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Please use this identifier to cite or link to this item: http://eprint.iitd.ac.in/handle/2074/2233

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dc.contributor.authorKrishnapuram, Raghu-
dc.contributor.authorJoshi, Anupam-
dc.contributor.authorNasraoui, Olfa-
dc.contributor.authorLiyu Yi-
dc.date.accessioned2006-10-12T04:37:43Z-
dc.date.available2006-10-12T04:37:43Z-
dc.date.issued2001-
dc.identifier.citationFuzzy Systems, IEEE Transactions on, 9(4), 595 - 607p.en
dc.identifier.urihttp://eprint.iitd.ac.in/dspace/handle/2074/2233-
dc.description.abstractThis paper presents new algorithms-fuzzy c-medoids (FCMdd) and robust fuzzy c-medoids (RFCMdd)-for fuzzy clustering of relational data. The objective functions are based on selecting c representative objects (medoids) from the data set in such a way that the total fuzzy dissimilarity within each cluster is minimized. A comparison of FCMdd with the well-known relational fuzzy c-means algorithm (RFCM) shows that FCMdd is more efficient. We present several applications of these algorithms to Web mining, including Web document clustering, snippet clustering, and Web access log analysisen
dc.format.extent326606 bytes-
dc.format.mimetypeapplication/msword-
dc.language.isoenen
dc.subjectalgorithms-fuzzy c-medoidsen
dc.subjectrobust fuzzy c-medoidsen
dc.subjectrelational fuzzy c-meansen
dc.titleLow-complexity fuzzy relational clustering algorithms for Web miningen
dc.typeArticleen
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