Iterative bayesian network implementation by using annotated association rules

Clément Faure 1 Sylvie Delprat 2 Jean-François Boulicaut 1 Alain Mille 3
1 DM2L - Data Mining and Machine Learning
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
3 SILEX - Supporting Interaction and Learning by Experience
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : 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 is enhanced 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.
Document type :
Conference papers
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https://hal.archives-ouvertes.fr/hal-01592340
Contributor : Équipe Gestionnaire Des Publications Si Liris <>
Submitted on : Saturday, September 23, 2017 - 2:04:18 PM
Last modification on : Friday, January 11, 2019 - 4:29:36 PM

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  • HAL Id : hal-01592340, version 1

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Clément Faure, Sylvie Delprat, Jean-François Boulicaut, Alain Mille. Iterative bayesian network implementation by using annotated association rules. Proc. 15th Int. Conf. on Knowledge Engineering and Knowledge Management EKAW'06, Sep 2006, Podebrady, Czech Republic. pp.326-333. ⟨hal-01592340⟩

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