M-estimators and robust fuzzy clustering

Rajesh Dave, Raghu Krishnapuram

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

In this paper, we compare several recent robust fuzzy clustering methods, and show their equivalence in terms of their connection to techniques in robust statistics. In particular, we establish a connection between fuzzy set theory (as used in robust fuzzy clustering methods) and robust statistics, and point out the similarities between robust clustering methods and statistical methods such as the M-estimator. We also give qualitative and quantitative equivalence of the robust fuzzy clustering techniques with M-estimator.

Original languageEnglish (US)
Title of host publicationNew Frontiers in Fuzzy Logic and Soft Computing
PublisherIEEE
Pages400-404
Number of pages5
StatePublished - Jan 1 1996
EventProceedings of the 1996 Biennial Conference of the North American Fuzzy Information Processing Society - NAFIPS - Berkeley, CA, USA
Duration: Jun 19 1996Jun 22 1996

Other

OtherProceedings of the 1996 Biennial Conference of the North American Fuzzy Information Processing Society - NAFIPS
CityBerkeley, CA, USA
Period6/19/966/22/96

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Fingerprint Dive into the research topics of 'M-estimators and robust fuzzy clustering'. Together they form a unique fingerprint.

Cite this