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Fuzzy Ant Based Clustering

  • Steven Schockaert
  • Martine De Cock
  • Chris Cornelis
  • Etienne E. Kerre
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3172)

Abstract

Various clustering methods based on the behaviour of real ants have been proposed. In this paper, we develop a new algorithm in which the behaviour of the artificial ants is governed by fuzzy IF–THEN rules. Our algorithm is conceptually simple, robust and easy to use due to observed dataset independence of the parameter values involved.

Keywords

Fuzzy Rule Response Threshold Fuzzy Relation Very High Binary Fuzzy Relation 
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 2004

Authors and Affiliations

  • Steven Schockaert
    • 1
  • Martine De Cock
    • 1
  • Chris Cornelis
    • 1
  • Etienne E. Kerre
    • 1
  1. 1.Fuzziness and Uncertainty Modelling Research Unit, Department of Applied Mathematics and Computer ScienceGhent UniversityGentBelgium

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