Title :
Low-complexity fuzzy relational clustering algorithms for Web mining
Author :
Krishnapuram, Raghu ; Joshi, Anupam ; Nasraoui, Olfa ; Yi, Liyu
Author_Institution :
IBM India Res. Lab., Indian Inst. of Technol., New Delhi, India
fDate :
8/1/2001 12:00:00 AM
Abstract :
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
Keywords :
Internet; data mining; fuzzy set theory; information resources; minimisation; pattern clustering; relational databases; FCMdd; RFCMdd; Web access log analysis; Web document clustering; World Wide Web mining; data mining; low-complexity fuzzy relational clustering algorithms; relational data; robust fuzzy c-medoids; snippet clustering; total fuzzy dissimilarity minimization; Algorithm design and analysis; Clustering algorithms; Evolution (biology); Explosions; Fuzzy sets; Internet; Matched filters; Robustness; Telecommunication traffic; Web mining;
Journal_Title :
Fuzzy Systems, IEEE Transactions on
