Metrics to Support the Evaluation of Association Rule Clustering

  • Veronica Oliveira de Carvalho
  • Fabiano Fernandes dos Santos
  • Solange Oliveira Rezende
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8057)

Abstract

Many topics related to association mining have received attention in the research community, especially the ones focused on the discovery of interesting knowledge. A promising approach, related to this topic, is the application of clustering in the pre-processing step to aid the user to find the relevant associative patterns of the domain. In this paper, we propose nine metrics to support the evaluation of this kind of approach. The metrics are important since they provide criteria to: (a) analyze the methodologies, (b) identify their positive and negative aspects, (c) carry out comparisons among them and, therefore, (d) help the users to select the most suitable solution for their problems. Some experiments were done in order to present how the metrics can be used and their usefulness.

Keywords

Association Rules Clustering Pre-processing 

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Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2013

Authors and Affiliations

  • Veronica Oliveira de Carvalho
    • 1
  • Fabiano Fernandes dos Santos
    • 2
  • Solange Oliveira Rezende
    • 2
  1. 1.Instituto de Geociências e Ciências ExatasUNESP - Univ Estadual PaulistaRio ClaroBrazil
  2. 2.Instituto de Ciências Matemáticas e de ComputaçãoUSP - Universidade de São PauloSão CarlosBrazil

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