The feasible solution algorithm for fuzzy least trimmed squares clustering

Amit Banerjee, Rajesh N. Davé

Research output: Contribution to conferencePaper

4 Scopus citations

Abstract

The issue of sensitivity to noise and outliers of LS minimization based clustering techniques, is addressed In this paper. A novel and robust clustering scheme based on the feasible solution algorithm that implements the Least Trimmed Squares (LTS) estimator, is developed, implemented and the results presented. The LTS estimator is known to be resistant to noise and has a high breakdown point. The feasible solution algorithm approach also guarantees convergence of the solution set to a global optima. Our experiments show the practicability of the proposed scheme in terms of computational requirements and in the attractiveness of its simplistic framework.

Original languageEnglish (US)
Pages222-227
Number of pages6
DOIs
StatePublished - Jan 1 2004
EventNAFIPS 2004 - Annual Meeting of the North American Fuzzy Information Processing Society: Fuzzy Sets in the Heart of the Canadian Rockies - Banff, Alta, Canada
Duration: Jun 27 2004Jun 30 2004

Other

OtherNAFIPS 2004 - Annual Meeting of the North American Fuzzy Information Processing Society: Fuzzy Sets in the Heart of the Canadian Rockies
CountryCanada
CityBanff, Alta
Period6/27/046/30/04

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Mathematics(all)

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    Banerjee, A., & Davé, R. N. (2004). The feasible solution algorithm for fuzzy least trimmed squares clustering. 222-227. Paper presented at NAFIPS 2004 - Annual Meeting of the North American Fuzzy Information Processing Society: Fuzzy Sets in the Heart of the Canadian Rockies, Banff, Alta, Canada. https://doi.org/10.1109/nafips.2004.1336281