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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Title: Interestingness filtering engine: Mining Bayesian networks for interesting patterns
Author: Malhas, Rana; Al Aghbari, Zaher
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).
URI: http://dx.doi.org/10.1016/j.eswa.2008.06.028
http://hdl.handle.net/10576/10467
Date: 2009-04-01

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