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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: SIGMOD, pp. 207–216. ACM Press (1993)
Antonie, M.-L., Zaïane, O.R.: Mining positive and negative association rules: An approach for confined rules. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) PKDD 2004. LNCS (LNAI), vol. 3202, pp. 27–38. Springer, Heidelberg (2004)
Han, J., Pei, J., Yin, Y., Mao, R.: Mining frequent patterns without candidate generation: A frequent-pattern tree approach. Data Min. Knowl. Discov. 8(1), 53–87 (2004)
Li, W., Han, J., Pei, J.: CMAR: accurate and efficient classification based on multiple class-association rules. In: ICDM 2001, pp. 369–376 (2001)
Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. In: KDD 1998, pp. 80–86 (1998)
Ross Quinlan, J.: C4.5: Programs for Machine Learning. Morgan Kaufmann (1993)
Rauch, J., Šimůnek, M.: An alternative approach to mining association rules. Foundation of Data Mining and Knowl. Discovery 6, 211–231 (2005)
Thabtah, F.: Pruning techniques in associative classification: Survey and comparison. Journal of Digital Information Management 4(3) (2006)
Thabtah, F., Cowling, P., Peng, Y.: The impact of rule ranking on the quality of associative classifiers. In: Bramer, M., Coenen, F., Allen, T. (eds.) Research and Development in Intelligent Systems XXII, pp. 277–287. Springer, London (2006)
Thabtah, F., Cowling, P., Peng, Y.: Multiple labels associative classification. Knowledge and Information Systems 9(1), 109–129 (2006)
Thabtah, F.A.: A review of associative classification mining. Knowledge Eng. Review 22(1), 37–65 (2007)
Toivonen, H., Klemettinen, M., Ronkainen, P., Htnen, K., Mannila, H.: Pruning and grouping discovered association rules. In: ECML 1995 Workshop on Statistics, Machine Learning and Knowledge Discovery in Databases, pp. 47–52 (1995)
Vanhoof, K., Depaire, B.: Structure of association rule classifiers: a review. In: 2010 International Conference on Intelligent Systems and Knowledge Engineering (ISKE), pp. 9–12 (November 2010)
Vojíř, S., Kliegr, T., Hazucha, A., Skrabal, R., Šimunek, M.: Transforming association rules to business rules: Easyminer meets drools. In: Fodor, P., Roman, D., Anicic, D., Wyner, A., Palmirani, M., Sottara, D., Lévy, F. (eds.) RuleML (2). CEUR Workshop Proceedings, vol. 1004. CEUR-WS.org (2013)
Yin, X., Han, J.: CPAR: Classification based on predictive association rules. In: Proceedings of the SIAM International Conference on Data Mining, pp. 369–376. SIAM, San Franciso (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Kliegr, T., Kuchař, J., Sottara, D., Vojíř, S. (2014). Learning Business Rules with Association Rule Classifiers. In: Bikakis, A., Fodor, P., Roman, D. (eds) Rules on the Web. From Theory to Applications. RuleML 2014. Lecture Notes in Computer Science, vol 8620. Springer, Cham. https://doi.org/10.1007/978-3-319-09870-8_18
Download citation
DOI: https://doi.org/10.1007/978-3-319-09870-8_18
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09869-2
Online ISBN: 978-3-319-09870-8
eBook Packages: Computer ScienceComputer Science (R0)