Iterative Bayesian Network Implementation by Using Annotated Association Rules

@inproceedings{Faur2006IterativeBN,
  title={Iterative Bayesian Network Implementation by Using Annotated Association Rules},
  author={Cl{\'e}ment Faur{\'e} and Sylvie Delprat and Jean-François Boulicaut and Alain Mille},
  booktitle={EKAW},
  year={2006}
}
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… CONTINUE READING
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Criteria for combining knowledge from different sources in probabilistic networks

  • M. J. Druzdzel, F. Diez
  • 2000
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