Interestingness filtering engine: Mining Bayesian networks for interesting patterns

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Author Malhas, Rana en_US
Author Al Aghbari, Zaher en_US
Available date 2009-12-27T06:34:31Z en_US
Publication Date 2009-04-01 en_US
Citation Rana 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 en_US
URI http://dx.doi.org/10.1016/j.eswa.2008.06.028 en_US
URI http://hdl.handle.net/10576/10467 en_US
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
Language en en_US
Publisher Elsevier Ltd en
Subject Association rules en_US
Subject Interestingness en_US
Subject Bayesian networks en_US
Subject Data mining en_US
Title Interestingness filtering engine: Mining Bayesian networks for interesting patterns en_US
Type Article en_US


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