Generalized noise clustering as a robust fuzzy c-M-estimators model

Rajesh N. Davé, Sumit Sen

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

15 Scopus citations

Abstract

R.N. Dave's (1991) noise clustering (NC) algorithm has been generalized in an earlier work where the noise distance δ is allowed to take different values for different feature vectors. Based on that, it was shown that the membership generated by the NC algorithm is a product of two terms, one is the original fuzzy c-means (FCM) membership responsible for data partitioning and the other is a generalized possibilistic membership that achieves a mode seeking effect, and imparts robustness. It is shown that a variety of robust M-estimators can be incorporated into the generalized NC algorithm, for example Huber, Hampel, Cauchy, Tukey biweight, and Andrew's sine. The generalized NC algorithm is also compared with the recently introduced mixed c-means and a noise resistant FCM technique.

Original languageEnglish (US)
Title of host publication1998 Conference of the North American Fuzzy Information Processing Society, NAFIPS 1998
EditorsLawrence O. Hall, Jim Bezdek
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages256-260
Number of pages5
ISBN (Electronic)0780344537
DOIs
StatePublished - Jan 1 1998
Event1998 Conference of the North American Fuzzy Information Processing Society, NAFIPS 1998 - Pensacola Beach, United States
Duration: Aug 20 1998Aug 21 1998

Publication series

NameAnnual Conference of the North American Fuzzy Information Processing Society - NAFIPS

Other

Other1998 Conference of the North American Fuzzy Information Processing Society, NAFIPS 1998
Country/TerritoryUnited States
CityPensacola Beach
Period8/20/988/21/98

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Mathematics(all)

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