result is reported in Figure ?? with S(T) equal to 0.984. Recall that S is defined as a linear normalized and inverted tree cost score as previously explained in Chapter 5, page ??. This means that this tree is very near an optimal best tree.
The first result found relates to music analysis using gzip or bzip2 with preprocessed MIDI files. Surprisingly, the computer was able to reconstruct some common musical notions without any training whatsoever using just compression and quartet tree search. Here’s how:
All musical pieces are similar, but some are more similar than others. Apart from being an infinite source of discussion (“Haydn is just like Mozart — no, he’s not!”), such similarities are also crucial for the design of efficient music information retrieval systems. The amount of digitized music available on the internet has grown dramatically in recent years, both in the public domain and on commercial sites. Napster and its clones are prime examples. Websites offering musical content in some form or other (MP3, MIDI, …) need a way to organize their wealth of material; they need to somehow classify their files according to musical genres and subgenres, putting similar pieces together. The purpose of such organization is to enable users to navigate to pieces of music they already know and like, but also to give them advice and recommendations (“If you like this, you might also like…”). Currently, such organization is mostly done manually by humans, but some recent research has been looking into the possibilities of automating music classification.
A human expert, comparing different pieces of music with the aim to cluster likes together, will generally look for certain specific similarities. Previous attempts to automate this process do the same. Generally speaking, they take a file containing a piece of music and extract from it various specific numerical features, related to pitch, rhythm, harmony etc. One can extract such features using for instance Fourier transforms [] or wavelet transforms []. The feature vectors corresponding to the various files are then classified or clustered using existing classification software, based on various standard statistical pattern recognition classifiers [], Bayesian classifiers [], hidden Markov models [], ensembles of nearestneighbor classifiers [] or neural networks [, ]. For example, one feature would be to look for rhythm in the sense of beats per minute. One can make a histogram where each histogram bin corresponds to a particular tempo in beatsperminute and the associated peak shows how frequent and strong that particular periodicity was over the entire piece. In [] we see a gradual change from a few high peaks to many low and spreadout ones going from hiphip, rock, jazz, to classical. One can use this similarity type to try to cluster pieces in these categories. However, such a method requires specific and detailed knowledge of the problem area, since one needs to know what features to look for.
Our aim is much more general. We do not look for similarity in specific features known to be relevant for classifying music; instead we apply a general mathematical theory of similarity. The aim is to capture, in a single similarity metric, every effective metric: effective versions of Hamming distance, Euclidean distance, edit distances, LempelZiv distance, and so on. Such a metric would be able to simultaneously detect all similarities between pieces that other effective metrics can detect. As we have seen in Chapter 3, such a “universal” metric indeed exists. It is the NID metric which is approximated by the NCD metric.
In this section we apply this compressionbased method to the classification of pieces of music. We perform various experiments on sets of mostly classical pieces given as MIDI (Musical Instrument Digital Interface) files. This contrasts with most earlier research, where the music was digitized in some wave format or other (the only other research based on MIDI that we are aware of is []). We compute the distances between all pairs of pieces, and then build a tree containing those pieces in a way that is consistent with those distances. First, we show that our program can distinguish between various musical genres (classical, jazz, rock) quite well. Secondly, we experiment with various sets of classical pieces. The results are quite good (in the sense of conforming to our expectations) for small sets of data, but tend to get a bit worse for large sets. Considering the fact that the method knows nothing about music, or, indeed, about any of the other areas we have applied it to elsewhere, one is reminded of Dr Johnson’s remark about a dog’s walking on his hind legs: “It is not done well; but you are surprised to find it done at all.”
Initially, we downloaded 118 separate MIDI (Musical Instrument Digital Interface, a versatile digital music format available on the worldwideweb) files selected from a range of classical composers, as well as some popular music. Each of these files was run through a preprocessor to extract just MIDI NoteOn and NoteOff events. These events were then converted to a playerpiano style representation, with time quantized in 0.05 second intervals. All instrument indicators, MIDI Control signals, and tempo variations were ignored. For each track in the MIDI file, we calculate two quantities: An average volume and a modal note. The average volume is calculated by averaging the volume (MIDI Note velocity) of all notes in the track. The modal note is defined to be the note pitch that sounds most often in that track. If this is not unique, then the lowest such note is chosen. The modal note is used as a keyinvariant reference point from which to represent all notes. It is denoted by 0, higher notes are denoted by positive numbers, and lower notes are denoted by negative numbers. A value of 1 indicates a halfstep above the modal note, and a value of −2 indicates a wholestep below the modal note. The tracks are sorted according to decreasing average volume, and then output in succession. For each track, we iterate through each time sample in order, outputting a single signed 8bit value for each currently sounding note. Two special values are reserved to represent the end of a time step and the end of a track. This file is then used as input to the compression stage for distance matrix calculation and subsequent tree search.
Because we have already shown examples of the accuracy of the quartet tree reconstruction on artificial and controlled data, we will proceed immediately to natural data of considerably more interest than that already shown in earlier chapters.
Before testing whether our program can see the distinctions between various classical composers, we first show that it can distinguish between three broader musical genres: classical music, rock, and jazz. This should be easier than making distinctions “within” classical music. All musical pieces we used are listed in the tables in the appendix. For the genreexperiment we used 12 classical pieces (the small set from Table ??, consisting of Bach, Chopin, and Debussy), 12 jazz pieces (Table ??), and 12 rock pieces (Table ??). The tree that our program came up with is given in Figure ??. The S(T) score is 0.858.
The discrimination between the 3 genres is good but not perfect. The upper branch of the tree contains 10 of the 12 jazz pieces, but also Chopin’s Prélude no. 15 and a Bach Prelude. The two other jazz pieces, Miles Davis’ “So what” and John Coltrane’s “Giant steps” are placed elsewhere in the tree, perhaps according to some kinship that now escapes us but can be identified by closer studying of the objects concerned. Of the rock pieces, 9 are placed close together in the rightmost branch, while Hendrix’s “Voodoo chile”, Rush’ “Yyz”, and Dire Straits’ “Money for nothing” are further away. In the case of the Hendrix piece this may be explained by the fact that it does not fit well in a specific genre. Most of the classical pieces are in the lower left part of the tree. Surprisingly, 2 of the 4 Bach pieces are placed elsewhere. It is not clear why this happens and may be considered an error of our program, since we perceive the 4 Bach pieces to be very close, both structurally and melodically (as they all come from the monothematic “Wohltemperierte Klavier”). However, Bach’s is a seminal music and has been copied and cannibalized in all kinds of recognizable or hidden manners; closer scrutiny could reveal likenesses in its present company that are not now apparent to us. In effect our similarity engine aims at the ideal of a perfect data mining process, discovering unknown features in which the data can be similar.
In Table ?? we list all 60 classical piano pieces used, together with their abbreviations. Some of these are complete compositions, others are individual movements from larger compositions. They all are piano pieces, but experiments on 34 movements of symphonies gave very similar results (Section ??). Apart from running our program on the whole set of 60 piano pieces, we also tried it on two smaller sets: a small 12piece set, indicated by ‘(s)’ in the table, and a mediumsize 32piece set, indicated by ‘(s)’ or ‘(m)’.
The small set encompasses the 4 movements from Debussy’s Suite bergamasque, 4 movements of book 2 of Bach’s Wohltemperierte Klavier, and 4 preludes from Chopin’s opus 28. As one can see in Figure ??, our program does a pretty good job at clustering these pieces. The S(T) score is also high: 0.958. The 4 Debussy movements form one cluster, as do the 4 Bach pieces. The only imperfection in the tree, judged by what one would intuitively expect, is that Chopin’s Prélude no. 15 lies a bit closer to Bach than to the other 3 Chopin pieces. This Prélude no 15, in fact, consistently forms an oddoneout in our other experiments as well. This is an example of pure data mining, since there is some musical truth to this, as no. 15 is perceived as by far the most eccentric among the 24 Préludes of Chopin’s opus 28.
The medium set adds 20 pieces to the small set: 6 additional Bach pieces, 6 additional Chopins, 1 more Debussy piece, and 7 pieces by Haydn. The experimental results are given in Figure ??. The S(T) score is slightly lower than in the small set experiment: 0.895. Again, there is a lot of structure and expected clustering. Most of the Bach pieces are together, as are the four Debussy pieces from the Suite bergamasque. These four should be together because they are movements from the same piece; The fifth Debussy item is somewhat apart since it comes from another piece. Both the Haydn and the Chopin pieces are clustered in little subclusters of two or three pieces, but those subclusters are scattered throughout the tree instead of being close together in a larger cluster. These small clusters may be an imperfection of the method, or, alternatively point at musical similarities between the clustered pieces that transcend the similarities induced by the same composer. Indeed, this may point the way for further musicological investigation.
Figure ?? gives the output of a run of our program on the full set of 60 pieces. This adds 10 pieces by Beethoven, 8 by Buxtehude, and 10 by Mozart to the medium set. The experimental results are given in Figure ??. The results are still far from random, but leave more to be desired than the smallerscale experiments. Indeed, the S(T) score has dropped further from that of the mediumsized set to 0.844. This may be an artifact of the interplay between the relatively small size, and large number, of the files compared: (i) the distances estimated are less accurate; (ii) the number of quartets with conflicting requirements increases; and (iii) the computation time rises to such an extent that the correctness score of the displayed cluster graph within the set time limit is lower than in the smaller samples. Nonetheless, Bach and Debussy are still reasonably well clustered, but other pieces (notably the Beethoven and Chopin ones) are scattered throughout the tree. Maybe this means that individual music pieces by these composers are more similar to pieces of other composers than they are to each other? The placement of the pieces is closer to intuition on a small level (for example, most pairing of siblings corresponds to musical similarity in the sense of the same composer) than on the larger level. This is similar to the phenomenon of little subclusters of Haydn or Chopin pieces that we saw in the mediumsize experiment.
Finally, we tested whether the method worked for more complicated music, namely 34 symphonic pieces. We took two Haydn symphonies (no. 95 in one file, and the four movements of 104), three Mozart symphonies (39, 40, 41), three Beethoven symphonies (3, 4, 5), of Schubert’s Unfinished symphony, and of SaintSaens Symphony no. 3. The results are reported in Figure ??, with a quite reasonable S(T) score of 0.860.
Our research raises many questions worth looking into further:
Composer Piece Acronym J.S. Bach (10) Wohltemperierte Klavier II: Preludes and fugues 1,2 BachWTK2{F,P}{1,2} (s) Goldberg Variations: Aria, Variations 1,2 BachGold{Aria,V1,V2} (m) Kunst der Fuge: Variations 1,2 BachKdF{1,2} (m) Invention 1 BachInven1 (m) Beethoven (10) Sonata no. 8 (Pathetique), 1st movement BeetSon8m1 Sonata no. 14 (Mondschein), 3 movements BeetSon14m{1,2,3} Sonata no. 21 (Waldstein), 2nd movement BeetSon21m2 Sonata no. 23 (Appassionata) BeetSon23 Sonata no. 26 (Les Adieux) BeetSon26 Sonata no. 29 (Hammerklavier) BeetSon29 Romance no. 1 BeetRomance1 Für Elise BeetFurElise Buxtehude (8) Prelude and fugues, BuxWV 139,143,144,163 BuxtPF{139,143,144,163} Toccata and fugue, BuxWV 165 BuxtTF165 Fugue, BuxWV 174 BuxtFug174 Passacaglia, BuxWV 161 BuxtPassa161 Canzonetta, BuxWV 168 BuxtCanz168 Chopin (10) Préludes op. 28: 1, 15, 22, 24 ChopPrel{1,15,22,24} (s) Etudes op. 10, nos. 1, 2, and 3 ChopEtu{1,2,3} (m) Nocturnes nos. 1 and 2 ChopNoct{1,2} (m) Sonata no. 2, 3rd movement ChopSon2m3 (m) Debussy (5) Suite bergamasque, 4 movements DebusBerg{1,2,3,4} (s) Children’s corner suite (Gradus ad Parnassum) DebusChCorm1 (m) Haydn (7) Sonatas nos. 27, 28, 37, and 38 HaydnSon{27,28,37,38} (m) Sonata no. 40, movements 1,2 HaydnSon40m{1,2} (m) Andante and variations HaydnAndaVari (m) Mozart (10) Sonatas nos. 1,2,3,4,6,19 MozSon{1,2,3,4,6,19} Rondo from Sonata no. 16 MozSon16Rondo Fantasias K397, 475 MozFantK{397,475} Variations “Ah, vous diraisje madam” MozVarsDirais
John Coltrane Blue trane Giant steps Lazy bird Impressions Miles Davis Milestones Seven steps to heaven Solar So what George Gershwin Summertime Dizzy Gillespie Night in Tunisia Thelonious Monk Round midnight Charlie Parker Yardbird suite
The Beatles Eleanor Rigby Michelle Eric Clapton Cocaine Layla Dire Straits Money for nothing Led Zeppelin Stairway to heaven Metallica One Jimi Hendrix Hey Joe Voodoo chile The Police Every breath you take Message in a bottle Rush Yyz
In recent years, as the complete genomes of various species become available, it has become possible to do whole genome phylogeny (this overcomes the problem that using different targeted parts of the genome, or proteins, may give different trees []). Traditional phylogenetic methods on individual genes depended on multiple alignment of the related proteins and on the model of evolution of individual amino acids. Neither of these is practically applicable to the genome level. In absence of such models, a method which can compute the shared information between two sequences is useful because biological sequences encode information, and the occurrence of evolutionary events (such as insertions, deletions, point mutations, rearrangements, and inversions) separating two sequences sharing a common ancestor will result in the loss of their shared information. Our method (in the form of the CompLearn Toolkit) is a fully automated software tool based on such a distance to compare two genomes.
In evolutionary biology the timing and origin of the major extant placental clades (groups of organisms that have evolved from a common ancestor) continues to fuel debate and research. Here, we provide evidence by whole mitochondrial genome phylogeny for competing hypotheses in two main questions: the grouping of the Eutherian orders, and the Therian hypothesis versus the Marsupionta hypothesis.
We demonstrate (already in []) that a whole mitochondrial genome phylogeny of the Eutherians (placental mammals) can be reconstructed automatically from a set of unaligned complete mitochondrial genomes by use of an early form of our compression method, using standard software packages. As more genomic material has become available, the debate in biology has intensified concerning which two of the three main groups of placental mammals are more closely related: Primates, Ferungulates, and Rodents. In [], the maximum likelihood method of phylogeny tree reconstruction gave evidence for the (Ferungulates, (Primates, Rodents)) grouping for half of the proteins in the mitochondrial genomes investigated, and (Rodents, (Ferungulates, Primates)) for the other halves of the mt genomes. In that experiment they aligned 12 concatenated mitochondrial proteins, taken from 20 species: the humble rat (Rattus norvegicus), house mouse (Mus musculus), grey seal (Halichoerus grypus), harbor seal (Phoca vitulina), cat (Felis catus), white rhino (Ceratotherium simum), horse (Equus caballus), finback whale (Balaenoptera physalus), blue whale (Balaenoptera musculus), cow (Bos taurus), gibbon (Hylobates lar), gorilla (Gorilla gorilla), human (Homo sapiens), chimpanzee (Pan troglodytes), pygmy chimpanzee (Pan paniscus), orangutan (Pongo pygmaeus), Sumatran orangutan (Pongo pygmaeus abelii), using opossum (Didelphis virginiana), wallaroo (Macropus robustus), and the platypus (Ornithorhynchus anatinus) as outgroup. In [, ] we used the whole mitochondrial genome of the same 20 species, computing the NCD distances (or a closely related distance in []), using the GenCompress compressor, followed by tree reconstruction using the neighbor joining program in the MOLPHY package [] to confirm the commonly believed morphologysupported hypothesis (Rodents, (Primates, Ferungulates)). Repeating the experiment using the hypercleaning method [] of phylogeny tree reconstruction gave the same result. Here, we repeated this experiment several times using the CompLearn Toolkit using our new quartet method for reconstructing trees, and computing the NCD with various compressors (gzip, bzip2, PPMZ), again always with the same result. These experiments are not reported since they are subsumed by the larger experiment of Figure ??.
The extant monophyletic divisions of the class Mammalia are the Prototheria (monotremes: mammals that procreate using eggs), Metatheria (marsupials: mammals that procreate using pouches), and Eutheria (placental mammals: mammals that procreate using placentas). The sister relationships between these groups is viewed as the most fundamental question in mammalian evolution []. Phylogenetic comparison by either anatomy or mitochondrial genome has resulted in two conflicting hypotheses: the geneisolationsupported Marsupionta hypothesis: ((Prototheria, Metatheria), Eutheria) versus the morphologysupported Theria hypothesis: (Prototheria, (Metatheria, Eutheria)), the third possiblity apparently not being held seriously by anyone. There has been a lot of support for either hypothesis; recent support for the Theria hypothesis was given in [] by analyzing a large nuclear gene (M6P/IG2R), viewed as important across the species concerned, and even more recent support for the Marsupionta hypothesis was given in [] by phylogenetic analysis of another sequence from the nuclear gene (18S rRNA) and by the whole mitochondrial genome.
To test the Eutherian orders simultaneously with the Marsupionta versus Theria hypothesis, we added four animals to the above twenty: Australian echidna (Tachyglossus aculeatus), brown bear (Ursus arctos), polar bear (Ursus maritimus), using the common carp (Cyprinus carpio) as the outgroup. Interestingly, while there are many species of Eutheria and Metatheria, there are only three species of now living Prototheria known: platypus, and two types of echidna (or spiny anteater). So our sample of the Prototheria is large. The addition of the new species might be risky in that the addition of new relations is known to distort the previous phylogeny in traditional computational genomics practice. With our method, using the full genome and obtaining a single tree with a very high confidence S(T) value, that risk is not as great as in traditional methods obtaining ambiguous trees with bootstrap (statistic support) values on the edges. The mitochondrial genomes of the total of 24 species we used were downloaded from the GenBank Database on the worldwide web. Each is around 17,000 bases. The NCD distance matrix was computed using the compressor PPMZ. The resulting phylogeny, with an almost maximal S(T) score of 0.996 supports anew the currently accepted grouping (Rodents, (Primates, Ferungulates)) of the Eutherian orders, and additionally the Marsupionta hypothesis ((Prototheria, Metatheria), Eutheria), see Figure ?? (reproducing Figure ?? for the readers convenience). Overall, our wholemitochondrial NCD analysis supports the following hypothesis:
 , 
which indicates that the rodents, and the branch leading to the Metatheria and Prototheria, split off early from the branch that led to the primates and ferungulates. Inspection of the distance matrix Figure ?? on page ?? (exhibited earler in the context of hierarchical versus flat clustering) shows that the primates are very close together, as are the rodents, the Metatheria, and the Prototheria. These are tightlyknit groups with relatively close NCD’s. The ferungulates are a much looser group with generally distant NCD’s. The intergroup distances show that the Prototheria are furthest away from the other groups, followed by the Metatheria and the rodents. Also the finestructure of the tree is consistent with biological wisdom.
In another experiment we clustered the SARS virus after its sequenced genome was made publicly available, in relation to potential similar virii. The 15 virus genomes were downloaded from The Universal Virus Database of the International Committee on Taxonomy of Viruses, available on the worldwide web. The SARS virus was downloaded from Canada’s Michael Smith Genome Sciences Centre which had the first public SARS Coronavirus draft whole genome assembly available for download (SARS TOR2 draft genome assembly 120403). The NCD distance matrix was computed using the compressor bzip2. The relations in Figure ?? are very similar to the definitive tree based on medicalmacrobiogenomics analysis, appearing later in the New England Journal of Medicine, []. We depicted the figure in the ternary tree style, rather than the genomicsdendrogram style, since the former is more precise for visual inspection of proximity relations.
As a pilot for applications of the CompLearn Toolkit in fungi genomics research, the group of T. Boekhout, E. Kuramae, V. Robert, of the Fungal Biodiversity Center, Royal Netherlands Academy of Sciences, supplied us with the mitochondrial genomes of Candida glabrata, Pichia canadensis, Saccharomyces cerevisiae, S. castellii, S. servazzii, Yarrowia lipolytica (all yeasts), and two filamentous ascomycetes Hypocrea jecorina and Verticillium lecanii. The NCD distance matrix was computed using the compressor PPMZ. The resulting tree is depicted in Figure ??. The interpretation of the fungi researchers is “the tree clearly clustered the ascomycetous yeasts versus the two filamentous Ascomycetes, thus supporting the current hypothesis on their classification (for example, see []). Interestingly, in a recent treatment of the Saccharomycetaceae, S. servazii, S. castellii and C. glabrata were all proposed to belong to genera different from Saccharomyces, and this is supported by the topology of our tree as well [].”
To compare the veracity of the NCD clustering with a more featurebased clustering, we also calculated the pairwise distances as follows: Each file is converted to a 4096dimensional vector by considering the frequency of all (overlapping) 6byte contiguous blocks. The l2distance (Euclidean distance) is calculated between each pair of files by taking the square root of the sum of the squares of the componentwise differences. These distances are arranged into a distance matrix and linearly scaled to fit the range [0,1.0]. Finally, we ran the clustering routine on this distance matrix. The results are in Figure ??. As seen by comparing with the NCDbased Figure ?? there are apparent misplacements when using the Euclidean distance in this way. Thus, in this simple experiment, the NCD performed better, that is, agreed more precisely with accepted biological knowledge.
Our method improves the results of [], using a linguistic corpus of “The Universal Declaration of Human Rights (UDoHR)” [] in 52 languages. Previously, [] used an asymmetric measure based on relative entropy, and the full matrix of the pairwise distances between all 52 languages, to build a language classification tree. This experiment was repeated (resulting in a somewhat better tree) using the compression method in [] using standard biological software packages to construct the phylogeny. We have redone this experiment, and done new experiments, using the CompLearn Toolkit. Here, we report on an experiment to separate radically different language families. We downloaded the language versions of the UDoHR text in English, Spanish, Dutch, German (NativeEuropean), Pemba, Dendi, Ndbele, Kicongo, Somali, Rundi, Ditammari, Dagaare (Native African), Chikasaw, Perhupecha, Mazahua, Zapoteco (NativeAmerican), and didn’t preprocess them except for removing initial identifying information. We used an LempelZivtype compressor gzip to compress text sequences of sizes not exceeding the length of the sliding window gzip uses (32 kilobytes), and compute the NCD for each pair of language sequences. Subsequently we clustered the result. We show the outcome of one of the experiments in Figure ??. Note that three groups are correctly clustered, and that even the subclusters of the European languages are correct (English is grouped with the Romance languages because it contains up to 40% admixture of words from Latin origin).
The texts used in this experiment were downloaded from the worldwide web in original Cyrilliclettered Russian and in Latinlettered English by L. Avanasiev (Moldavian MSc student at the University of Amsterdam). The compressor used to compute the NCD matrix was bzip2. We clustered Russian literature in the original (Cyrillic) by Gogol, Dostojevski, Tolstoy, Bulgakov,Tsjechov, with three or four different texts per author. Our purpose was to see whether the clustering is sensitive enough, and the authors distinctive enough, to result in clustering by author. In Figure ?? we see a perfect clustering. Considering the English translations of the same texts, in Figure ??, we see errors in the clustering. Inspection shows that the clustering is now partially based on the translator. It appears that the translator superimposes his characteristics on the texts, partially suppressing the characteristics of the original authors. In other experiments we separated authors by gender and by period.
Perhaps surprisingly, it turns out that scanning a picture in raster rowmajor order retains enough regularity in both dimensions for the compressor to grasp. A simple task along these lines is to cluster handwritten characters. The handwritten characters in Figure ?? were downloaded from the NIST Special Data Base 19 (optical character recognition database) on the worldwide web. Each file in the data directory contains 1 digit image, either a four, five, or six. Each pixel is a single character; ’#’ for a black pixel, ’.’ for white. Newlines are added at the end of each line. Each character is 128x128 pixels. The NCD matrix was computed using the compressor PPMZ. Figure ?? shows each character that is used. There are 10 of each digit “4,” “5,” “6,” making a total of 30 items in this experiment. All but one of the 4’s are put in the subtree rooted at n1, all but one of the 5’s are put in the subtree rooted at n4, and all 6’s are put in the subtree rooted at n3. The remaining 4 and 5 are in the branch n23,n13 joining n6 and n3. So 28 items out of 30 are clustered correctly, that is, 93%.
In the experiment above we used only 3 digits. Using the full set of decimal digits results in a lower clustering accuracy. However, we can use the NCD as a oblivious feature extraction technique to convert generic objects into finitedimensional vectors. This is done using the anchor method which we introduced in Chapter 5, Section ??. We have used this technique to train a support vector machine (SVM) based OCR system to classify handwritten digits by extracting 80 distinct, ordered NCD features from each input image. The images are black and white square rasterized images. The anchors are chosen once and for all at the beginning of the experiment randomly from the training object pool, ensuring that eight examples of each class are chosen. Once chosen, the anchors are kept in order (so that the first coordinate always refers to the same anchor and so on) and used to translate all other training data files into 80dimensional vectors. In this initial stage of ongoing research, by our oblivious method of compressionbased clustering to supply a kernel for an SVM classifier, we achieved a handwritten single decimal digit recognition accuracy of 85%. The current stateoftheart for this problem, after half a century of interactive featuredriven classification research, in the upper ninety % level [, ]. All experiments are bench marked on the standard NIST Special Data Base 19 (optical character recognition database).
As a proof of principle we clustered data from unknown objects, for example objects that are far away. In [] observations of the microquasar GRS 1915+105 made with the Rossi Xray Timing Explorer were analyzed. The interest in this microquasar stems from the fact that it was the first Galactic object to show a certain behavior (superluminal expansion in radio observations). Photonometric observation data from Xray telescopes were divided into short time segments (usually in the order of one minute), and these segments have been classified into a bewildering array of fifteen different modes after considerable effort. Briefly, spectrum hardness ratios (roughly, “color”) and photon count sequences were used to classify a given interval into categories of variability modes. From this analysis, the extremely complex variability of this source was reduced to transitions between three basic states, which, interpreted in astronomical terms, gives rise to an explanation of this peculiar source in standard blackhole theory. The data we used in this experiment made available to us by M. Klein Wolt (coauthor of the above paper) and T. Maccarone, both researchers at the Astronomical Institute “Anton Pannekoek”, University of Amsterdam. The observations are essentially time series, and our aim was experimenting with our method as a pilot to more extensive joint research. Here the task was to see whether the clustering would agree with the classification above. The NCD matrix was computed using the compressor PPMZ. The results are in Figure ??. We clustered 12 objects, consisting of three intervals from four different categories denoted as δ, γ, φ, θ in Table 1 of []. In Figure ?? we denote the categories by the corresponding Roman letters D, G, P, and T, respectively. The resulting tree groups these different modes together in a way that is consistent with the classification by experts for these observations. The oblivious compression clustering corresponds precisely with the laborious featuredriven classification in [].
To interpret what the NCD is doing, and to explain its remarkable accuracy and robustness across application fields and compressors, the intuition is that the NCD minorizes all similarity metrics based on features that are captured by the reference compressor involved. Such features must be relatively simple in the sense that they are expressed by an aspect that the compressor analyzes (for example frequencies, matches, repeats). Certain sophisticated features may well be expressible as combinations of such simple features, and are therefore themselves simple features in this sense. The extensive experimenting above shows that even elusive features are captured.
A potential application of our nonfeature (or rather, manyunknownfeature) approach is exploratory. Presented with data for which the features are as yet unknown, certain dominant features governing similarity are automatically discovered by the NCD. Examining the data underlying the clusters may yield this hitherto unknown dominant feature.
Our experiments indicate that the NCD has application in two new areas of support vector machine (SVM) based learning. Firstly, we find that the inverted NCD (1NCD) is useful as a kernel for generic objects in SVM learning. Secondly, we can use the normal NCD as a featureextraction technique to convert generic objects into finitedimensional vectors, see the last paragraph of Section ??. In effect our similarity engine aims at the ideal of a perfect data mining process, discovering unknown features in which the data can be similar. This is the subject of current joint research in genomics of fungi, clinical molecular genetics, and radioastronomy.
We thank Loredana Afanasiev, Graduate School of Logic, University of Amsterdam; Teun Boekhout, Eiko Kuramae, Vincent Robert, Fungal Biodiversity Center, Royal Netherlands Academy of Sciences; Marc Klein Wolt, Thomas Maccarone, Astronomical Institute “Anton Pannekoek”, University of Amsterdam; Evgeny Verbitskiy, Philips Research; Steven de Rooij, Ronald de Wolf, CWI; the referees and the editors, for suggestions, comments, help with experiments, and data; Jorma Rissanen and Boris Ryabko for useful discussions, TzuKuo Huang for pointing out some typos and simplifications, and Teemu Roos and Henri Tirry for implementing a visualization of the clustering process.
The NCD investigations of the previous chapters focused on using data compressors to compress data in files. This chapter deals with an entirely different sort of analysis that is not performed on files but rather on search terms for the Google web search engine. By using wellknown connections between codelengths and probabilities, we apply the NCD theory to Google’s search engine index, providing insight into the subjective relationships enjoyed among a group of words or phrases. The Google Simple Object Access Protocol is used to connect it with the CompLearn system. Remarkably, the system does not use the contents of web pages directly, but instead only uses the estimated results count indicator from the Google search engine to make a probabilistic model of the web. This model is based on sampling each search term in a group as well as all pairs in order to find structure in their cooccurrence. Before explaining the method in detail the reader is invited to have a look at the experiment in Figure ?? involving the names of politicians. The tree shows the subjective relationships among several European Commission members. After giving a general introduction to the method, we introduce some relevant background material in Section ??. We explain the formula that connects NCD to Google in Section ??. We provide a sketch of one possible theoretical breakdown concerning the surprising robustness of the results and consequent Googlebased distance metric. We prove a certain sort of universality property for this metric. In Section ??, we present a variety of experiments demonstrating the sorts of results that may be obtained. We demonstrate positive correlations, evidencing an underlying semantic structure, in both numerical symbol notations and numbername words in a variety of natural languages and contexts. Next, we demonstrate the ability to distinguish between colors and numbers, and to distinguish between 17th century Dutch painters; the ability to understand electrical terms, religious terms, and emergency incidents; we conduct a massive experiment in understanding WordNet categories; and finally we demonstrate the ability to do a simple automatic EnglishSpanish vocabulary acquisition.
Objects can be given literally, like the literal fourletter genome of a mouse, or the literal text of War and Peace by Tolstoy. For simplicity we take it that all meaning of the object is represented by the literal object itself. Objects can also be given by name, like “the fourletter genome of a mouse,” or “the text of War and Peace by Tolstoy.” There are also objects that cannot be given literally, but only by name, and that acquire their meaning from their contexts in background common knowledge in humankind, like “home” or “red.” To make computers more intelligent one would like to represent meaning in computer digestible form. Longterm and laborintensive efforts like the Cyc project [] and the WordNet project [] try to establish semantic relations between common objects, or, more precisely, names for those objects. The idea is to create a semantic web of such vast proportions that rudimentary intelligence, and knowledge about the real world, spontaneously emerge. This comes at the great cost of designing structures capable of manipulating knowledge, and entering high quality contents in these structures by knowledgeable human experts. While the efforts are longrunning and large scale, the overall information entered is minute compared to what is available on the worldwideweb.
The rise of the worldwideweb has enticed millions of users to type in trillions of characters to create billions of web pages of on average low quality contents. The sheer mass of the information available about almost every conceivable topic makes it likely that extremes will cancel and the majority or average is meaningful in a lowquality approximate sense. We devise a general method to tap the amorphous lowgrade knowledge available for free on the worldwideweb, typed in by local users aiming at personal gratification of diverse objectives, and yet globally achieving what is effectively the largest semantic electronic database in the world. Moreover, this database is available for all by using any search engine that can return aggregate pagecount estimates for a large range of search queries, like Google.
Previously, we and others developed a compressionbased method to establish a universal similarity metric among objects given as finite binary strings [, , , , , ], which was widely reported [, , ]; some of these experiments are shown in chapters 5 and 7. Such objects can be genomes, music pieces in MIDI format, computer programs in Ruby or C, pictures in simple bitmap formats, or time sequences such as heart rhythm data. This method is featurefree in the sense that it does not analyze the files looking for particular features; rather it analyzes all features simultaneously and determines the similarity between every pair of objects according to the most dominant shared feature. The crucial point is that the method analyzes the objects themselves. This precludes comparison of abstract notions or other objects that do not lend themselves to direct analysis, like emotions, colors, Socrates, Plato, Mike Bonanno and Albert Einstein. While the previous method that compares the objects themselves is particularly suited to obtain knowledge about the similarity of objects themselves, irrespective of common beliefs about such similarities, here we develop a method that uses only the name of an object and obtains knowledge about the similarity of objects by tapping available information generated by multitudes of web users. Here we are reminded of the words of D.H. Rumsfeld []
“A trained ape can know an awful lot
Of what is going on in this world,
Just by punching on his mouse
For a relatively modest cost!”
This is useful to extract knowledge from a given corpus of knowledge, in this case the Google database, but not to obtain true facts that are not common knowledge in that database. For example, common viewpoints on the creation myths in different religions may be extracted by the Googling method, but contentious questions of fact concerning the phylogeny of species can be better approached by using the genomes of these species, rather than by opinion.
Intuitively, the approach is as follows. The Google search engine indexes around ten billion pages on the web today. Each such page can be viewed as a set of index terms. A search for a particular index term, say “horse”, returns a certain number of hits (web pages where this term occurred), say 46,700,000. The number of hits for the search term “rider” is, say, 12,200,000. It is also possible to search for the pages where both “horse” and “rider” occur. This gives, say, 2,630,000 hits. This can be easily put in the standard probabilistic framework. If w is a web page and x a search term, then we write x ∈ w to mean that Google returns web page w when presented with search term x. An event is a set of web pages returned by Google after it has been presented by a search term. We can view the event as the collection of all contexts of the search term, background knowledge, as induced by the accessible web pages for the Google search engine. If the search term is x, then we denote the event by x, and define x = {w: x ∈ w }. The probability p(x) of an event x is the number of web pages in the event divided by the overall number M of web pages possibly returned by Google. Thus, p( x)= x/M. At the time of writing, Google searches 8,058,044,651 web pages. Define the joint event x ∩y = { w : x,y ∈ w} as the set of web pages returned by Google, containing both the search term x and the search term y. The joint probability p(x, y) = { w : x,y ∈ w}/M is the number of web pages in the joint event divided by the overall number M of web pages possibly returned by Google. This notation also allows us to define the probability p(xy) of conditional events xy = (x ∩y)/y defined by p(x y) = p( x,y)/p(y). We explain later what happens if p(y) = 0.
In the above example we have therefore p(horse) ≈ 0.0058. Furthermore, we have p(rider) ≈ 0.0015, and p(horse,rider) ≈ 0.0003. We conclude that the probability p(horserider) of “horse” accompanying “rider” is ≈ 1/5 and the probability p(riderhorse) of “rider” accompanying “horse” is ≈ 1/19. The probabilities are asymmetric, and it is the least probability that is the significant one. A very general search term like “the” occurs in virtually all (English language) web pages. Hence p(therider) ≈ 1, and for almost all search terms x we have p(thex) ≈ 1. But p(riderthe) ≪ 1, say about equal to p(rider), and gives the relevant information about the association of the two terms.
Our first attempt is therefore a distance
D_{1} (x,y) = min{ p(xy),p(yx) }. 
Experimenting with this distance gives bad results because the differences among small probabilities have increasing significance the smaller the probabilities involved are. Another reason is that we deal with absolute probabilities: two notions that have very small probabilities each and have D_{1}distance є are much less similar than two notions that have much larger probabilities and have the same D_{1}distance. To resolve the first problem we take the negative logarithm of the items being minimized, resulting in
D_{2} (x,y) = max{ log1/p(xy), log1/p(yx) }. 
To fix the second problem we normalize D_{2}(x,y) by dividing by the maximum of either of log1/p(x) or log1/p(y). Altogether, we obtain the following normalized distance
D_{3} (x,y) = 
 , 
for p(xy) > 0 (and hence p(yx)>0), and D_{3} (x,y) = ∞ for p(xy)=0 (and hence p(yx)=0). Note that p(xy) = p(x,y)/p(y)=0 means that the search terms “x” and “y” never occur together. The two conditional complexities are either both 0 or they are both strictly positive. Moreover, as long as p(x)>0, p(y)>0 , then so are the conditional probabilities, but not necessarily vice versa.
We note that in the conditional probabilities the total number M, of web pages indexed by Google, is divided out. Therefore, the conditional probabilities are independent of M, and can be replaced by the number of pages, the frequency, returned by Google. Define the frequency f(x) of search term x as the number of pages a Google search for x returns: f(x)= Mp(x), f(x,y)=Mp(x,y), and p(xy) = f(x,y)/f(y). Rewriting D_{3} results in our final notion, the normalized Google distance (NGD ), defined by
NGD (x,y) = 
 , (1) 
and if f(x),f(y)>0 and f(x,y)=0 then NGD (x,y)= ∞. From (??) we see that
Note that NGD (x,y) can exceed 1 for natural search terms. For example, the two terms: “by”, generating a count of 2,770,000,000, and “with”, generating a count of 2,566,000,000, together give a count of only 49,700,000. Using (??) (with M=8,058,044,651), we obtain NGD (by,with)=≈ 3.51. The explanation is that two terms that just happen to never occur together for whatever reason, but do occur separately often, can create an NGD that is high.
In practice, however, most pairs’ NGD ’s come between 0 and 1. This is not due to an imperfection in the method. Rather, NGD values greater than 1 may be thought to correspond to the idea of negative correlation in probability theory; we may venture to guess, for instance, that the English words “by” and “with” are both very popular, yet can often be used as substitutes for one another, and thus a rather large proportion of webpages contain either “by” or “with” but not both. In the case of NID , (??), we meet later, the subadditivity property of K yields the upper bound of 1, but there is no such restriction for an arbitrary probability distribution such as Google.
With the Google hit numbers above, we can now compute, using (??),
NGD (horse,rider) ≈ 0.443. 
We did the same calculation when Google indexed only onehalf of the current number of pages and received 4,285,199,774. It is instructive that the probabilities of the used search terms didn’t change significantly over this doubling of pages, with number of hits for “horse” equal 23,700,000, for “rider” equal 6,270,000, and for “horse, rider” equal to 1,180,000. The NGD (horse,rider) we computed in that situation was ≈ 0.460. This is in line with our contention that the relative frequencies of web pages containing search terms gives objective information about the semantic relations between the search terms. If this is the case, then with the vastness of the information accessed by Google the Google probabilities of search terms and the computed NGD ’s should stabilize (be scale invariant) with a growing Google database. The above derivation gives the intuition. However, the original thinking which led to the NGD derived from a mathematical theory of similarity based on Kolmogorov complexity, and we shall trace this derivation in Section ??. The following are the main properties of the NGD :
If frequency f(x)=M and f(x,y) < f(x), then NGD (x,y)= ∞. If f(x)=f(x,y)=M, then NGD (x,y)= 0/0, that is, it is undefined. These anomalies, and related anomalies, are redressed by the proper formulation (??) of the NGD , with quantity N > M replacing M, in the theoretical analysis of Section ??.
It has been brought to our attention, that there is a great deal of work in both cognitive psychology [], linguistics, and computer science, about using word (phrases) frequencies in text corpora to develop measures for word similarity or word association, partially surveyed in [, ], going back to at least []. These approaches are based on arguments and theories that are fundamentally different from the approach we develop, which is based on coding and compression, based on the theory of Kolmogorov complexity []. This allows to express and prove properties of absolute relations between objects that cannot even be expressed by other approaches. The NGD is the result of a new type of theory and as far as we know is not equivalent to any earlier measure. Nevertheless, in practice the resulting measure may still sometimes lead to similar results as existing methods. The current thesis is a next step in a decade of cumulative research in this area, of which the main thread is [, , , , , , ] with [, ] using the related approach of [].
Previously, we have outlined the classical information theoretic notions that have underpinned our approach, as well as the more novel ideas of Kolmogorov complexity, information distance, and compressionbased similarity metric (Section ??). Here, we give a technical description of the Google distribution, the Normalized Google Distance, and the universality of these notions (Section ??), preceded by Subsection ?? pressing home the difference between literal object based similarity (as in compression based similarity), and context based similarity between objects that are not given literally but only in the form of names that acquire their meaning through contexts in databases of background information (as in Google based similarity). In Section ?? we present a plethora of clustering and various classification experiments to validate the universality, robustness, and accuracy of our proposal. A mass of experimental work, which for space reasons can not be reported here, is available []. In section ?? we explained some basics of the SVM approach we use in the classification experiments, where the Google similarity is used to extract feature vectors used by the kernel.
Every text corpus or particular user combined with a frequency extractor defines its own relative frequencies of words and phrases. In the worldwideweb and Google setting there are millions of users and text corpora, each with its own distribution. In the sequel, we show (and prove) that the Google distribution is universal for all the individual web users distributions.
The number of web pages currently indexed by Google is approaching 10^{10}. Every common search term occurs in millions of web pages. This number is so vast, and the number of web authors generating web pages is so enormous (and can be assumed to be a truly representative very large sample from humankind), that the probabilities of Google search terms, conceived as the frequencies of page counts returned by Google divided by the number of pages indexed by Google, may approximate the actual relative frequencies of those search terms as actually used in society. Based on this premise, the theory we develop in this chapter states that the relations represented by the Normalized Google Distance (??) approximately capture the assumed true semantic relations governing the search terms. The NGD formula (??) only uses the probabilities of search terms extracted from the text corpus in question. We use the world wide web and Google, but the same method may be used with other text corpora like the King James version of the Bible or the Oxford English Dictionary and frequency count extractors, or the worldwideweb again and Yahoo as frequency count extractor. In these cases one obtains a text corpus and frequency extractor biased semantics of the search terms. To obtain the true relative frequencies of words and phrases in society is a major problem in applied linguistic research. This requires analyzing representative random samples of sufficient sizes. The question of how to sample randomly and representatively is a continuous source of debate. Our contention that the web is such a large and diverse text corpus, and Google such an able extractor, that the relative page counts approximate the true societal word and phrases usage, starts to be supported by current real linguistics research [].
Similarly, the NGD minorizes and incorporates all the different semantics of all the different users and text corpora on the web. It extracts as it were the semantics as used in the society (of all these web users) and not just the bias of any individual user or document. This is only possible using the web, since its sheer mass of users and documents with different intentions averages out to give the true semantic meaning as used in society. This is experimentally evidenced by the fact that when Google doubled its size the sample semantics of rider, horse stayed the same. Determining the NGD between two Google search terms does not involve analysis of particular features or specific background knowledge of the problem area. Instead, it analyzes all features automatically through Google searches of the most general background knowledge data base: the worldwideweb. (In Statistics “parameterfree estimation” means that the number of parameters analyzed is infinite or not a priori restricted. In our setting “featurefreeness” means that we analyze all features.)
We start from the observation that a compressor defines a code word length for every source word, namely, the number of bits in the compressed version of that source word. Viewing this code as a ShannonFano code, Section ??, it defines in its turn a probability mass function on the source words. Conversely, every probability mass function of the source words defines a ShannonFano code of the source words. Since this code is optimally compact in the sense of having expected codeword length equal to the entropy of the initial probability mass function, we take the viewpoint that a probability mass function is a compressor.
For example, the NID (Normalized Information Distance, Chapter 3, Section ??) uses the probability mass function m(x) = 2^{−K(x)}, where K is the Kolmogorov complexity function, Chapter 2. This function is not computable, but it has the weaker property of being lower semicomputable: by approximating K(x) from above by better and better compressors, we approximate m(x) from below. The distribution m(x) has the remarkable property that it dominates every lower semicomputable probability mass function P(x) (and hence all computable ones) by assigning more probability to every finite binary string x than P(x), up to a multiplicative constant c_{P}>0 depending on P but not on x (m(x) ≥ c_{P} P(x)). We say that m(x) is universal for the enumeration of all lower semicomputable probability mass functions, [], a terminology that is closely related to the “universality” of a universal Turing machine in the enumeration of all Turing machines. It is this property that allows us to show [] that NID is the most informative distance (actually a metric) among a large family of (possibly nonmetric) “admissible normalized information distances.” But we cannot apply these same formal claims to the realworld NCD , except in a sense that is relativized on how well the compressor approximates the ultimate Kolmogorov complexity [, , ] and as shown in Section ??.
In essence, the choice of compressor brings a particular set of assumptions to bear upon an incoming data stream, and if these assumptions turn out to be accurate for the data in question, then compression is achieved. This is the same as saying that the probability mass function defined by the compressor concentrates high probability on these data. If a pair of files share information in a way that matches the assumptions of a particular compressor, then we obtain a low NCD . Every compressor analyzes the string to be compressed by quantifying an associated family of features. A compressor such as gzip detects a class of features, for example matching substrings that are separated by no more than 32 kilobytes. Certain higherorder similarities are detected in the final Huffman coding phase. This explains how gzip is able to correctly cluster files generated by Bernoulli processes. A better compressor like bzip2 detects substring matches across a wider window of 900 kilobytes, and detects an expanded set of higherorder features. Such compressors implicitly assume that the data has no global, structured, meaning. The compressor only looks for statistical biases, repetitions, and other biases in symmetrically defined local contexts, and cannot achieve compression even for very lowcomplexity meaningful strings like the digits of π. They assume the data source is at some level a simple stationary ergodic random information source which is by definition meaningless. The problem with this is clearly sketched by the great probabilist A.N. Kolmogorov [, ]: “The probabilistic approach is natural in the theory of information transmission over communication channels carrying ‘bulk’ information consisting of a large number of unrelated or weakly related messages obeying definite probabilistic laws. … [it] can be convincingly applied to the information contained, for example, in a stream of congratulatory telegrams. But what real meaning is there, for example, in [applying this approach to] ‘War and Peace’? · Or, on the other hand, must we assume that the individual scenes in this book form a random sequence with ‘stochastic relations’ that damp out quite rapidly over a distance of several pages?” The compressors apply no external knowledge to the compression, and so will not take advantage of the fact that U always follows Q in the English language, and instead must learn this fact anew for each separate file (or pair) despite the simple ubiquity of this rule. Thus the generality of common data compressors is also a liability, because the features which are extracted are by construction meaningless and devoid of relevance.
Yet, files exist in the real world, and the files that actually exist in stored format by and large carry a tremendous amount of structural, global, meaning; if they didn’t then we would throw them away as useless. They do exhibit heavy biases in terms of the meaningless features, for instance in the way the letters T and E occur more frequently in English than Z or Q, but even this fails to capture the heart of the reason of the file’s existence in the first place: because of its relevance to the rest of the world. But gzip does not know this reason; it is as if everywhere gzip looks it only finds a loaded die or biased coin, resolute in its objective and foolish consistency. In order to address this coding deficiency we choose an opposing strategy: instead of trying to apply no external knowledge at all during compression, we try to apply as much as we can from as many sources as possible simultaneously, and in so doing attempt to capture not the literal part but instead the contextualized importance of each string within a greater and allinclusive whole.
Thus, instead of starting with a standard data compression program, we start from a probability mass function that reflects knowledge, and construct the corresponding ShannonFano code to convert probabilities to code word lengths, and apply the NCD formula. At this moment one database stands out as the pinnacle of computer accessible human knowledge and the most inclusive summary of statistical information: the Google search engine. There can be no doubt that Google has already enabled science to accelerate tremendously and revolutionized the research process. It has dominated the attention of internet users for years, and has recently attracted substantial attention of many Wall Street investors, even reshaping their ideas of company financing. We have devised a way to interface the Google search engine to our NCD software to create a new type of pseudocompressor based NCD , and call this new distance the Normalized Google Distance, or NGD . We have replaced the obstinate objectivity of classical compressors with an anthropomorphic subjectivity derived from the efforts of millions of people worldwide. Experiments suggest that this new distance shares some strengths and weaknesses in common with the humans that have helped create it: it is highly adaptable and nearly unrestricted in terms of domain, but at the same time is imprecise and fickle in its behavior. It is limited in that it doesn’t analyze the literal objects like the NCD does, but instead uses names for those objects in the form of ASCII search terms or tuples of terms as inputs to extract the meaning of those objects from the total of information on the worldwideweb.
An example may help clarify the distinction between these two opposing paradigms. Consider the following sequence of letters:
U Q B
Assume that the next letter will be a vowel. What vowel would you guess is most likely, in the absence of any more specific information? One common assumption is that the samples are i.i.d. (identical, independently distributed), and given this assumption a good guess is U ; since it has already been shown once, chances are good that U is weighted heavily in the true generating distribution. In assuming i.i.d.ness, we implicitly assume that there is some true underlying random information source. This assumption is often wrong in practice, even in an approximate sense. Changing the problem slightly, using English words as tokens instead of just letters, suppose we are given the sequence
the quick brown
Now we are told that the next word has three letters and does not end the sentence. We may imagine various three letter words that fit the bill. On an analysis as before, we ought to expect the to continue the sentence. The computer lists 535 English words of exactly three letters. We may use the gzip data compressor to compute the NCD for each possible completion like this: NCD (the quick brown,cow), NCD (the quick brown,the), and so on, for all of the 3letter words. We may then sort the words in ascending order of NCD and this yields the following words in front, all with NCD of 0.61: own, row, she, the . There are other three letter words, like hot, that have NCD of 0.65, and many with larger distance. With such very small input strings, there are granularity effects associated with the compressor rounding to full bytes, which makes compression resolve only to the level of 8 bits at best. So as we might expect, gzip is using a sort of inference substantially similar to the sort that might lead the reader to guess U as a possible completion in the first example above.
Consider now what would happen if we were to use Google instead of gzip as the data compressor. Here we may change the input domain slightly; before, we operated on general strings, binary or otherwise. With Google, we will restrict ourselves to ASCII words that can be used as search terms in the Google Search engine. With each search result, Google returns a count of matched pages. This can be thought to define a function mapping search terms (or combinations thereof) to page counts. This, in turn, can be thought to define a probability distribution, and we may use a Shannon Fano code to associate a code length with each page count. We divide the total number of pages returned on a query by the maximum that can be returned, when converting a page count to a probability; at the time of these experiments, the maximum was about 5,000,000,000. Computing the NGD of the phrase the quick brown, with each threeletter word that may continue the phrase (ignoring the constraint that that word immediately follows the word brown), we arrive at these first five most likely (candidate, NGD )pairs (using the Google values at the time of writing):
fox: 0.532154812757325 vex: 0.561640307093518 jot: 0.579817813761161 hex: 0.589457285818459 pea: 0.604444512168738
As many typing students no doubt remember, a popular phrase to learn the alphabet is The quick brown fox jumps over the lazy dog. It is valuable because it uses every letter of the English alphabet.
Thus, we see a contrast between two styles of induction: The first type is the NCD based on a literal interpretation of the data: the data is the object itself. The second type is the NGD based on interpreting the data as a name for an abstract object which acquires its meaning from masses of contexts expressing a large body of commonsense knowledge. It may be said that the first case ignores the meaning of the message, whereas the second focuses on it.
Using the Google search engine we obtain the number of web pages containing the requested search term, as in Section ??. That is what we actually use in our experiments. For our theoretical analysis, where we want to express the ShannonFano code length of encoding a search term based on a probability of the associated event, this will turn out not proper. Instead, we need a minor renormalization, which in typical situations only slightly changes the values according to (??), and resolves the anomalies observed in Remark ??. Let the set of singleton Google search terms be denoted by S. In the sequel we use both singleton search terms and doubleton search terms {{x,y}: x,y ∈ S }. Let the set of web pages indexed (possible of being returned) by Google be Ω. The cardinality of Ω is denoted by M=Ω, and at the time of this writing 8· 10^{9} ≤ M ≤ 9 · 10^{9} (and presumably greater by the time of reading this). Assume that a priori all web pages are equiprobable, with the probability of being returned by Google being 1/M. A subset of Ω is called an event. Every search term x usable by Google defines a singleton Google event x ⊆ Ω of web pages that contain an occurrence of x and are returned by Google if we do a search for x. Let L: Ω → [0,1] be the uniform mass probability function. The probability of such an event x is L(x)=x/M. Similarly, the doubleton Google event x ∩y ⊆ Ω is the set of web pages returned by Google if we do a search for pages containing both search term x and search term y. The probability of this event is L(x ∩y) = x ∩y/M. We can also define the other Boolean combinations: ¬ x= Ω \ x and x ∪y = ¬ ( ¬ x ∩¬ y), each such event having a probability equal to its cardinality divided by M. If e is an event obtained from the basic events x, y, …, corresponding to basic search terms x,y, …, by finitely many applications of the Boolean operations, then the probability L(e) = e/M.
Google events capture in a particular sense all background knowledge about the search terms concerned available (to Google) on the web. The Google event x, consisting of the set of all web pages containing one or more occurrences of the search term x, thus embodies, in every possible sense, all direct context in which x occurs on the web. It is of course possible that parts of this direct contextual material link to other web pages in which x does not occur and thereby supply additional context. In our approach this indirect context is ignored. Nonetheless, indirect context may be important and future refinements of the method may take it into account. The event x consists of all possible direct knowledge on the web regarding x. Therefore, it is natural to consider code words for those events as coding this background knowledge. However, we cannot use the probability of the events directly to determine a prefix code, or, rather the underlying information content implied by the probability. The reason is that the events overlap and hence the summed probability exceeds 1. By the Kraft inequality [] this prevents a corresponding set of codeword lengths. The solution is to normalize: We use the probability of the Google events to define a probability mass function over the set {{x,y}: x,y ∈ S} of Google search terms, both singleton and doubleton terms. The singleton terms correspond to elements {x,x} = {x}, the doubleton terms correspond to elements {x,y} with x ≠ y. There are  S singleton terms, and  S 2 doubletons consisting of a pair of nonidentical terms. Define
N= 
 x ∩ y, 
counting each singleton set and each doubleton set (by definition unordered) once in the summation. Note that this means that for every pair {x,y} ⊆ S, with x ≠ y, the web pages z ∈ x ∩ y are counted three times: once in x = x ∩ x, once in y = y ∩ y, and once in x ∩ y. Since every web page that is indexed by Google contains at least one occurrence of a search term, we have N ≥ M. On the other hand, web pages contain on average not more than a certain constant α search terms. Therefore, N ≤ α M. Define

Then, ∑_{{x,y} ⊆ S} g(x,y) = 1. This gdistribution changes over time, and between different samplings from the distribution. But let us imagine that g holds in the sense of an instantaneous snapshot. The real situation will be an approximation of this. Given the Google machinery, these are absolute probabilities which allow us to define the associated prefix codeword lengths (information contents) for both the singletons and the doubletons. The Google code G is defined by

In contrast to strings x where the complexity C(x) represents the length of the compressed version of x using compressor C, for a search term x (just the name for an object rather than the object itself), the Google code of length G(x) represents the shortest expected prefixcode word length of the associated Google event x. The expectation is taken over the Google distribution p. In this sense we can use the Google distribution as a compressor for Google “meaning” associated with the search terms. The associated NCD , now called the normalized Google distance (NGD ) is then defined by (??) with N substituted for M, rewritten as
NGD (x,y)= 
 . (3) 
This NGD is an approximation to the NID of (??) using the ShannonFano code (Google code) generated by the Google distribution as defining a compressor approximating the length of the Kolmogorov code, using the background knowledge on the web as viewed by Google as conditional information.
The NGD according to (??) equals the practically used NGD according to (??) up to a multiplicative factor
δ(x,y) = 
 . 
Proof. Straightforward rewriting.
This factor 0 ≤ δ(x,y) ≤ 1 is the price we pay for proper prefix coding of the Google events corresponding to the search terms. We assume that M ≤ N ≤ 1000 M, since on average web pages probably do not contain more than 1000 Google search terms. Therefore, logN − logM ≤ 10. Used in practice, (??) may possibly improve the results in case min{ logf(x),f(y) } is large, say 20. With M = 5 · 10^{9}, and N=10^{3} M, we have δ (x,y) ≈ 1/2. But for min{ logf(x),f(y) } is small, say 10, we have δ (x,y) ≈ 2/3. Indeed, this normalization (??) using N instead of M also resolves the anomalies with (??) discussed in Remark ??. In fact, however, our experimental results suggest that every reasonable (greater than any f(x)) value can be used for the normalizing factor N, and our results seem in general insensitive to this choice. In our software, this parameter N can be adjusted as appropriate, and we often use M for N.
Like the NGD according to (??), the one according to (??) is nonnegative for all x,y, NGD (x,x)=0 for every x, and it is symmetric. But again it is not a metric since there are x,y, x ≠ y, with NGD (x,y)=0, and the triangle inequality is violated. The example used to show violation of the triangle inequality of (??) yields here: NGD (x,y)= 1/2 logM / (logN − 1/2 logM) and NGD (x,z)=NGD (z,y)=1/(logN − 1/2 logM −1).
A central notion in the application of compression to learning is the notion of “universal distribution,” see []. Consider an effective enumeration P = p_{1},p_{2}, … of probability mass functions with domain S. The list P can be finite or countably infinite. A probability mass function p_{u} occurring in P is universal for P, if for every p_{i} in P there is a constant c_{i} >0 and ∑_{i ≠ u} c_{i} ≥ 1, such that for every x ∈ S we have p_{u}(x) ≥ c_{i} · p_{i} (x). Here c_{i} may depend on the indexes u,i, but not on the functional mappings of the elements of list P nor on x.
If p_{u} is universal for P, then it immediately follows that for every p_{i} in P, the ShannonFano codeword length for source word x, Section ??, associated with p_{u}, minorizes the ShannonFano codeword length associated with p_{i}, by satisfying log1/p_{u}(x) ≤ log1/p_{i}(x) + logc_{i}, for every x ∈ S.
Now consider the particular (finite) list of probability mass functions of search terms, each probability mass function associated with its web author in the list A = 1,2, … ,a of web authors producing pages on the web. In the following we simply model the generated probability mass functions via frequency analysis, and make no claims as to how the pages were made. For convenience we assume that each page is generated by a single web author, and that the pages are indexed by Google. Let web author i of the list A produce the set of web pages Ω_{i} and denote M_{i}=Ω_{i}. We identify a web author i with the set of web pages Ω_{i} he produces. Since we have no knowledge of the set of web authors, we consider every possible partition of Ω into one of more equivalence classes, Ω = Ω_{1} ∪⋯ ∪Ω_{a}, Ω_{i} ∩Ω_{j} = ∅ (1 ≤ i ≠ j ≤ a ≤ Ω), as defining a realizable set of web authors A= 1, …, a.
Consider a partition of Ω into Ω_{1}, … , Ω_{a}. A search term x usable by Google defines an event x_{i} ⊆ Ω_{i} of web pages produced by web author i that contain search term x. Similarly, x_{i} ∩y_{i} is the set of web pages produced by i that is returned by Google searching for pages containing both search term x and search term y. Let
N_{i} = 
 x_{i} ∩ y_{i}. 
Note that there is an α_{i} ≥ 1 such that M_{i} ≤ N_{i} ≤ α_{i} M_{i}. For every search term x ∈ S define a probability mass function g_{i}, the individual web author’s Google distribution, on the sample space {{x,y}: x,y ∈ S} by

Then, ∑_{{x,y} ⊆ S} g_{i}(x,y) = 1. Let Ω_{1}, … , Ω_{a} be any partition of Ω into subsets (web authors), and let g_{1}, … , g_{a} be the corresponding individual Google distributions. Then the Google distribution g is universal for the enumeration g,g_{1}, … , g_{a}.
Proof. We can express the overall Google distribution in terms of the individual web author’s distributions:

Consequently, g(x) ≥ (N_{i}/N) g_{i}(x) and g(x,y) ≥ (N_{i}/N) g_{i}(x,y).
The definition of universality above requires pointwise domination by the universal probability mass function of every enumerated probability mass function, up to a positive multiplicative constant that is independent of the properties of the graphs of the actual probability mass functions in the enumeration. The universality of a given probability mass function in the enumeration is invariant under changing the probability mass functions in the enumeration, within the obvious constraints, apart from the given universal probability mass function. Thus, for the Google distribution to be universal for the list of individual Google distributions, it does not matter how we change the individual Google distributions, or even add new distributions, as long as the weighted sum with weights N_{i}/N sums up to the overall (in this case also the universal) Google distribution. This requirement is in line with the extensive body of research in universal algorithmic probability, where the list of enumerated probability mass functions is countably infinite—related to the standard enumeration of Turing machines, []. It intends and ensures precisely the same nontrivial properties in the case of a finite list of probability mass functions, of precisely the same quality and intention: The universality notion has the same purpose and effect for finite and infinite enumerations.
For the sake of completeness, let us make a detailed comparison with the related notions in algorithmic probability. There, one is interested in establishing a universal probability mass function for a countably infinite list of all, and only, probability mass functions that are computable in a certain approximation sense. For details see the above reference. Here, it is relevant that all, and only, these probability mass functions can be effectively enumerated by starting from the standard effective enumeration of the list of all Turing machines, effectively modifying each Turing machine in the list so that it computes a probability mass function of the prescribed type. In this setting, it suffices to consider a series of weights c_{i} > 0 such that ∑c_{i} ≤ 1, for example, c_{i} = d/i^{2} with ∑1/i^{2} = d < ∞. For every converging infinite series ∑c_{i} < ∞ of positive terms c_{i}, we have that (i) the series sums to a particular positive value c, depending only on the summed series , and not on the probability mass functions being weighted; and (ii) lim_{i → ∞} c_{i} = 0. In the current setting of finitely many individual Google distributions, we have replaced the notion of “all countably infinitely many probability mass functions of a certain type” by “all combinations of probability mass functions possible by partitioning of Ω in all possible ways.” For a finite series ∑_{i=1}^{a} c_{i}, item (ii) is not applicable. Item (i) is translated into ∑_{i=1}^{a} c_{i} ≥ 1; but ∑_{i=1}^{a} c_{i} can be set to any positive constant that does not depend on Ω and S. Then, from a certain cardinality of Ω and S onwards, the choice will be satisfactory. Here we have chosen ∑_{i=1}^{a} c_{i} so as to suffice also for small cardinalities. Let us show that, for example, the uniform distribution L(x)=1/s (s= S) over the search terms x ∈ S is not universal, for s > 2. By the requirement ∑c_{i} ≥ 1, the sum taken over the number a of web authors in the list A, there is an i such that c_{i} ≥ 1/a. Taking the uniform distribution on say s search terms assigns probability 1/s to each of them. Now choosing a partition of Ω in a < s web authors, there is an individual Google distribution g_{i} with c_{i} ≥ 1/a, and we must satisfy g(x) ≥ c_{i} g_{i}(x). By the definition of universality of a probability mass function for the list of individual Google probability mass functions g_{i}, we can choose the function g_{i} freely (as long as a ≥ 2, and there is another function g_{j} to exchange probabilities of search terms with). So choose some search term x and set g_{i}(x)=1, and g_{i} (y)=0 for all search terms y ≠ x. Then, we obtain g(x)=1/s ≥ c_{i} g_{i} (x) = 1/a. This yields the required contradiction for s > a ≥ 2.
Every individual web author produces both an individual Google distribution g_{i}, and an individual ShannonFano code G_{i} associated with g_{i}, Section ??, for the search terms. The associated individual normalized Google distance NGD _{i} of web author i is defined according to (??), with G_{i} substituted for G. The normalized Google distance is universal for the family of individual normalized Google distances, in the sense that it is at least as small as the least individual normalized Google distance, with high probability. In Theorem ?? we show that, for every k ≥ 1, the inequality
NGD (x,y) < β NGD _{i}(x,y) + γ, (5) 
with β = (min{G_{i}(x),G_{i}(y) − logk)/min{G_{i}(x),G_{i}(y)} and γ = (logkN/N_{i})/min{G(x),G(y) } , is satisfied with probability going to 1 with growing k.
To interpret (??), we observe that in case G_{i}(x) and G_{i}(y) are large with respect to logk, then β ≈ 1. If moreover logN/N_{i} is large with respect to logk, then γ ≈ (logN/N_{i})/ min{G(x),G(y)}. Let us estimate γ under reasonable assumptions. Without loss of generality assume G(x) ≤ G(y). If f(x)= x, the number of pages returned on query x, then G(x)= log(N/f(x)). Thus, γ ≈ (logN − logN_{i})/ (logN − logf(x)). The uniform expectation of N_{i} is N/ A, and N divided by that expectation of N_{i} equals  A, the number of web authors producing web pages. The uniform expectation of f(x) is N/ S, and N divided by that expectation of f(x) equals  S, the number of Google search terms we use. Thus, the more the number of search terms exceeds the number of web authors, the more γ goes to 0 in expectation. To understand (??), we may consider the codelengths involved as the Google database changes over time. It is reasonable to expect that both the total number of pages as well as the total number of search terms in the Google database will continue to grow for some time. In this period, the sum total probability mass will be carved up into increasingly smaller pieces for more and more search terms. The maximum singleton and doubleton codelengths within the Google database will grow without bound. But the universality property of the Google distribution implies that the Google distribution’s code length for almost all particular search terms will be within a negligible error of the best codelength among any of the individual web authors. The size of this gap will grow more slowly than the codelength for any particular search term over time. Thus, the coding space that is suboptimal in the Google distribution’s code is an eversmaller piece (in terms of proportion) of the total coding space.
(i) For every pair of search terms x,y, the set of web authors B for which (??) holds, has ∑{N_{i}/N: i ∈ B} > (1 − 1/k)^{2}. That is, if we select a web page uniformly at random from the total set Ω of web pages, then we have probability > (1 − 1/k)^{2} that (??) holds for web author i that authored the selected web page.
(ii) For every web author i ∈ A, the g_{i}probability concentrated on the pairs of search terms for which (??) holds is at least (1−2/k)^{2}.
Proof. The ShannonFano codes G_{i} associated with g_{i} satisfy G(x) ≤ G_{i} (x) + logN/N_{i} and G(x,y) ≤ G_{i} (x,y) + logN/N_{i}. Therefore, substituting G(x,y) by G_{i} (x,y) + logN/N_{i} in (??), we obtain
NGD (x,y) ≤ 
 . (6) 
Markov’s Inequality says the following: Let p be any probability mass function; let f be any nonnegative function with pexpected value E = ∑_{i} p(i) f(i) < ∞. For E > 0 we have ∑_{i} { p(i): f(i)/E > k } < 1/k.
(i) Define a probability mass function p(i) = N_{i}/N for i ∈ A. For every x ∈ S, the Google probability mass value g(x) equals the expectation of the individual Google probability mass values at x, that is, ∑_{i ∈ A} p(i) g_{i}(x). We can now argue as follows: for every x as above, we can consider x fixed and use “g_{i}(x)” for “f(i)”, and “g(x)” for “E” in Markov’s Inequality. Then, ∑_{i} { p(i): g_{i}(x)/g(x) > k } < 1/k, and therefore ∑_{i} { p(i): g_{i}(x)/g(x) ≥ k } > 1− 1/k. Hence, there is a xdependent subset of web authors B_{x} ={i ∈ A: g_{i}(x)/g(x) ≥ k}, such that ∑{p(i): i ∈ B} > 1 − 1/k and by definition g_{i}(x) ≥ k g(x) for i ∈ B_{x}. Then, for i ∈ B_{x}, we have G_{i}(x) − logk ≤ G (x). Substitute G_{i}(x) − logk for G (x), with probability >(1−1/k) that G_{i}(x)−logk ≤ G(x) for web author i that authored a web page that is selected uniformly at random from the set Ω of all web pages. Recall that ∑{p(i): i ∈ B_{x}}= ∑{N_{i}: i ∈ B_{x}}/N. Similarly this holds for search term y with respect to the equivalently defined B_{y}. The combination of search terms x and y therefore satisfy both G_{i}(x)−logk ≤ G(x) and G_{i}(y)−logk ≤ G(y), for i ∈ B with B= B_{x} ∩ B_{y}. Then, ∑{p_{i}: i ∈ B }= ∑{N_{i}: i ∈ B}/N ≥ ∑{N_{i}: i ∈ B_{x}}/N × ∑{N_{i}: i ∈ B_{y}}/N > (1−1/k)^{2}. Hence, the probability that both search terms x and y satisfy G_{i}(x)−logk ≤ G(x) and G_{i}(y)−logk ≤ G(y), respectively, for web author i that authored a web page selected uniformly at random from Ω, is greater than (1−1/k)^{2}. Substitute both G(x) and G(y) according to these probabilistically satisfied inequalities in (??), both in the maxterm in the numerator, and in the minterm in the denominator. This proves item (i).
(ii) Fix web author i ∈ A. We consider the conditional probability mass functions g′(x)=g(x  x ∈ S) and g′_{i}(x)=g_{i}(x  x ∈ S) over single search terms: The g′_{i}expected value of g′ (x)/g′_{i}(x) is
 g′_{i}(x) 
 ≤ 1. 
 { g′_{i}(x): g′ (x) ≤ j g′_{i} (x) } > 1 − 
 . (7) 
Let ∑_{x ∈ S} g(x)=h and ∑_{x ∈ S} g_{i}(x)=h_{i}. Since the probability of an event of a doubleton set of search terms is not greater than that of an event based on either of the constituent search terms, 1 ≥ h,h_{i} ≥ 1/2. Therefore, 2g(x) ≥ g′(x) ≥ g(x) and 2g_{i}(x) ≥ g′_{i} (x) ≥ g_{i}(x). Then, for the search terms x satisfying (??), we have
 { g_{i}(x): g (x) ≤ 2j g_{i} (x) } > 1 − 
 . 
For the x’s with g(x) ≤ 2j g_{i}(x) we have G_{i}(x) ≤ G (x) + log2j. Substitute G_{i}(x) − log2j for G (x) (there is g_{i}probability ≥ 1−1/j that G_{i}(x)−log2j ≤ G(x)) and G_{i}(y)−log2j ≤ G(y) in (??), both in the maxterm in the numerator, and in the minterm in the denominator. Noting that the two g_{i}probabilities (1−1/j) are independent, the total g_{i}probability that both substitutions are justified is at least (1−1/j)^{2}. Substituting k=2j proves item (ii).
Therefore, the Google normalized distance minorizes every normalized compression distance based on a particular user’s generated probabilities of search terms, with high probability up to an error term that in typical cases is ignorable.
In our first experiment, we seek to verify that Google page counts capture something more than meaningless noise. For simplicity, we do not use NGD here, but instead look at just the Google probabilities of small integers in several formats. The first format we use is just the standard numeric representation using digits, for example “43”. The next format we use is the number spelled out in English, as in “forty three”. Then we use the number spelled in Spanish, as in “cuarenta y tres”. Finally, we use the number as digits again, but now paired with the fixed and arbitrary search term green . In each of these examples, we compute the probability of search term x as f(x)/M. Here, f(x) represents the count of webpages containing search term x. We plotted log(f(x)/M) against x in Figure ?? for x runs from 1 to 120. Notice that numbers such as even multiples of ten and five stand out in every representation in the sense that they have much higher frequency of occurrence. We can treat only low integers this way: integers of the order 10^{23} mostly do not occur since there are not web pages enough to represent a noticeable fraction of them (but Avogadro’s number 6.022 × 10^{23} occurs with high frequency both in letters and digits).
Visual inspection of the plot gives clear evidence that there is a positive correlation between every pair of formats. We can therefore assume that that there is some underlying structure that is independent of the language chosen, and indeed the same structure appears even in the restricted case of just those webpages that contain the search term green .
Before explaining our primary NGD results, a few implementation details should be clarified. When entering searches in Google, a rich syntax is available whereby searches may be precisely constrained, see []. We use two important features. If you enter the term every generation in Google, it counts precisely the number of pages that contain both the word every and the word generation, but not necessarily consecutively like every generation. If you instead enter "every generation", then this tells Google that both words must appear consecutively. Another feature that is important is the + modifier. Google ignores common words and characters such as “where” and “how”, as well as certain single digits and single letters. Prepending a + before a searchterm indicates that every result must include the following term, even if it is a term otherwise ignored by Google. Experiments show that every generation and +"every" +"generation" give slightly different results, say 17,800,000 against 17,900,000. Some other experiments show, that whatever the Google manual says, the form horse rider is slightly sensitive to adding spaces, while +"horse" +"rider" is not. Therefore, we only use the latter form. Our translation from a tuple of search terms into a Google search query proceeds in three steps: First we put doublequotes around every search term in the tuple. Next, we prepend a + before every term. Finally, we join together each of the resultant strings with a single space. For example, when using the search terms “horse” and “rider”, it is converted to the Google search query +"horse" +"rider".
Another detail concerns avoiding taking the logarithm of 0. Although our theory conveniently allows for ∞ in this case, our implementation makes a simplifying compromise. When returning f(x) for a given search, we have two cases. If the number of pages returned is nonzero, we return twice this amount. If the pages is equal to 0, we do not return 0, but instead return 1. Thus, even though a page does not exist in the Google index, we credit it half the probability of the smallest pages that do exist in Google. This greatly simplifies our implementation and seems not to result in much distortion in the cases we have investigated.
In this chapter we give three applications of the Google method: unsupervised learning in the form of hierarchical clustering, supervised learning using support vector machines, and matching using correlation. For the hierarchical clustering method we refer to Section ??. and the correlation method is well known. For the supervised learning, several techniques are available. For the SVM method used in this thesis, we refer to the excellent exposition [], and give a brief summary in Appendix ??.
For these examples, we used our software tool available from http://complearn.sourceforge.net/, the same tool that has been used in other chapters to construct trees representing hierarchical clusters of objects in an unsupervised way. However, now we use the normalized Google distance (NGD ) instead of the normalized compression distance (NCD ). Recapitulating, the method works by first calculating a distance matrix using NGD among all pairs of terms in the input list. Then it calculates a bestmatching unrooted ternary tree using a novel quartetmethod style heuristic based on randomized hillclimbing using a new fitness objective function optimizing the summed costs of all quartet topologies embedded in candidate trees.
In the first example, the objects to be clustered are search terms consisting of the names of colors, numbers, and some tricky words. The program automatically organized the colors towards one side of the tree and the numbers towards the other, Figure ??. It arranges the terms which have as only meaning a color or a number, and nothing else, on the farthest reach of the color side and the number side, respectively. It puts the more general terms black and white, and zero, one, and two, towards the center, thus indicating their more ambiguous interpretation. Also, things which were not exactly colors or numbers are also put towards the center, like the word “small”. We may consider this an example of automatic ontology creation.
In the example of Figure ??, the names of fifteen paintings by Steen, Rembrandt, and Bol were entered. The names of the associated painters were not included in the input, however they were added to the tree display afterword to demonstrate the separation according to painters. This type of problem has attracted a great deal of attention []. A more classical solution is offered in [], where a domainspecific database is used for similar ends. The present automatic oblivious method obtains results that compare favorably with the latter featuredriven method.
In the example of Figure ??, several Chinese names were entered. The tree shows the separation according to concepts like regions, political parties, people, etc. See Figure ?? for English translations of these characters. This figure also shows a feature of the CompLearn system that has not been encountered before: the CompLearn system can draw dotted lines with numbers inbetween each adjacent node along the perimeter of the tree. These numbers represent the NCD distance between adjacent nodes in the final (ordered tree) output of the CompLearn system. The tree is presented in such a way that the sum of these values in the entire ring is minimized. This generally results in trees that makes the most sense upon initial visual inspection, converting an unordered binary tree to an ordered one. This feature allows for a quick visual inspection around the edges to determine the major groupings and divisions among coarse structured problems. It grew out of an idea originally suggested by Lloyd Rutledge at CWI [].
We augment the Google method by adding a trainable component of the learning system. This allows us to consider classification rather than clustering problems. Here we use the Support Vector Machine (SVM) as a trainable component. For a brief introduction to SVM’s see Section ??. We use LIBSVM software for all of our SVM experiments.
The setting is a binary classification problem on examples represented by search terms. We require a human expert to provide a list of at least 40 training words, consisting of at least 20 positive examples and 20 negative examples, to illustrate the contemplated concept class. The expert also provides, say, six anchor words a_{1}, … , a_{6}, of which half are in some way related to the concept under consideration. Then, we use the anchor words to convert each of the 40 training words w_{1} , … , w_{40} to 6dimensional training vectors v_{1} , … , v_{40}. The entry v_{j,i} of v_{j} = (v_{j,1}, … , v_{j,6}) is defined as v_{j,i} = NGD (w_{i}, a_{j}) (1 ≤ i ≤ 40, 1 ≤ j ≤ 6). The training vectors are then used to train an SVM to learn the concept, and then test words may be classified using the same anchors and trained SVM model. We present all positive examples as xdata (input data), paired with y=1. We present all negative examples as xdata, paired with y = −1.
In the next example, Figure ??, we trained using a list of emergencies as positive examples, and a list of “almost emergencies” as negative examples. The figure is selfexplanatory. The accuracy on the test set is 75%.
In Figure ?? the method learns to distinguish prime numbers from nonprime numbers by example:
The prime numbers example illustrates several common features of our method that distinguish it from the strictly deductive techniques. It is common for our classifications to be good but imperfect, and this is due to the unpredictability and uncontrolled nature of the Google distribution.
To create the next example, we used WordNet. WordNet is a semantic concordance of English. It also attempts to focus on the meaning of words instead of the word itself. The category we want to learn, the concept, is termed “electrical”, and represents anything that may pertain to electronics, Figure ??. The negative examples are constituted by simply everything else. Negative samples were chosen randomly and uniformly from a dictionary of English words. This category represents a typical expansion of a node in the WordNet hierarchy. The accuracy on the test set is 100%: It turns out that “electrical terms” are unambiguous and easy to learn and classify by our method.
In the next example, Figure ??, the concept to be learned is “religious”. Here the positive examples are terms that are commonly considered as pertaining to religious items or notions, the negative examples are everything else. The accuracy on the test set is 88.89%. Religion turns out to be less unequivocal and unambiguous than “electricity” for our method.
Notice that what we may consider to be errors, can be explained, or point at, a secondary meaning or intention of these words. For instance, some may consider the word “shepherd” to be full of religious connotation. And there has been more than one religion that claims to involve “earth” as a component. Such examples suggest to use the method for exploratory semantics: establishing less common, idiosyncratic, or jargon meaning of words.
The previous examples show only a few handcrafted special cases. To investigate the more general statistics, a method was devised to estimate how well the NGD GoogleSVM approach agrees with WordNet in a large number of automatically selected semantic categories. Each automatically generated category followed the following sequence.
First we must review the structure of WordNet; the following is paraphrased from the official WordNet documentation available online. WordNet is called a semantic concordance of the English language. It seeks to classify words into many categories and interrelate the meanings of those words. WordNet contains synsets. A synset is a synonym set; a set of words that are interchangeable in some context, because they share a commonlyagreed upon meaning with little or no variation. Each word in English may have many different senses in which it may be interpreted; each of these distinct senses points to a different synset. Every word in WordNet has a pointer to at least one synset. Each synset, in turn, must point to at least one word. Thus, we have a manytomany mapping between English words and synsets at the lowest level of WordNet. It is useful to think of synsets as nodes in a graph. At the next level we have lexical and semantic pointers. Lexical pointers are not investigated in this thesis; only the following semantic pointer types are used in our comparison: A semantic pointer is simply a directed edge in the graph whose nodes are synsets. The pointer has one end we call a source and the other end we call a destination. The following relations are used:
Using these semantic pointers we may extract simple categories for testing. First, a random semantic pointer (or edge) of one of the types above is chosen from the WordNet database. Next, the source synset node of this pointer is used as a sort of root. Finally, we traverse outward in a breadthfirst order starting at this node and following only edges that have an identical semantic pointer type; that is, if the original semantic pointer was a hyponym, then we would only follow hyponym pointers in constructing the category. Thus, if we were to pick a hyponym link initially that says a tiger is a cat, we may then continue to follow further hyponym relationships in order to continue to get more specific types of cats. See the WordNet homepage [] documentation for specific definitions of these technical terms. For examples of each of these categories consult the experiments listed in the Appendix at [].
Once a category is determined, it is expanded in a breadth first way until at least 38 synsets are within the category. 38 was chosen to allow a reasonable amount of training data to be presented with several anchor dimensions, yet also avoiding too many. Here, Bernie’s Rule^{1} is helpful: it states that the number of dimensions in the input data must not exceed one tenth the number of training samples. If the category cannot be expanded this far, then a new one is chosen. Once a suitable category is found, and a set of at least 38 members has been formed, a training set is created using 25 of these cases, randomly chosen. Next, three are chosen randomly as anchors. And finally the remaining ten are saved as positive test cases. To fill in the negative training cases, random words are chosen from the WordNet database. Next, three random words are chosen as unrelated anchors. Finally, 10 random words are chosen as negative test cases.
For each case, the SVM is trained on the training samples, converted to 6dimensional vectors using NGD . The SVM is trained on a total of 50 samples. The kernelwidth and errorcost parameters are automatically determined using fivefold cross validation. Finally testing is performed using 20 examples in a balanced ensemble to yield a final accuracy.
There are several caveats with this analysis. It is necessarily rough, because the problem domain is difficult to define. There is no protection against certain randomly chosen negative words being accidentally members of the category in question, either explicitly in the greater depth transitive closure of the category, or perhaps implicitly in common usage but not indicated in WordNet. In several cases, such as “radio wave” and “DC” in the “big science” experiment, there appears to be an arguable case to support the computer’s classification in cases where this phenomenon occurs. Another detail to notice is that WordNet is available through some web pages, and so undoubtedly contributes something to Google pagecounts. Further experiments comparing the results when filtering out WordNet images on the web suggest that this problem does not usually affect the results obtained, except when one of the anchor terms happens to be very rare and thus receives a nonnegligible contribution towards its page count from WordNet views. In general, our previous NCD based methods, as in [], exhibit largegranularity artifacts at the low end of the scale; for small strings we see coarse jumps in the distribution of NCD for different inputs which makes differentiation difficult. With the Googlebased NGD we see similar problems when page counts are less than a hundred.
We ran 100 experiments. The actual data are available at []. A histogram of agreement accuracies is shown in Figure ??. On average, our method turns out to agree well with the WordNet semantic concordance made by human experts. The mean of the accuracies of agreements is 0.8725. The variance is ≈ 0.01367, which gives a standard deviation of ≈ 0.1169. Thus, it is rare to find agreement less than 75%. These results confirm that we are able to perform a rudimentary form of generalization within a conceptual domain programmatically using Google. For handcrafted examples it performed comparably, and so this suggests that there may be latent semantic knowledge. Is there a way to use it?
Yet another potential application of the NGD method is in natural language translation. (In the experiment below we do not use SVM’s to obtain our result, but determine correlations instead.) Suppose we are given a system that tries to infer a translationvocabulary among English and Spanish. Assume that the system has already determined that there are five words that appear in two different matched sentences, but the permutation associating the English and Spanish words is, as yet, undetermined. This setting can arise in real situations, because English and Spanish have different rules for wordordering. Thus, at the outset we assume a preexisting vocabulary of eight English words with their matched Spanish translation. Can we infer the correct permutation mapping the unknown words using the preexisting vocabulary as a basis? We start by forming an NGD matrix using additional English words of which the translation is known, Figure ??. We label the columns by the translationknown English words, the rows by the translationunknown words. The entries of the matrix are the NGD ’s of the English words labeling the columns and rows. This constitutes the English basis matrix. Next, consider the known Spanish words corresponding to the known English words. Form a new matrix with the known Spanish words labeling the columns in the same order as the known English words. Label the rows of the new matrix by choosing one of the many possible permutations of the unknown Spanish words. For each permutation, form the NGD matrix for the Spanish words, and compute the pairwise correlation of this sequence of values to each of the values in the given English word basis matrix. Choose the permutation with the highest positive correlation. If there is no positive correlation report a failure to extend the vocabulary. In this example, the computer inferred the correct permutation for the testing words, see Figure ??.
A comparison can be made with the Cyc project []. Cyc, a project of the commercial venture Cycorp, tries to create artificial common sense. Cyc’s knowledge base consists of hundreds of microtheories and hundreds of thousands of terms, as well as over a million handcrafted assertions written in a formal language called CycL []. CycL is an enhanced variety of first order predicate logic. This knowledge base was created over the course of decades by paid human experts. It is therefore of extremely high quality. Google, on the other hand, is almost completely unstructured, and offers only a primitive query capability that is not nearly flexible enough to represent formal deduction. But what it lacks in expressiveness Google makes up for in size; Google has already indexed more than eight billion pages and shows no signs of slowing down.
In the case of contextfree statistical compression such as gzip , we are trying to approximate the Kolmogorov complexity of a string. Another way of describing the calculation is to view it as determining a probability mass function (viewing the compressed string as ShannonFano code, Section ??), approximating the universal distribution, that is, the negative exponential of the Kolmogorov complexity []. The universal probability of a given string can equivalently be defined as the probability that the reference universal Turing machine outputs the string if its input program is generated by fair coin flips. In a similar manner, we can associate a particular ShannonFano code, the Google code, with the Google probability mass function. Coding every search term by its Google code, we define a “Google compressor.” Then, in the spirit of Section ??, we can view the Google probability mass function as a universal distribution for the individual Google probability mass functions generated by the individual web authors, substituting “web authors” for “Turing machines”.
The GoogleSVM method does not use an individual word in isolation, but instead uses an ordered list of its NGD relationships with fixed anchors. This then removes the possibility of attaching to the isolated (contextfree) interpretation of a literal term. That is to say, the inputs to our SVM are not directly search terms, but instead an image of the search term through the lens of the Google distribution, and relative to other fixed terms which serve as a grounding for the term. In most schools of ontological thought, and indeed in the WordNet database, there is imagined a twolevel structure that characterizes language: a manytomany relationship between wordforms or utterances and their many possible meanings. Each link in this association will be represented in the Google distribution with strength proportional to how common that usage is found on the web. The NGD then amplifies and separates the many contributions towards the aggregate page count sum, thereby revealing some components of the latent semantic web. In almost every informal theory of cognition we have the idea of connectedness of different concepts in a network, and this is precisely the structure that our experiments attempt to explore.
The Google distribution is a comparable notion, in the context of the worldwideweb background information, to the universal distribution: The universal distribution multiplicatively dominates all other distributions in that it assigns a higher weight to some elements when appropriately scaled. This suggests that it covers everything without bound. Google surely represents the largest publiclyavailable single corpus of aggregate statistical and indexing information so far created. Only now has it been cheap enough to collect this vast quantity of data, and it seems that even rudimentary analysis of this distribution yields a variety of intriguing possibilities. One of the simplest avenues for further exploration must be to increase training sample size, because it is wellknown that SVM accuracy increases with training sample size. It is likely that this approach can never achieve 100% accuracy like in principle deductive logic can, because the Google distribution mirrors humankind’s own imperfect and varied nature. But it is also clear that in practical terms the NGD can offer an easy way to provide results that are good enough for many applications, and which would be far too much work if not impossible to program in a foolproof deductive way.
We have demonstrated that NGD can be used to extract meaning in a variety of ways from the statistics inherent to the Google database. So far, all of our techniques look only at the page count portion of the Google result sets and achieve surprising results. How much more amazing might it be when the actual contents of search results are analyzed? Consider the possibility of using WordNet familiarity counts to filter returned search results to select only the least familiar words, and then using these in turn as further inputs to NGD to create automatic discourse or concept diagrams with arbitrary extension. Or perhaps this combination can be used to expand existing ontologies that are only seeded by humans. Let us list some of the future directions and potential application areas:
We thank Teemu Roos, Hannes Wettig, Petri Mylliymaki, and Henry Tirri at COSCO and The Helsinki Institute for Information Technology for interesting discussions. We also thank ChihJen Lin and his excellent group for providing and supporting vigorously, free of charge to all, the very easy to use LIBSVM package. We thank the Cognitive Science Laboratory at Princeton University for providing the wonderful and free WordNet database. And we wish to thank the staff of Google, Inc. for their delightful support of this research by providing an API as well as generous access to their websearch system.
Stemmatology studies relations among different variants of a text that has been gradually altered as a result of imperfectly copying the text over and over again. This chapter presents a new method for using this assumption to reconstruct a lineage tree explicating the derivational relationships among the many variations, just as we might reconstruct an evolutionary tree from a set of gene sequences. We propose a new computer assisted method for stemmatic analysis based on compression of the variants. We will provide an overview of the chapter at the end of the next section. The method is related to phylogenetic reconstruction criteria such as maximum parsimony and maximum likelihood. We apply our method to the tradition of the legend of St. Henry of Finland, and report encouraging preliminary results. The obtained family tree of the variants, the stemma, corresponds to a large extent with results obtained with more traditional methods. Some of the identified groups of manuscripts are previously unrecognized ones. Moreover, due to the impossibility of manually exploring all plausible alternatives among the vast number of possible trees, this work is the first attempt at a complete stemma for the legend of St. Henry. The used methods are being released as opensource software, and are entirely distinct from the CompLearn system. They are presented here only for rough comparison.
St. Henry, according to the medieval tradition Bishop of Uppsala (Sweden) and the first Bishop of Finland, is the key figure of the Finnish Middle Ages. He seems to have been one of the leaders of a Swedish expedition to Finland probably around 1155. After this expedition Henry stayed in Finland with sad consequences: he was murdered already next year. He soon became the patron saint of Turku cathedral and of the bishopric covering the whole of Finland. He remained the only ‘local’ one of the most important saints until the reformation. Henry is still considered to be the Finnish national saint. The knowledge of writing was almost totally concentrated into the hands of the Church and the clergymen during the early and high Middle Ages. On the other hand, the official and proper veneration of a saint needed unavoidably a written text containing the highlights of the saint’s life and an account of his miracles to be recited during the services in the church. The oldest text concerning St. Henry is his legend written in Latin. It contains both his life and a collection of his miracles and seems to have been ready by the end of the 13th century at the very latest. The text is the oldest literary work preserved in Finland and can thus be seen as the starting point of the Finnish literary culture. Whereas the influence of St. Henry on the Christianization of Finland has been one of the focusing points of the Finnish and Swedish medievalists for hundreds of years, only the most recent research has really concentrated on his legend as a whole. According to the latest results, the Latin legend of St. Henry is known in 52 different medieval versions preserved in manuscripts and incunabula written in the early 14th–early 16th centuries (Fig. ??).^{1}
The reasons for such a substantial amount of versions differing from each other are several. On one hand, the texts were copied by hand until the late 15th and early 16th centuries, which resulted in a multitude of unintended scribal errors by the copyists. In addition, the significance of the cult of St. Henry varied considerably from one part of the Latin Christendom to the other. In the medieval bishopric of Turku covering the whole of medieval Finland St. Henry was venerated as the most important local saint, whose adoration required the reciting of the whole legend during the celebrations of the saint’s day. In Sweden, for instance, St. Henry was not so important a saint, which led to different kinds of abridgments fitted into the needs of local bishoprics and parishes. As a consequence, the preserved versions of the legend are all unique.
With the aid of traditional historically oriented auxiliary sciences like codicology and paleography it is possible to find out — at least roughly — where and when every version was written. Thus, the versions form a pattern representing the medieval and later dissemination of the text. Even if the existent manuscripts containing the different versions represent but a tiny part of the much larger number of manuscripts and versions written during the Middle Ages, they still provide us with an insight into a variety of aspects of medieval culture. The versions help to reconstruct the actual writing process and the cultural ties that carried the text from one place to another. When one combines the stemma — i.e. the family tree — of a text with a geographical map and adds the time dimension, one gets important information that no single historical source can ever provide a historian with. The potential of this kind of an approach is emphasized when researching hagiographical texts — i.e. saints’ lives, for instance — since they were the most eagerly read and most vastly disseminated literary genre of the Middle Ages.
Taking into consideration the possibilities of stemmatology, it is not surprising that the historians and philologists have tried to establish a reliable way to reconstruct the stemma of the text and its versions for centuries. The main difficulty has been the great multitude of textual variants that have to be taken into consideration at the same time. An example from the legend material of St. Henry shall elucidate the problems: there are over 50 manuscripts and incunabula to be taken into consideration; in the relatively short text there are nearly one thousand places where the versions differ from each other. Since the multitude of variants rises easily to tens of thousands, it has been impossible for researchers using traditional methods of paper and pen to form the stemma and thus get reliable answers to the questions related to the writing and disseminating of the text. There have been some previous attempts to solve the problems of stemmatology with the aid of computer science. In addition, the powerful computer programs developed for the needs of the computer aided cladistics in the field of evolutionary biology have been used. In many cases this has proven to be a fruitful approach, extending the possibilities of stemmatics to the analysis of more complex textual traditions that are outside the reach of manual analysis. Moreover, formalizing the often informal and subjective methods used in manual analysis makes the methods and results obtained with them more transparent and brings them under objective scrutiny. Still, many issues in computer assisted stemmatic analysis remain unsolved, underlining the importance of advances towards general and reliable methods for shaping the stemma of a text.
Overview of this Chapter: The chapter is organized as follows: In Section ?? we present a criterion for stemmatic analysis that is based on compression of the manuscripts. We then outline an algorithm, in Section ??, that builds stemmata by comparing a large number of treeshaped stemmata and choosing the one that minimizes the criterion. The method is demonstrated on a simple example in Section ??, where we also present our main experiment using some 50 variants of the legend of St. Henry, and discuss some of the restrictions of the method and potential ways to overcome them. Conclusions are presented in Section ??. We also compare our method to a related method in the CompLearn package in Appendix A.
One of the most applied methods in biological phylogeny is socalled maximum parsimony. A maximally parsimonious tree minimizes the total number of differences between connected nodes — i.e., species, individuals, or manuscripts that are directly related — possibly weighted by their importance. Stemmatology analysis is based on variable readings that result from unintentional errors in copying or intentional omissions, insertions, or other modifications. In his seminal work on computer assisted stemmatology, O’Hara used a parsimony method of the PAUP software [] in Robinson’s Textual Criticism challenge []. For further applications of maximum parsimony and related method, see [, , , ] and references therein.
Our compressionbased minimum information criterion shares many properties of the very popular maximum parsimony method. Both can also be seen as instances of the minimum description length (MDL) principle of Rissanen [] — although this is slightly anachronistic: the maximum parsimony method predates the more general MDL principle — which in turn is a formal version of Occam’s razor. The underlying idea in the minimum information criterion is to minimize the amount of information, or codelength, required to reproduce all the manuscripts by the process of copying and modifying the text under study. In order to describe a new version of an existing manuscript, one needs an amount of information that depends on both the amount and the type of modifications made. For instance, a deletion of a word or a change of word order requires less information to describe compared to introducing a completely new expression. In order to be concrete, we need a precise, numerical, and computable measure for the amount of information. The commonly accepted definition of the amount information in individual objects is Kolmogorov complexity [, ], defined as the length of the shortest computer program to describe the given object, as explained in Chapter 3. However, Kolmogorov complexity is defined only up to a constant that depends on the language used to encode programs, and what is more, fundamentally uncomputable. In the spirit of a number of earlier authors [, , , , , , ] we approximate Kolmogorov complexity by using a compression program, also as we did in previous chapters. Currently, we use gzip based on the LZ77 [] algorithm, and plan to experiment with other compressors in subsequent work. In particular, given two strings, x and y, the amount of information in y conditional on x, denoted by C(y∣ x) is given by the length of the compressed version of the concatenated string x,y minus the length of the compressed version of x alone^{2}. A simple example illustrating these concepts is given below in Section ??.
In addition to the MDL interpretation, our method can be seen as (an approximation of) maximum likelihood, another commonly used criterion in phylogeny. The maximum likelihood criterion requires that we have a probabilistic model for evolution, assigning specific probabilities for each kind of change. The joint likelihood of the whole graph is then evaluated as a product of likelihoods of the individual changes. The tree achieving the highest joint likelihood given the observed data is then preferred. In the case of manuscripts such a model is clearly more difficult to construct that in biology, where the probabilities of mutation can be estimated from experimental data. Nevertheless, a model for manuscript evolution is presented in []. Codelength is isomorphic to (behaves in the same way as) likelihood: sums of codelengths have a direct correspondence with products of likelihoods. If the probability induced by the information cost, 2^{−C(y∣ x)}, is approximately proportional to the likelihood of creating a copy y based on the original x, then minimizing the total information cost approximates maximizing the likelihood.
Let G = (V,E) be an undirected graph where V is a set of nodes corresponding to the text variants, E ⊂ V × V is a set of edges. We require that the graph is a connected bifurcating tree, i.e., that (i) each node has either one or three neighbors, and (ii) the tree is acyclic. Such a graph G can be made directed by picking any one of the nodes as a root and directing each edge away from the root. Given a directed graph G^{→}, the total information cost of the tree is given by

where Pa(v) denotes the parent node of v unless v is the root in which case Pa(v) is the empty string. Assuming that order has no significant effect on the complexity of a concatenated string, i.e., we have C(x,y) ≈ C(y,x), as seems to be the case in our data, it can easily verified that for acyclic bifurcating trees, the above can rewritten as
C(G) ≈ 
 C(v,w) − 2 
 C(v), (2) 
where the first summation has a term for each edge in the graph, and the second summation goes over the set of interior nodes V_{I}. The formula is a function of the undirected structure G only: the choice of the root is irrelevant. The factor two in the latter term comes from using bifurcating trees.
For practical reasons we make three modifications to this criterion. First, as we explain in the next section, due to algorithmic reasons we need to splice the texts in smaller segments, not longer than roughly 10–20 words (we used 11). Secondly, we found that the cost assigned by gzip to reproducing an identical copy of a string is too high in the sense that it is sometimes ‘cheaper’ to omit a large part of the text for a number of generations and to reinvent it later in an identical form. Therefore we define the cost of making an identical copy to be zero. Thirdly, it is known that the variation between an ampersand (’&’) and the word et, and the letters v and u was mostly dependent on the style of the copyist and changed with time and region, and thus, bears little information relevant to stemmatic analysis. This domain knowledge was taken into account by replacing, in both of the above cases, all occurrences of the former by the latter^{3}. Thus, we use the following modified cost function
C′( 
 ) = 

 C′(v_{i} ∣ Pa_{i}(v)), (3) 
where n is the number of segments into which each text is spliced, v_{i} and Pa_{i}(v) are the ith segment of variant v and its parent, respectively, all strings are modified according to the above rules (ampersand to et, and v to u), and C′(x ∣ y) equals the gzip cost if x and y differ, and zero otherwise. This modified cost also allows a form similar to (??) and hence, is practically independent of the choice of the root.
Since it is known that many of the text variants have been lost during the centuries between the time of the writing of the first versions and present time, it is not realistic to build a tree of only the about 50 variants that we have as our data. This problem is even more prominent in biology where we can only make observations about organisms that still exist (excluding fossil evidence). The common way of handling this problem is to include in the tree a number of ‘hidden’ nodes, i.e., nodes representing individuals whose characteristics are unobserved. We construct bifurcating trees that have N observed nodes as leafs, and N−2 hidden nodes as the interior nodes.
Evaluating the criterion (??) now involves the problem of dealing with the hidden nodes. Without knowing the values of Pa_{i}(v), it is not possible to compute C′(v ∣ Pa_{i}(v)). We solve this problem by searching simultaneously for the best tree structure G^{→} and for the optimal contents of the hidden nodes with respect to criterion (??). As mentioned above, we patch up the contents of the interior nodes from segments of length 10–20 words appearing in some of the available variants. In principle we would like to do this on a perwordbasis, which would not be a notable restriction since it is indeed reasonable to expect that a reconstruction only consists of words appearing in the available variants — any other kind of behavior would require rather striking innovation. However, since we evaluate the gzip cost in terms of the segments, it is likely give better values when the segments are longer than one word. Secondly, one of the most common modifications is change in word order. Using 1020 word segments we assign less cost to change in word order than to genuine change of words, unless the change happens to cross a segment border.
Perhaps surprisingly, given a tree structure, finding the optimal contents is feasible. The method for efficiently optimizing the contents of the hidden nodes is an instance of dynamic programming and called ‘the Sankoff algorithm’ [] or ‘the Felsenstein’s algorithm’ []. As Siepel and Haussler [] note, it is in fact an instance of a ‘messagepassing’ or ‘elimination’ algorithm in graphical models (see also []). The basic idea is to maintain for each node a table of minimal costs for the whole subtree starting at the node, given that the contents of the node take any given value. For instance, let us fix a segment, and denote by x^{1}, …, x^{m} the different versions of the segment that appear in some of the observed variants. The minimal cost for the subtree starting at node i, given that the segment in question of node i contains the string x^{j} is given by (see [])
cost_{i}(j) = 
 ⎡ ⎣  C′(x^{k} ∣ x^{j}) + cost_{a}(k)  ⎤ ⎦  + 
 ⎡ ⎣  C′(x^{l} ∣ x^{j}) + cost_{b}(l)  ⎤ ⎦  , 
where a and b are the two children of node i. For leaf nodes the cost is defined as being infinite if j does not match the known content of the node, and zero if j matches or if the content of the node is unknown. Evaluating cost_{i}(j) can be done for each segment independently, starting from the leaf nodes and working towards the root. Finally, the (unconditional) complexity of the root is added so that the minimal cost of the segment is obtained by choosing at the root the string x^{j} that minimizes the sum cost_{root}(j) + C′(x^{j}). The total cost of the tree is then obtained by summing over the minimal costs for each segment. After this, actually filling the contents can be done by propagating back down from the root towards the leafs. It is important to remember that while the algorithm for optimizing the contents of the hidden nodes requires that a root is selected, the resulting cost and the optimal contents of the hidden nodes only depend on the undirected structure (see Eq. (??)).
There still remains the problem of finding the tree structure, which together with corresponding optimal contents of the hidden nodes minimizes criterion (??). The obvious solution, trying all possible tree structures and choosing the best one, fails because for N leafs nodes, the number of possible bifurcating trees is as large as (see [])
1 × 3 × 5 × … × (2N−5). 
For N=52 this number is about 2.73 × 10^{78}, which is close to the estimated number of atoms in the universe. Instead, we have to resort to heuristic search, trying to find as good a tree as possible in the time available.
We use a simulated annealing algorithm which starts with an arbitrary tree and iteratively tries to improve it by small random modification, such as exchanging the places of two subtrees^{4}. Every modification that reduces the value of the criterion is accepted. In order to escape local optima in the search space, modifications that increase the value are accepted with probability
exp  ⎛ ⎜ ⎜ ⎝ 
 ⎞ ⎟ ⎟ ⎠  , 
where C′_{old} is the cost of the current tree, C′_{new} is the cost of the modified tree, and T is a ‘temperature’ parameter that is slowly decreased to zero. In our main experiment, reported in the next section, we ran 1,200,000 iterations of annealing, which we found to be sufficient in our setting.
We first illustrate the behavior of the method by an artificial example in Fig. ??. Assume that we have observed five pieces of text, shown at the tips of the tree’s branches. Because the text is so short, the length of the segment was fixed to one word. One of the trees — not the only one — minimizing the information cost with total cost of 44 units (bytes) is drawn in the figure. Even though, as explained above, the obtained tree is undirected, let us assume for simplicity that the original version is the topmost one (“sanctus henricus ex Anglia”). The sum of the (unconditional) complexities of the four words in this string is equal to 8 + 9 + 3 + 7 = 27, which happens to coincide with the length of the string, including spaces and a finishing newline. The changes, labeled by number 1–5 in the figure, yield 5 + 3 + 3 + 3 + 3 = 17 units of cost. Thus the total cost of the tree equals 27 + 17 = 44 units.
As our main experiment, we analyzed a set of 49 variants of the legend of St. Henry. We had prepared four out of the nine sections (sections 1,4,5, and 6) in a suitable format. Three variants were excluded since they had only ten words or less in the prepared sections. The remaining variants contained 33–379 words each. Table ?? on page ?? lists the estimated time or writing and place of origin, as well as the number of words in the used sections for each manuscript. The best (wrt. the information cost) tree found is shown in Fig. ??. By comparing the tree with earlier results [], it can be seen that many groups of variants have been successfully placed next to each other. For instance, groups of Finnish variants appearing in the tree that are believed to be related are Ho–I–K–T and R–S. Among the printed versions the pairs BA–BS and BLu–BL are correctly identified and also grouped close the each other^{5}. Other pairs of variants appearing in the tree that are believed to be directly related are Li–Q (that are also correctly associated with BA–BS and BL–BLu), JG–B, Dr–M, NR2–JB, LT–E, AJ–D, and Bc–MN–Y. In addition, the subtree including the nine nodes between (and including) BU and Dr is rather well supported by traditional methods. All in all, the tree corresponds very well with relationships discovered with more traditional methods. This is quite remarkable taking into account that in the current experiments we have only used four out of the nine sections of the legend.
In order to quantify confidence in the obtained trees we used on top of our method, blockwise bootstrap [] and a consensus tree program in the phylogeny inference package PHYLIP [], Section ??. One hundred bootstrap samples were generated by sampling (with replacement) n segments out of the n segments that make each manuscript. The compressionbased method described in this work was run on each bootstrap sample — this took about a week of computation — and the resulting 100 trees were analyzed with the consense program in PHYLIP using default settings (modified majority rule). The resulting consensus tree is shown in Fig. ??.
It should be noted that the central node with nine neighbors does not corresponds to a single manuscript with nine descendants, but rather, that the relationships between the nine subtrees is unidentified. Because the interpretation of the consensus tree is less direct than the interpretation of the tree in Fig. ?? as the family tree of the variants, it is perhaps best to use the consensus tree to quantify the confidence in different parts of the tree in Fig. ??. For instance, it can be seen that the pairs BL–BLu, AJ–D, Li–Q, NR2–JB, O–P, L–G, JG–B, and R–S are well supported. More interestingly, The group Ho–I–K–T–A is organized in a different order in Fig. ?? and the consensus tree. This group also illustrates one of the problems in the consensus tree method. Namely the confidence in contiguous groups that are in the middle of the tree tends to be artificially low since the group does not make up a subtree, in this case only 3/100 (Fig. ??).
The following potential problems and sources of bias in the resulting stemmata are roughly in decreasing order of severity:
In future work we plan to investigate ways to overcome some of these limitations, to carry out more experiments with more data in order to validate the method and to compare the results with those obtained with, for instance, the existing methods in CompLearn [], PHYLIP [], and PAUP []. We are also planning to release the software as a part of the CompLearn package. Among the possibilities we have not yet explored is the reconstruction of a likely original text. In fact, in addition to the stemma, the method finds an optimal — i.e., optimal with respect to the criterion — history of the manuscript including a text version at each branching point of the stemma. Assuming a point of origin, or a root, in the otherwise undirected stemma tree, thus directly suggests a reconstruction of the most original version.
We proposed a new compressionbased criterion, and an associated algorithm for computer assisted stemmatic analysis. The method was applied to the tradition of the legend of St. Henry of Finland, of which some fifty manuscripts are known. Even for such a moderate number, manual stemma reconstruction is prohibitive due to the vast number of potential explanations, and the obtained stemma is the first attempt at a complete stemma of the legend of St. Henry. The relationships discovered by the method are largely supported by more traditional analysis in earlier work, even though we have thus far only used a part of the legend in our experiments. Moreover, our results have pointed out groups of manuscripts not noticed in earlier manual analysis. Consequently, they have contributed to research on the legend of St. Henry carried out by historians and helped in forming a new basis for future studies. Trying to reconstruct the earliest version of the text and the direction of the relationships between the nodes in the stemma is an exciting line of research where a combination of stemmatological, palaeographical, codicological and content based analysis has great potential.
The CompLearn package [] (Section ??) performs similar analysis as our method in a more general context where the strings need not consist of wordbyword aligned text. Recall that it is based on the Normalized Compression Distance (NCD) defined as in (??), for convenience restated,
NCD(x,y) = 
 , 
that was developed and analyzed in [, , , , ] (Chapter 3). Both our minimum information criterion and NCD are based on (approximations of) Kolmogorov complexity. The core method in CompLearn uses a quartet tree heuristic in order to build a bifurcating tree with the observed strings as leafs [] (Chapter 5).
In contrast to our method, where the cost function involves the contents of both the observed strings in the leaves and the unobserved interior nodes, CompLearn only uses the pairwise NCD distances between the observed strings (in [] the latter kind of methods are called distance matrix methods).
The relation between NCD and the criterion presented in this work may be made more clear by considering the sumdistance C(y ∣ x) + C(x ∣ y). Bennett et al. [] show that the sumdistance is sandwiched between the numerator of NCD and two times the same quantity, ignoring logarithmic terms:
max{C(x ∣ y),C(y ∣ x)} ≤ C(y ∣ x) + C(x ∣ y) ≤ 2 max{C(x ∣ y),C(y ∣ x)}. (1) 
Assuming that C(x,y) ≈ C(y,x) for all x,y, the sumdistance yields the cost
 C(w ∣ v) + C(v ∣ w) = 2 
 C(v,w) − 3 
 C(v) − 
 C(w), 
where the summations are over the set of edges E, the set of interior nodes V_{I}, and the set of leaf nodes V_{L}, respectively. Since the set of leaf nodes is constant in the phylogenetic reconstruction problem, the last term can be ignored. Comparing the first two terms with (??) shows that the only difference is in the ratio of the factors of the first two terms (2 : 3 above; 1 : 2 in (??)). Thus, the difference between the the sumdistance and the information cost depends only on the variation of C(v): if all strings are of roughly the same complexity, the difference is small. On the other hand, the difference between the sumdistance and NCD results, up to a factor of two (inequality (??)), from the normalization by max{C(x),C(y)} in NCD . Thus, if all strings are equally complex, the sumdistance and NCD do not differ ‘too much’, which in turn implies, summa summarum, that the information cost and NCD agree, at least roughly. However, in our case, many of the variants are partially destroyed, and consequently the complexity of the existing texts varies. The difference between the quartet tree heuristic and the Sankoffstyle algorithm (Section ??) is more difficult to analyze, but clearly, both are designed for the same purpose.
Figure ?? shows the tree obtained by CompLearn using a blocksort approximation to Kolmogorov complexity (see the documentation of CompLearn for more information). The tree agrees at least roughly in many places with the tree in Fig. ??, for instance, the expected pairs Ho–T, JB–NR2, D–AJ, JG–B, MN–Y, BA–BS, and LT–E are next to or almost next to each other in both trees. We plan to investigate whether the remaining differences between the two trees are due to the cost functions, the search methods, or other features of the methods. At any rate, such agreements corroborate the validity of both methods and provide yet stronger support for the results.
.95@percent Table 8.1. Estimated time of writing and place of origin (alternative place in parentheses) from [], and total number of words in Sections 1,4,5, and 6.  
Code  Time  Place # of Words  
A  1st half of 14th c.  53mmFinland (/Sweden)  364 
Ab  14th c.  Finland  7 
AJ  1416–1442  Vadstena  185 
B  ca. 1460  Cologne  336 
BA  1513  Västeröas  185 
Bc  15th c.  Sweden  250 
BL  1493  Linköping  246 
BLu  1517  Lund  185 
BS  1498  Skara  185 
BSt  1495  Strängnäs  189 
BU  1496  Uppsala  329 
C  14th to 15th c.  Sweden  375 
Cd  15th c.  Sweden (/Finland)  102 
CP  1462–1500  Vadstena  59 
D  1446–1460  Vadstena  181 
De  15th c.  Växjö (/Sweden)  95 
Dr  end of 14th c.  Linköping (/Växjö)  371 
E  1442–1464  Vadstena  237 
Ef  end of 14th c. /  Sweden (/Finland)  82 
beginning of 15th c.  
F  1st half of 15th c.  Vadstena (/Linköping)  339 
Fg  14th c.  Finland (Sweden)  44 
G  1476–1514  Vadstena  251 
Gh  14th c.  Sweden (/Finland)  97 
H  end of 14th c. /  Finland  74 
beginning of 15th c.  
Ho  after 1485  Hollola  371 
I  end of 15th c. /  Ikaalinen  267 
beginning of 16th c.  
JB  1428–1447  Vadstena  166 
JG  ca. 1480  Brussels  341 
K  end of 15th c. /  Kangasala  372 
beginning of 16th c.  
L  15th c.  Sweden  132 
Li  2nd half of 15th c.  Vadstena  193 
LT  1448–1458  Vadstena  266 
Lu  1st half of 14th c.  Sweden  149 
M  1st half of 15th c.  Bishopric of Linköping  228 
MN  1495  Vadstena  372 
N  15th c.  Finland  373 
NR  1476–1514  Vadstena  0 
NR2  after 1489  Vadstena  158 
O  middle 14th c.  Ösmo (/Uppsala)  182 
P  ca. 1380  Strängnäs (/Vadstena)  379 
Q  2nd half of 15th c.,  Bishopric of Linköping  176 
before 1493  (/Vadstena)  
R  15th c.  Finland  267 
S  1st half of 15th c.  Finland  370 
St  beginning of 15th c.  Bishopric of Strängnäs (/Sweden)  211 
T  ca. 1485  Finland  373 
U  15th c.  Uppsala  154 
V  1485  Bishopric of Uppsala  301 
Vae  14th c.  Sweden (/Finland)  247 
Vg  end of 14th c. /  Sweden (/Finland)  33 
beginning of 15th c.  
X  middle or late 15th c.  Bishopric of Uppsala  188 
Y  ca. 1500  Vadstena (/Linköping)  372 
Z  15th c.  Sweden (/Finland)  10 