Advertisement

A New Approach of Eliminating Redundant Association Rules

  • Mafruz Zaman Ashrafi
  • David Taniar
  • Kate Smith
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3180)

Abstract

Two important constraints of association rule mining algorithm are support and confidence. However, such constraints-based algorithms generally produce a large number of redundant rules. In many cases, if not all, number of redundant rules is larger than number of essential rules, consequently the novel intention behind association rule mining becomes vague. To retain the goal of association rule mining, we present several methods to eliminate redundant rules and to produce small number of rules from any given frequent or frequent closed itemsets generated. The experimental evaluation shows that the proposed methods eliminate significant number of redundant rules.

Keywords

Association Rule Frequent Itemsets Association Rule Mining Support Threshold Confidence Threshold 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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: ACM SIGMOD, May 1993, 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.
    Zaki, M.J.: Scalable Algorithms for Association Mining. IEEE Transactions on Knowledge and Data Engineering 12(2), 372–390 (2000)CrossRefMathSciNetGoogle Scholar
  4. 4.
    Aggarwal, C.C., Yu, P.S.: A new Approach to Online Generation of Association Rules. IEEE TKDE 13(4), 527–540Google Scholar
  5. 5.
    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
  6. 6.
    Liu, B., Hsu, W., Ma, Y.: Pruning and Summarize the Discovered Associations. In: The proc. of ACM SIGMOD, San Diego, CA, August 1999, pp. 125–134 (1999)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), http://www.ics.uci.edu/~mlearn/MLRepository.html
  10. 10.
    Kohavi, R., Brodley, C., Frasca, B., Mason, L., Zheng, Z.: KDDCup 2000 organizers report: Peeling the onion. SIGKDD Explorations 2(2), 86–98 (2000), http://www.ecn.purdue.edu/KDDCUP/ CrossRefGoogle Scholar
  11. 11.
    Goethals, B.: Frequent Pattern Mining Implementations, University of Helsinki-Department of Computer Science, http://www.cs.helsinki.fi/u/goethals/software/

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

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

Personalised recommendations