Information Distance in Multiples
Paul M. B. Vitanyi · IEEE Transactions on Information Theory · 2011
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. Index Terms—Data mining, information distance, Kolmogorov complexity, multiples, pattern recognition, similarity.