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An Investigation of Representations and Operators for Evolutionary Data Clustering with a Variable Number of Clusters

  • Julia Handl
  • Joshua Knowles
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4193)

Abstract

This paper analyses the properties of four alternative representation/operator combinations suitable for data clustering algorithms that keep the number of clusters variable. These representations are investigated in the context of their performance when used in a multiobjective evolutionary clustering algorithm (MOCK), which we have described previously. To shed light on the resulting performance differences observed, we consider the relative size of the search space and heuristic bias inherent to each representation, as well as its locality and heritability under the associated variation operators. We find that the representation that performs worst when a random initialization is employed, is nevertheless the best overall performer given the heuristic initialization normally used in MOCK. This suggests there are strong interaction effects between initialization, representation and operators in this problem.

Keywords

Pareto Front Data Item Cluster Solution Cluster Membership Initialization Scheme 
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 2006

Authors and Affiliations

  • Julia Handl
    • 1
  • Joshua Knowles
    • 1
  1. 1.Manchester Interdisciplinary BiocentreUniversity of ManchesterUK

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