Information distance and its extensions
DS'11 Proceedings of the 14th international conference on Discovery science
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CiE'12 Proceedings of the 8th Turing Centenary conference on Computability in Europe: how the world computes
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Natural Computing: an international journal
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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.