Information Distance in Multiples

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, universali...

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Veröffentlicht in:IEEE transactions on information theory 2011-04, Vol.57 (4), p.2451-2456
1. Verfasser: VITANYI, Paul M. B
Format: Artikel
Sprache:eng
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Zusammenfassung: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.
ISSN:0018-9448
1557-9654
DOI:10.1109/TIT.2011.2110130