Advertisement

Redundant Association Rules Reduction Techniques

  • Mafruz Zaman Ashrafi
  • David Taniar
  • Kate Smith
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3809)

Abstract

Association rule mining has a capability to find hidden correlations among different items within a dataset. To find hidden correlations, it uses two important thresholds known as support and confidence. However, association rule mining algorithms produce many redundant rules though it uses above thresholds. Indeed such redundant rules seem as a main impediment to efficient utilization discovered association rules, and should be removed. To achieve this aim, in the paper, we present several redundant rule elimination methods that first identify the rules that have similar meaning and then eliminate those rules. Furthermore, our methods eliminate redundant rules in such a way that they never drop any higher confidence or interesting rules from the resultant ruleset. The experimental evaluation shows that our proposed methods eliminate a significant number of redundant rules.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Agrawal, R., Imielinski, T., Srikant, R.: Mining Association Rules between Sets of Items in Large Databases. In: The proc. ACM SIGMOD, pp. 207–216 (1993)Google Scholar
  2. 2.
    Zaki, M.J.: Parallel and Distributed Association Mining: A Survey. In: IEEE Concurrency, pp. 14–25 (October-December 1999)Google Scholar
  3. 3.
    Aggarwal, C.C., Yu, P.S.: A new Approach to Online Generation of Association Rules. IEEE TKDE 13(4), 527–540Google Scholar
  4. 4.
    Liu, B., Hu, M., Hsu, W.: Multi-Level Organization and Summarization of the Discovered Rules. In: The proc. KDD, pp. 208–217 (2000)Google Scholar
  5. 5.
    Liu, B., Hsu, W., Ma, Y.: Pruning and Summarize the Discovered Associations. In: The proc. ACM SIGMOD, San Diego, CA, pp. 125–134 (August 1999)Google Scholar
  6. 6.
    Klemettinen, M., Mannila, H., Ronkainen, P., Toivonen, H., Verkamo, A.I.: Finding Interesting Rules from Large Sets of Discovered Association Rules. In: Proc.CIKM, pp. 401–407 (1994)Google Scholar
  7. 7.
    Zaki, M.J.: Generating non-redundant association rules. In: Proceeding of the ACM SIGKDD, pp. 34–43 (2000)Google Scholar
  8. 8.
    Liu, B., Hsu, W., Ma, Y.: Mining Association Rules with Multiple Minimum Supports. In: The proc. KDD, pp. 337–341 (1999)Google Scholar
  9. 9.
    Blake, C.L., Merz, C.J.: UCI Repository of Machine Learning Databases, University of California, Irvine, Dept. of Information and Computer Science (1998), www.ics.uci.edu/~mlearn/MLRepository.html
  10. 10.
    Kohavi, R., Brodley, C., Frasca, B., Mason, L., Zheng, Z.: KDD-Cup 2000 organizers report: Peeling the onion. SIGKDD Explorations 2(2), 86–98 (2000), http://www.ecn.purdue.edu/KDDCUP/
  11. 11.
    Goethals, B.: Frequent Pattern Mining Implementations, University of Helsinki-Department of Computer Science, http://www.cs.helsinki.fi/u/goethals/software/
  12. 12.
    Bastide, Y., Pasquier, N., Taouil, R., Stumme, G., Lakhal, L.: Mining minimal non-redundant association rules using frequent closed itemsets. In: Palamidessi, C., Moniz Pereira, L., Lloyd, J.W., Dahl, V., Furbach, U., Kerber, M., Lau, K.-K., Sagiv, Y., Stuckey, P.J. (eds.) CL 2000. LNCS (LNAI), vol. 1861, p. 972. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  13. 13.
    Zaki, M.J.: Mining Non-Redundant Association Rules. Data Mining and Knowledge Discovery 9, 223–248 (2004)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Mafruz Zaman Ashrafi
    • 1
  • David Taniar
    • 1
  • Kate Smith
    • 1
  1. 1.School of Business SystemsMonash UniversityClaytonAustralia

Personalised recommendations