Abstract
There are a great many metrics available for measuring the interestingness of rules. In this paper, we design a distinct approach for identifying association rules that maximizes the interestingness in an applied context. More specifically, the interestingness of association rules is defined as the dissimilarity between corresponding clusters. In addition, the interestingness assists in filtering out those rules that may be uninteresting in applications. Experiments show the effectiveness of our algorithm.
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.
References
Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of tiems in large databases. In: Proc. of the ACM SIGMOD Int. Conf. on Management of Data (SIGMOD 1993), Washington, D.C., USA, May 1993, pp. 207–216 (1993)
Chan, R., Yang, Q., Shen, Y.-D.: Mining high utility itemsets. In: Proc. of the 2003 IEEE International Conference on Data Mining, Florida, USA (November 2003)
Dong, G., Li, J.: Interestingness of discovered association rules in terms of neighborhood-based unexpectedness. In: Proc. of the 2nd Pacific-Asia Conf. on Knowledge Discovery and Data Mining, Melbourne, Australia, April, pp. 72–86 (1998)
Kolatch, E.: Clustering Algorithms for Spatial Databases: A Survey. Dept. of Computer Science, University of Maryland, College Park (2001), http://citeseer.nj.nec.com/436843.html
Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery: an overview. In: Advances in Knowledge Discovery & Data Mining, pp. 1–34. AAAI/MIT (1996)
Freitas, A.A.: On objective measures of rule surprisingness. In: Żytkow, J.M. (ed.) PKDD 1998. LNCS, vol. 1510, pp. 1–9. Springer, Heidelberg (1998)
Hilderman, R.J., Hamilton, H.J.: Knowledge discovery and interestingness measures: a survey. Tech. Report 99-4, Department of Computer Science, University of Regina (October 1999)
Kim, W.-Y., Lee, Y.-K., Han, J.: CCMine: efficient mining of confidence-closed correlated patterns. In: Proc. of 2004 Pacific-Asia Conf. on Knowledge Discovery and Data Mining, Sydney, Australia, May 2004, pp. 569–579 (2004)
Liu, B., Hsu, W., Chen, S., MA, Y.: Analyzing the subjective interestingness of association rules. IEEE Intelligent Systems 15(5), 47–55 (2000)
Omiecinski, E.: Alternative interest measures for mining associations. IEEE Trans. Knowledge and Data Engineering 15, 57–69 (2003)
Tan, P.-N., Kumar, V., Srivastava, J.: Selecting the right interestingness measure for association patterns. In: Proc. of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, Alberta, pp. 32–41 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhao, Y., Zhang, C., Zhang, S. (2004). Discovering Interesting Association Rules by Clustering. In: Webb, G.I., Yu, X. (eds) AI 2004: Advances in Artificial Intelligence. AI 2004. Lecture Notes in Computer Science(), vol 3339. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30549-1_101
Download citation
DOI: https://doi.org/10.1007/978-3-540-30549-1_101
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24059-4
Online ISBN: 978-3-540-30549-1
eBook Packages: Computer ScienceComputer Science (R0)