Cube Based Summaries of Large Association Rule Sets

  • Marie Ndiaye
  • Cheikh T. Diop
  • Arnaud Giacometti
  • Patrick Marcel
  • Arnaud Soulet
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6440)


A major problem when dealing with association rules post-processing is the huge amount of extracted rules. Several approaches have been implemented to summarize them. However, the obtained summaries are generally difficult to analyse because they suffer from the lack of navigational tools. In this paper, we propose a novel method for summarizing large sets of association rules. Our approach enables to obtain from a rule set, several summaries called Cube Based Summaries (CBSs). We show that the CBSs can be represented as cubes and we give an overview of OLAP  navigational operations that can be used to explore them. Moreover, we define a new quality measure called homogeneity, to evaluate the interestingness of CBSs. Finally, we propose an algorithm that generates a relevant CBS w.r.t. a quality measure, to initialize the exploration. The evaluation of our algorithm on benchmarks proves the effectiveness of our approach.


Association rules summary cubes 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Marie Ndiaye
    • 1
    • 2
  • Cheikh T. Diop
    • 2
  • Arnaud Giacometti
    • 1
  • Patrick Marcel
    • 1
  • Arnaud Soulet
    • 1
  1. 1.Laboratoire d’InformatiqueUniversité François Rabelais Tours, Antenne Universitaire de BloisBloisFrance
  2. 2.Laboratoire d’Analyse Numérique et d’InformatiqueUniversité Gaston Berger de Saint-LouisSaint-LouisSenegal

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