A fuzzy relative of the k-medoids algorithm with application to web document and snippet clustering

Author

Krishnapuram, Raghu ; Joshi, Anupam ; Yi, Liyu

Author_Institution

Dept. of Math. & Comput. Sci., Colorado Sch. of Mines, Golden, CO, USA

Volume

3

fYear

1999

fDate

22-25 Aug. 1999

Firstpage

1281

Abstract

This paper presents new algorithms (fuzzy e-methods (FCMdd) and fuzzy c trimmed medoids (FCTMdd)) 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 dissimilarity within each cluster is minimized. A comparison of FCMdd with the relational fuzzy c-means algorithm shows that FCMdd is much faster. We present examples of applications of these algorithms to web document and snippet clustering.

Keywords

computational complexity; fuzzy set theory; information retrieval; pattern recognition; relational databases; dissimilarity; fuzzy c trimmed medoids; fuzzy c-means algorithm; fuzzy clustering; medoids algorithm; objective functions; relational data; snippet clustering; web document; Application software; Clustering algorithms; Computer science; Couplings; Fuzzy sets; Merging; Partitioning algorithms; Pattern recognition; Prototypes; Uniform resource locators;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems Conference Proceedings, 1999. FUZZ-IEEE '99. 1999 IEEE International

Conference_Location

Seoul, South Korea

ISSN

1098-7584

Print_ISBN

0-7803-5406-0

Type

conf

DOI

10.1109/FUZZY.1999.790086

Filename

790086