Iterative Bayesian Network Implementation by Using Annotated Association Rules

  • Clément Fauré
  • Sylvie Delprat
  • Jean-François Boulicaut
  • Alain Mille
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4248)


This paper concerns the iterative implementation of a knowledge model in a data mining context. Our approach relies on coupling a Bayesian network design with an association rule discovery technique. First, discovered association rule relevancy isenhanced by exploiting the expert knowledge encoded within a Bayesian network, i.e., avoiding to provide trivial rules w.r.t. known dependencies. Moreover, the Bayesian network can be updated thanks to an expert-driven annotation process on computed association rules. Our approach is experimentally validated on the Asia benchmark dataset.


Bayesian Network Association Rule Frequent Itemsets Knowledge Model Bayesian Network Structure 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Clément Fauré
    • 1
    • 2
  • Sylvie Delprat
    • 1
  • Jean-François Boulicaut
    • 2
  • Alain Mille
    • 3
  1. 1.Learning Systems DepartmentEADS CCRBlagnac
  2. 2.LIRIS UMR 5205INSA Lyon, Bâtiment Blaise PascalVilleurbanne
  3. 3.LIRIS UMR 5205Université Lyon 1, NautibusVilleurbanne

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