Information Distance in Multiples
نویسنده
چکیده
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.
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ورودعنوان ژورنال:
- IEEE Trans. Information Theory
دوره 57 شماره
صفحات -
تاریخ انتشار 2011