A genetic algorithm implementation of the fuzzy least trimmed squares clustering

Amit Banerjee, Sushil J. Louis

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

4 Scopus citations

Abstract

This paper describes a new approach to finding a global solution for the fuzzy least trimmed squares clustering. The least trimmed squares (LTS) estimator is known to be a high breakdown estimator, in both regression and clustering. From the point of view of implementation, the feasible solution algorithm is one of the few known techniques that guarantees a global solution for the LTS estimator. The feasible solution algorithm divides a noisy data set into two parts - the non-noisy retained set and the noisy trimmed set, by implementing a pairwise swap of datum between the two sets until a least squares estimator provides the best fit on the retained set. We present a novel genetic algorithm-based implementation of the feasible solution algorithm for fuzzy least trimmed squares clustering, and also substantiate the efficacy of our method by three examples.

Original languageEnglish (US)
Title of host publication2007 IEEE International Conference on Fuzzy Systems, FUZZY
DOIs
StatePublished - Dec 1 2007
Event2007 IEEE International Conference on Fuzzy Systems, FUZZY - London, United Kingdom
Duration: Jul 23 2007Jul 26 2007

Publication series

NameIEEE International Conference on Fuzzy Systems
ISSN (Print)1098-7584

Other

Other2007 IEEE International Conference on Fuzzy Systems, FUZZY
CountryUnited Kingdom
CityLondon
Period7/23/077/26/07

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Artificial Intelligence
  • Applied Mathematics

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    Banerjee, A., & Louis, S. J. (2007). A genetic algorithm implementation of the fuzzy least trimmed squares clustering. In 2007 IEEE International Conference on Fuzzy Systems, FUZZY [4295399] (IEEE International Conference on Fuzzy Systems). https://doi.org/10.1109/FUZZY.2007.4295399