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.
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Fauré, C., Delprat, S., Boulicaut, JF., Mille, A. (2006). Iterative Bayesian Network Implementation by Using Annotated Association Rules. In: Staab, S., Svátek, V. (eds) Managing Knowledge in a World of Networks. EKAW 2006. Lecture Notes in Computer Science(), vol 4248. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11891451_29
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DOI: https://doi.org/10.1007/11891451_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46363-4
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