DSpace
 

EPrints@IIT Delhi >
Faculty Research Publicatons  >
Computer Science and Engineering >

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

Title: Low-complexity fuzzy relational clustering algorithms for Web mining
Authors: Krishnapuram, Raghu
Joshi, Anupam
Nasraoui, Olfa
Liyu Yi
Keywords: algorithms-fuzzy c-medoids
robust fuzzy c-medoids
relational fuzzy c-means
Issue Date: 2001
Citation: Fuzzy Systems, IEEE Transactions on, 9(4), 595 - 607p.
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
URI: http://eprint.iitd.ac.in/dspace/handle/2074/2233
Appears in Collections:Computer Science and Engineering

Files in This Item:

File Description SizeFormat
krishnapuramlow2001.pdf207.92 kBAdobe PDFView/Open
View Statistics

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

 

Valid XHTML 1.0! DSpace Software Copyright © 2002-2010  Duraspace - Feedback