Low-complexity fuzzy relational clustering algorithms for Web mining

被引：246

作者：

Krishnapuram, R ^{[1
]}

Joshi, A

Nasraoui, O

Yi, LY

机构：

[1] Indian Inst Technol, IBM India Res Lab, New Delhi 110016, India

[2] Colorado Sch Mines, Dept Math & Comp Sci, Golden, CO 80401 USA

[3] Univ Maryland, Dept Comp Sci & Elect Engn, Baltimore, MD 21250 USA

[4] Univ Memphis, Dept Elect Engn, Memphis, TN 38152 USA

[5] BoldTech Syst Inc, Denver, CO 80202 USA

来源：

IEEE TRANSACTIONS ON FUZZY SYSTEMS | 2001年 / 9卷 / 04期

基金：

美国国家科学基金会;

关键词：

document clustering; fuzzy relational clustering; snippet clustering; user access patterns; user profiles; Web mining;

D O I：

10.1109/91.940971

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：595 / 607

页数：13

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