Normalized Information Distance

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dc.creatorVitanyi, Paul M. B.
dc.creatorBalbach, Frank J.
dc.creatorCilibrasi, Rudi L.
dc.creatorLi, Ming
dc.date2008-09-15
dc.date.accessioned2026-07-07T10:02:53Z
dc.date.available2026-07-07T10:02:53Z
dc.descriptionThe normalized information distance is a universal distance measure for objects of all kinds. It is based on Kolmogorov complexity and thus uncomputable, but there are ways to utilize it. First, compression algorithms can be used to approximate the Kolmogorov complexity if the objects have a string representation. Second, for names and abstract concepts, page count statistics from the World Wide Web can be used. These practical realizations of the normalized information distance can then be applied to machine learning tasks, expecially clustering, to perform feature-free and parameter-free data mining. This chapter discusses the theoretical foundations of the normalized information distance and both practical realizations. It presents numerous examples of successful real-world applications based on these distance measures, ranging from bioinformatics to music clustering to machine translation.
dc.description33 pages, 12 figures, pdf, in: Normalized information distance, in: Information Theory and Statistical Learning, Eds. M. Dehmer, F. Emmert-Streib, Springer-Verlag, New-York, To appear
dc.identifierhttps://arxiv.org/abs/0809.2553
dc.identifierhttp://arxiv.org/abs/0809.2553
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/169129
dc.subjectInformation Retrieval
dc.subjectArtificial Intelligence
dc.titleNormalized Information Distance
dc.typetext

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