Robust fuzzy clustering as a multi-objective optimization procedure

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

4 Scopus citations

Abstract

In this paper, a multi-objective genetic algorithm for data clustering based on the robust fuzzy least trimmed squares estimator is proposed. The clustering methodology addresses two critical issues in unsupervised data clustering - the ability to produce meaningful classification in noisy data, and the requirement that the number of clusters be known a priori. The GA-driven clustering routine optimizes number of clusters as well as cluster assignment, and cluster prototypes. A twoparameter, mapped, fixed point coding scheme is used to represent assignment of data into either the true retained set and the noisy trimmed set, and the optimal number of clusters in the retained set. A three-objective criterion is used as the minimization functional for the GA. Results on well-known data sets from literature suggest that the proposed methodology is comparable (in many cases superior) to conventional robust fuzzy clustering algorithms that assume a known value for optimal number of clusters.

Original languageEnglish (US)
Title of host publicationNAFIPS 2009 - 2009 Annual Meeting of the North American Fuzzy Information Processing Society
DOIs
StatePublished - Nov 2 2009
Event2009 Annual Meeting of the North American Fuzzy Information Processing Society, NAFIPS 2009 - Cincinnati, OH, United States
Duration: Jun 14 2009Jun 17 2009

Other

Other2009 Annual Meeting of the North American Fuzzy Information Processing Society, NAFIPS 2009
CountryUnited States
CityCincinnati, OH
Period6/14/096/17/09

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Mathematics(all)

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