Discovering Interesting Association Rules by Clustering

  • Yanchang Zhao
  • Chengqi Zhang
  • Shichao Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3339)


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.


Interestingness Association Rules Clustering 


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  1. 1.
    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)Google Scholar
  2. 2.
    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)Google Scholar
  3. 3.
    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)Google Scholar
  4. 4.
    Kolatch, E.: Clustering Algorithms for Spatial Databases: A Survey. Dept. of Computer Science, University of Maryland, College Park (2001),
  5. 5.
    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)Google Scholar
  6. 6.
    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)CrossRefGoogle Scholar
  7. 7.
    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)Google Scholar
  8. 8.
    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)Google Scholar
  9. 9.
    Liu, B., Hsu, W., Chen, S., MA, Y.: Analyzing the subjective interestingness of association rules. IEEE Intelligent Systems 15(5), 47–55 (2000)CrossRefGoogle Scholar
  10. 10.
    Omiecinski, E.: Alternative interest measures for mining associations. IEEE Trans. Knowledge and Data Engineering 15, 57–69 (2003)CrossRefGoogle Scholar
  11. 11.
    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)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Yanchang Zhao
    • 1
  • Chengqi Zhang
    • 1
  • Shichao Zhang
    • 1
  1. 1.Faculty of Information TechnologyUniv. of TechnologySydneyAustralia

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