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AuthorMalhas, Rana
AuthorAl Aghbari, Zaher
Available date2009-12-27T06:34:31Z
Publication Date2009-04-01
Publication NameExpert Systems with Applications
CitationRana Malhas, Zaher Al Aghbari, Interestingness filtering engine: Mining Bayesian networks for interesting patterns, Expert Systems with Applications, Volume 36, Issue 3, Part 1, April 2009, Pages 5137-5145
AbstractIn this paper, we present a new measure of interestingness to discover interesting patterns based on the user’s background knowledge, represented by a Bayesian network. The new measure (sensitivity measure) captures the sensitivity of the Bayesian network to the patterns discovered by assessing the uncertainty-increasing potential of a pattern on the beliefs of the Bayesian network. Patterns that attain the highest sensitivity scores are deemed interesting. In our approach, mutual information (from information theory) came in handy as a measure of uncertainty. The Sensitivity of a pattern is computed by summing up the mutual information increases incurred by a pattern when entered as evidence/findings to the Bayesian network. We demonstrate the strength of our approach experimentally using the KSL dataset of Danish 70 year olds as a case study. The results were verified by consulting two doctors (internists).
PublisherElsevier Ltd
SubjectAssociation rules
SubjectBayesian networks
SubjectData mining
TitleInterestingness filtering engine: Mining Bayesian networks for interesting patterns

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