Low-complexity fuzzy relational clustering algorithms for Web mining

This 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 clu...

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Bibliographic Details
Published inIEEE transactions on fuzzy systems Vol. 9; no. 4; pp. 595 - 607
Main Authors Krishnapuram, R., Joshi, A., Nasraoui, O., Yi, L.
Format Journal Article
LanguageEnglish
Published New York IEEE 01.08.2001
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithm design and analysis
Algorithms
Clustering
Clustering algorithms
Clusters
Evolution (biology)
Explosions
Fuzzy
Fuzzy logic
Fuzzy set theory
Fuzzy sets
Fuzzy systems
Internet
Matched filters
Mining
Robustness
Telecommunication traffic
Web mining
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Summary:This 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 analysis.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:1063-6706
1941-0034
DOI:10.1109/91.940971