Genetic fuzzy clustering by means of discovering membership functions


Turhan M.

ADVANCES IN INTELLIGENT DATA ANALYSIS, vol.1280, pp.383-393, 1997 (Peer-Reviewed Journal) identifier

  • Publication Type: Article / Article
  • Volume: 1280
  • Publication Date: 1997
  • Journal Name: ADVANCES IN INTELLIGENT DATA ANALYSIS
  • Journal Indexes: Science Citation Index Expanded, Scopus, Compendex, EMBASE, MathSciNet, Philosopher's Index, zbMATH
  • Page Numbers: pp.383-393

Abstract

It has been observed that in the previous Genetic Algorithms (GA) based Fuzzy Clustering (FC) works only some of the parameters of an FC system are developed. Here, a new approach is proposed to develop directly the membership functions for the clusters using GA. This new technique is implemented and tested on common test data. A comparative study of the results against the quotations in literature reveals that the standard c-means FC technique is outperformed by the proposed technique in the count of misclassifications aspect.