@inproceedings{b3e1291c677d44469010bc1cda268196,
title = "Learning business rules with association rule classifiers",
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.",
keywords = "Drools, association rules, business rules, rule pruning",
author = "Tom{\'a}{\v s} Kliegr and Jaroslav Kucha{\v r} and Davide Sottara and Stanislav Voj{\'i}{\v r}",
year = "2014",
doi = "10.1007/978-3-319-09870-8_18",
language = "English (US)",
isbn = "9783319098692",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "236--250",
booktitle = "Rules on the Web",
note = "8th International Web Rule Symposium, RuleML 2014, Co-located with the 21st European Conference on Artificial Intelligence, ECAI 2014 ; Conference date: 18-08-2014 Through 20-08-2014",
}