Iterative bayesian network implementation by using annotated association rules

Authors:
Clément Fauré;Sylvie Delprat;Jean-François Boulicaut;Alain Mille
Affiliations:
Learning Systems Department, EADS CCR, Blagnac;Learning Systems Department, EADS CCR, Blagnac;LIRIS UMR 5205, INSA Lyon, Bâtiment Blaise Pascal, Villeurbanne;LIRIS UMR 5205, Université Lyon 1, Nautibus, Villeurbanne
Venue:
EKAW'06 Proceedings of the 15th international conference on Managing Knowledge in a World of Networks
Year:
2006

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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 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.