Abstract :
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.
Keywords :
communication complexity; data mining; information theory; pattern classification; pattern clustering; Kolmogorov complexity; data mining; information distance; parameter-free similarity measure; pattern classification; pattern clustering; pattern recognition; phylogeny; Additives; Color; Complexity theory; Measurement; Pattern recognition; Proposals; Turing machines; Data mining; Kolmogorov complexity; information distance; multiples; pattern recognition; similarity;
