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Labeling Association Rule Clustering through a Genetic Algorithm Approach

  • Renan de Padua
  • Veronica Oliveira de Carvalho
  • Adriane Beatriz de Souza Serapião
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 241)

Abstract

Among the post-processing association rule approaches, a promising one is clustering. When an association rule set is clustered, the user is provided with an improved presentation of the mined patterns, since he can have a view of the domain to be explored. However, to take advantage of this organization, it is essential that good labels be assigned to the groups, in order to guide the user during the exploration process. Moreover, few works have explored and proposed labeling methods to this context. Therefore, this paper proposes a labeling method, named GLM (Genetic Labeling Method), for association rule clustering. The method is a genetic algorithm approach that aims to balance the values of the measures that are used to evaluate labeling methods in this context. In the experiments, GLM presented a good performance and better results than some other methods already explored.

Keywords

Association Rules Clustering Labeling Methods Genetic Algorithm 

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References

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Renan de Padua
    • 1
  • Veronica Oliveira de Carvalho
    • 1
  • Adriane Beatriz de Souza Serapião
    • 1
  1. 1.Instituto de Geociências e Ciências ExatasUNESP - Univ Estadual PaulistaRio ClaroBrazil

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