Abstract
Association rules have exhibited an excellent ability to identify interesting association relationships among a set of binary variables describing huge amount of transactions. Although the rules can be relatively easily generalized to other variable types, the generalization can result in a computationally expensive algorithm generating a prohibitive number of redundant rules of little significance. This danger especially applies to quantitative and ordinal variables. This paper presents and verifies an alternative approach to the quantitative and ordinal association rule mining. In this approach, quantitative or ordinal variables are not immediately transformed into a set of binary variables. Instead, it applies simple arithmetic operations in order to construct the cedents and searches for areas of increased association which are finally decomposed into conjunctions of literals. This scenario outputs rules that do not syntactically differentiate from classical association rules.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Agrawal, R., Imeliski, T., Swami, A.: Mining Association Rules Between Sets of Items in Large Databases. In: Proc. of ACM SIGMOD Conference on Management of Data, Washington, D.C., pp. 207–216 (1993)
Piatetsky-Shapiro, G.: Discovery, analysis, and presentation of strong rules. In: Knowledge Discovery in Databases, pp. 229–248. AAAI/MIT, Cambridge (1991)
Srikant, R., Agrawal, R.: Mining Quantitative Association Rules in Large Relational Databases. In: Proc. of ACM SIGMOD Conference on Management of Data, Montreal, Canada (1996)
Fukuda, T., Morimoto, Y., Morishita, S., Tokuyama, T.: Mining Optimized Association Rules for Numeric Attributes. In: Proc. of ACM SIGMOD Conference on Management of Data, Montreal, Canada (1996)
Miller, R.J., Yang, Y.: Association Rules over Interval Data. In: Proc. of ACM SIGMOD Conference on Management of Data, Tuscon, AZ (1997)
Imberman, S., Domanski, B.: Finding Association Rules from Quantitative Data Using Data Booleanization (1999)
Webb, G.I.: Discovering Associations with Numeric Variables. In: Proc. of ACM SIGMOD Conference on Management of Data, San Francisco, CA (2001)
Rastogi, R., Shim, K.: Mining Optimized Association Rules with Categorical and Numeric Attributes. IEEE Transactions on Knowledge and Data Engineering 14(1) (2002)
Guillaume, S.: Discovery of Ordinal Association Rules. In: Chen, M.-S., Yu, P.S., Liu, B. (eds.) PAKDD 2002. LNCS (LNAI), vol. 2336, pp. 322–327. Springer, Heidelberg (2002)
Aumann, Y., Lindell, Y.: A Statistical Theory for Quantitative Association Rules. Journal of Intelligent Information Systems 20, 255–283 (2003)
Wijsen, J., Meersman, R.: On the Complexity of Mining Quantitative Association Rules. Data Mining and Knowledge Discovery 2, 263–281 (1998)
Dougherty, J., Kohavi, R., Sahami, M.: Supervised and unsupervised discretization of continuous features. In: Proceedings of the Twelfth International Conference on Machine Learning, Tahoe City, CA, pp. 194–202 (1995)
STULONG project, WWW page, http://euromise.vse.cz/stulong
Klema, J., Novakova, L., Karel, M., Stepankova, O.: Trend Analysis in Stulong Data. In: Proceedings of ECML/PKDD 2004 Discovery Challenge (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Karel, F. (2006). Quantitative and Ordinal Association Rules Mining (QAR Mining). In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science(), vol 4251. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11892960_24
Download citation
DOI: https://doi.org/10.1007/11892960_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46535-5
Online ISBN: 978-3-540-46536-2
eBook Packages: Computer ScienceComputer Science (R0)