A Genetic Approach to Fuzzy Clustering with a Validity Measure Fitness Function

Susana Nascimento, Fernando Moura Pires

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

15 Citations (Scopus)


This paper presents an extension to the genetic fuzzy clustering algorithm proposed by the authors. The original algorithm, which combines the powerful search technique of genetic algorithms with the fuzzy c-means (FCM) algorithm, is extended such that the FCM algorithm was totally embedded in the genetic operators design. Two objective functions are applied as fitness functions: the performance index of a P fuzzy c-partition Jm(P), used on the FCM algorithm, and the partition coe~cient Fc(P), a function commonly used as a measure of cluster validity. The fuzzy c-means and the new proposal for the genetic fuzzy clustering algorithm were compared on generating multiple prototypes. The experimental results show that the use of genetic search improves the quality of the clustering solutions and that the partition coe~cient Fc(P) is a better measure for clustering than the performance index Jm(P).
Original languageEnglish
Title of host publicationAdvances in Intelligent Data Analysis, Reasoning About Data
EditorsX. Liu, P. Cohen, M. Berthold
PublisherSpringer Verlag
Number of pages11
Publication statusPublished - 1997
EventSecond International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis. Reasoning about Data - London, United Kingdom
Duration: 4 Aug 19974 Aug 1997

Publication series

NameLecture Notes in Computer Science


ConferenceSecond International Symposium on Intelligent Data Analysis
Abbreviated title IDA-97
Country/TerritoryUnited Kingdom
Internet address


  • Image segmentation
  • Information analysis
  • Algorithms
  • Data handling
  • Fuzzy clustering
  • Fuzzy systems
  • Genetic algorithms


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