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Communication Dans Un Congrès Année : 2006

Iterative bayesian network implementation by using annotated association rules

Résumé

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.

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hal-01592340 , version 1 (23-09-2017)

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Clément Faure, Sylvie Delprat, Jean-François Boulicaut, Alain Mille. Iterative bayesian network implementation by using annotated association rules. 15th International Conference on Knowledge Engineering and Knowledge Management EKAW'06, Sep 2006, Podebrady, Czech Republic. pp.326-333, ⟨10.1007/11891451_29⟩. ⟨hal-01592340⟩
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