Interestingness filtering engine: Mining Bayesian networks for interesting patterns

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Interestingness filtering engine: Mining Bayesian networks for interesting patterns

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dc.contributor.author Malhas, Rana
dc.contributor.author Al Aghbari, Zaher
dc.date.accessioned 2009-12-27T06:34:31Z
dc.date.available 2009-12-27T06:34:31Z
dc.date.issued 2009-04-01
dc.identifier.citation Volume 36, Issue 3, Part 1, April 2009, Pages 5137-5145 en_US
dc.identifier.uri http://dx.doi.org/10.1016/j.eswa.2008.06.028
dc.identifier.uri http://hdl.handle.net/10576/10467
dc.description.abstract In 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). en_US
dc.language.iso en en_US
dc.subject Association rules en_US
dc.subject Interestingness en_US
dc.subject Bayesian networks en_US
dc.subject Data mining en_US
dc.title Interestingness filtering engine: Mining Bayesian networks for interesting patterns en_US
dc.type Article en_US

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