A bayesian association rule mining algorithm

David Tian, Ann Gledson, Athos Antoniades, Aristo Aristodimou, Ntalaperas Dimitrios, Ratnesh Sahay, Jianxin Pan, Stavros Stivaros, Goran Nenadic, Xiao Jun Zeng, John Keane

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Abstract

    This paper proposes a Bayesian association rule mining algorithm (BAR) which combines the Apriori association rule mining algorithm with Bayesian networks. Two interestingness measures of association rules: Bayesian confidence (BC) and Bayesian lift (BL) which measure conditional dependence and independence relationships between items are defined based on the joint probabilities represented by the Bayesian networks of association rules. BAR outputs best rules according to BC and BL. BAR is evaluated for its performance using two anonymized clinical phenotype datasets from the UCI Repository: Thyroid disease and Diabetes. The results show that BAR is capable of finding the best rules which have the highest BC, BL and very high support, confidence and lift. © 2013 IEEE.
    Original languageEnglish
    Title of host publicationProceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013|Proc. - IEEE Int. Conf. Syst., Man, Cybern., SMC
    Place of PublicationIEEE Xplore
    Pages3258-3264
    Number of pages6
    DOIs
    Publication statusPublished - 2013
    Event2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013 - Manchester
    Duration: 1 Jul 2013 → …

    Conference

    Conference2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013
    CityManchester
    Period1/07/13 → …

    Keywords

    • Bayesian association rules
    • Bayesian confidence
    • Bayesian lift
    • Bayesian networks
    • Joint probability distribution

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