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Clustering by Compression. Rudi Cilibrasi (CWI), Paul Vitanyi (CWI/UvA). Overview. Input to the software is a set of files Output is a hierarchical clustering shown as an unrooted binary tree This is a case of unsupervised learning (example follows). Process Overview.

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Clustering by compression

Clustering by Compression

Rudi Cilibrasi (CWI), Paul Vitanyi (CWI/UvA)


Overview
Overview

  • Input to the software is a set of files

  • Output is a hierarchical clustering shown as an unrooted binary tree

  • This is a case of unsupervised learning

  • (example follows)


Process overview
Process Overview

  • 1. File translations, if necessary, for example from MIDI to “player-piano” type format.

  • 2. Calculation of Normalized Compression Distance, or NCD.

  • 3. Representation as an unrooted binary tree.


What s unique
What’s Unique?

  • This clustering system is unique in that it can be described as feature-free

  • There are no parameters to tune, and no domain-specific knowledge went into it.

  • Using general-purpose data compressors gives us a parameterized family of features automatically for each domain


Featureless clustering
Featureless Clustering

  • No parameters and no customized features makes it convenient to develop as well as use

  • Since it is based on information-theoretic foundations, it tends to be less brittle than other methods that make considerably more domain-specific assumptions

  • So how does it work?


Midi translation
Midi Translation

  • In order to restrict information entering the algorithm, we removed undesirable MIDI fields such as artist or composer name, headers, and other non-musical data.

  • We keep only the basic MIDI-track decomposition as well as note timing and duration events. We throw away individual note volume.


Gene sequence translation
Gene sequence translation

  • Genetic sequences are represented in ASCII ain four letter alphabets: A,T,G,C

  • Almost no translation at all


Image translation
Image Translation

  • Black and white images are converted to ASCII using spaces for black and # for white

  • Newlines are used to separate rows


Clustering by compression
NCD

  • Once a group of songs has been acquired and translated, a quantity is computed on each pair in the group

  • Normalized Compression Distance measures how different two files are from one another.


Clustering by compression
NCD

  • NCD is based on an earlier idea called Normalized Information Distance.

  • NID uses as compressor a mathematical abstraction called Kolmogorov Complexity, often abbreviated K.

  • K represents a perfect data compressor, and is therefore uncomputable.


Clustering by compression
NCD

  • Since we cannot compute K, we approximate it using real general-purpose file-compressors like gzip, bzip2, winzip, ppmz, and others

  • NCD depends on a particular compressor and NCD with different compressors may give different results for the same pair of objects


Clustering by compression
NCD

  • C(x) means “the compressed size of x”

  • C(xy) means “compressed size of x and y”

  • 0 <= NCD(x,y) <= 1 (roughly)


Clustering by compression
NCD

  • NCD measures how similar or different two strings (or equivalently, files) are.

  • NCD(x,x) = 0, because nothing is different from itself

  • NCD(x,y) = 1 means that x and y are completely unrelated

  • Often less extreme values in real cases


Clustering by compression
NCD

  • Computing NCD of every song with every other song yields a 2-dimensional symmetric distance matrix

  • Next step is transforming this array of distances into something easier to grasp

  • We use the Quartet Method to construct an unrooted binary tree from the NCD matrix


Quartet method
Quartet Method

  • Our algorithm is a slight enhancement of the standard quartet method of tree reconstruction popular for the last 30 years

  • The input is a matrix of distances (NCD)

  • The output is an unrooted binary tree topology where each song is at a leaf and each non-leaf node has exactly three connections.

  • Tree is just one visualization of NCD matrix


Newer developments
Newer developments

  • Since the original Algorithmic Clustering of Music paper, we have since developed further the underlying mathematical formalisms upon which the method is based in a new paper, Clustering by Compression

  • We’ve included experiments from many other areas: biology, astronomy, images…


Current and future work
Current and future work

  • This year, we’ve begun experimenting with automatic conversion from .mp3 (and most other audio formats) to MIDI. This enables us to participate in new emerging spaces

  • We’re investigating alternatives for all stages of this process, to try to understand more about this apparently general machine learning algorithm


New directions
New directions

  • Combination of NCD and Support Vector Machine (SVM) learning for providing scalable generalization in a wide class of domains both musical and otherwise

  • Application of our techniques in real outstanding questions within the musical community


Contact and more info
Contact and more info

  • Related papers and information:

    http://www.cwi.nl/~cilibrar

  • Software: http://complearn.sourceforge.net/

  • Rudi.Cilibrasi@cwi.nl

  • Paul.Vitanyi@cwi.nl

  • Ronald.de.Wolf@cwi.nl