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Fuzzy Clustering with Grouping Genetic Algorithms

  • S. Salcedo-Sanz
  • L. Carro-Calvo
  • A. Portilla-Figueras
  • L. Cuadra
  • D. Camacho
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8206)

Abstract

This paper presents a novel approach to fuzzy clustering based on Grouping Genetic Algorithms (GGAs). Our approach consists of a GGA devised for fuzzy clustering by means of a novel encoding of individuals (containing elements and clusters sections), a new fitness function (a superior modification of the Davies-Boudin index) and specially tailored crossover and mutation operators. The overall performance of our approach has been tested in a variety of fuzzy clustering problems, showing very good performance in all cases.

Keywords

Mutation Operator Fuzzy Cluster Cluster Problem Rand Index Partition Matrix 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • S. Salcedo-Sanz
    • 1
  • L. Carro-Calvo
    • 1
  • A. Portilla-Figueras
    • 1
  • L. Cuadra
    • 1
  • D. Camacho
    • 2
  1. 1.Universidad de AlcaláMadridSpain
  2. 2.Universidad Autónoma de MadridMadridSpain

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