Information Distance in Multiples

Details Information Distance in Multiples Information Distance in Multiples

2009-05-20

arxiv.org/abs/0905.3347v1

Computer Science

Computer Vision and Pattern Recognition

LateX 14 pages, Submitted to a technical journal

Scientific paper

Information distance is a parameter-free similarity measure based on compression, used in pattern recognition, data mining, phylogeny, clustering, and classification. The notion of information distance is extended from pairs to multiples (finite lists). We study maximal overlap, metricity, universality, minimal overlap, additivity, and normalized information distance in multiples. We use the theoretical notion of Kolmogorov complexity which for practical purposes is approximated by the length of the compressed version of the file involved, using a real-world compression program. {\em Index Terms}-- Information distance, multiples, pattern recognition, data mining, similarity, Kolmogorov complexity

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