Learning business rules with association rule classifiers

Tomáš Kliegr, Jaroslav Kuchař, Davide Sottara, Stanislav Vojíř

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    14 Scopus citations

    Abstract

    The main obstacles for a straightforward use of association rules as candidate business rules are the excessive number of rules discovered even on small datasets, and the fact that contradicting rules are generated. This paper shows that Association Rule Classification algorithms, such as CBA, solve both these problems, and provides a practical guide on using discovered rules in the Drools BRMS and on setting the ARC parameters. Experiments performed with modified CBA on several UCI datasets indicate that data coverage rule pruning keeps the number of rules manageable, while not adversely impacting the accuracy. The best results in terms of overall accuracy are obtained using minimum support and confidence thresholds. Disjunction between attribute values seem to provide a desirable balance between accuracy and rule count, while negated literals have not been found beneficial.

    Original languageEnglish (US)
    Title of host publicationRules on the Web
    Subtitle of host publicationFrom Theory to Applications - 8th International Symposium, RuleML 2014, Co-located with the 21st European Conference on Artificial Intelligence, ECAI 2014, Proceedings
    PublisherSpringer Verlag
    Pages236-250
    Number of pages15
    ISBN (Print)9783319098692
    DOIs
    StatePublished - Jan 1 2014
    Event8th International Web Rule Symposium, RuleML 2014, Co-located with the 21st European Conference on Artificial Intelligence, ECAI 2014 - Prague, Czech Republic
    Duration: Aug 18 2014Aug 20 2014

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume8620 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Other

    Other8th International Web Rule Symposium, RuleML 2014, Co-located with the 21st European Conference on Artificial Intelligence, ECAI 2014
    CountryCzech Republic
    CityPrague
    Period8/18/148/20/14

    Keywords

    • Drools
    • association rules
    • business rules
    • rule pruning

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • Computer Science(all)

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  • Cite this

    Kliegr, T., Kuchař, J., Sottara, D., & Vojíř, S. (2014). Learning business rules with association rule classifiers. In Rules on the Web: From Theory to Applications - 8th International Symposium, RuleML 2014, Co-located with the 21st European Conference on Artificial Intelligence, ECAI 2014, Proceedings (pp. 236-250). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8620 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-09870-8_18