Abstract
This paper proposes an extended method to discover exceptional association rule sets from incomplete databases. The proposed method calculates an odds ratio directly for the rule evaluation. The exceptional rule set is defined as each itemset X, Y has a weak or no statistical relation to class C, respectively; however, the join of X and Y has a strong relation to C. The exceptional rule set has potential to interpret long rules for the join of X and Y. The proposed method is applied to rule mining for oral health assessment databases. We obtained interesting exceptional rule sets and the results showed effectiveness of the method in the medical and health care fields.
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This work was partly supported by JSPS KAKENHI Grant Number 16K00316.
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Shimada, K., Noguchi, S., Makino, M., Naito, T. (2018). Exceptional Association Rule Set Mining from Oral Health Assessment Database. In: Gruca, A., Czachórski, T., Harezlak, K., Kozielski, S., Piotrowska, A. (eds) Man-Machine Interactions 5. ICMMI 2017. Advances in Intelligent Systems and Computing, vol 659. Springer, Cham. https://doi.org/10.1007/978-3-319-67792-7_42
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DOI: https://doi.org/10.1007/978-3-319-67792-7_42
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