Abstract
We put forward a new conception, dynamic association rule, which can describe the regularities of changes over time in association rules. The dynamic association rule is different in that it contains not only a support and a confidence but also a support vector and a confidence vector. During the mining process, the data used for mining is divided into several parts according to certain time indicators, such as years, seasons and months, and a support vector and a confidence vector for each rule are generated which show the support and the confidence of the rule in each subsets of the data. By using the two vectors, we can not only find the information about the rules’ changes with time but also predict the tendencies of the rules, which ordinary association rules can not offer.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proc. of the 11th Int’l. Conference on Data Engineering, March 3–14 (1995)
Dong, G., Li, J.: Efficient mining of emerging patterns: Discovering trends and differences. In: Proc. of the Fifth Int’l. Conference on Knowledge Discovery and Data Mining, pp. 43–52 (1999)
Au, W.H., Chan, K.C.C.: Fuzzy data mining for discovering changes in association rules over time. In: Proc. of 2002 IEEE Int’l. Conference on Fuzzy Systems, May 2002, pp. 890–895 (2002)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proc of 20th Int’l. Conference on Very Large Data Bases, September 1994, pp. 487–499 (1994)
Han, J., Kamber, M.: Data Mining Concepts and Techniques. Morgan Kaufmann Publishers, San Fransisco (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, J., Rong, G. (2005). Mining Dynamic Association Rules in Databases. In: Hao, Y., et al. Computational Intelligence and Security. CIS 2005. Lecture Notes in Computer Science(), vol 3801. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11596448_102
Download citation
DOI: https://doi.org/10.1007/11596448_102
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30818-8
Online ISBN: 978-3-540-31599-5
eBook Packages: Computer ScienceComputer Science (R0)