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Mining Large Patterns with Profit-Based Support in e-Commerce

  • Jin-Guk Jung
  • Supratip Ghose
  • Geun-Sik Jo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4304)

Abstract

In this paper, we propose an unique profit criterion as a new minimum support threshold for each item and exploit the criterion as multiple minimum supports in our algorithm. We then apply our profit-based association rule mining algorithm to generate large itemsets and show the result of our experiment. Experiment results carried on synthetic data set show that the proposed approach is efficient and effective in terms of reducing candidate itemsets and generating more profitable itemsets respectively.

Keywords

Association Rule Minimum Support Association Rule Mining Minimum Support Threshold Weighted Algorithm 
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.

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References

  1. 1.
  2. 2.
    Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proc. of the ACM SIGMOD conference on Management of data, pp. 207–216 (1993)Google Scholar
  3. 3.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proc. of the conference on Very Large Databases, pp. 487–499 (1994)Google Scholar
  4. 4.
    Brin, S., Motwani, R., Ullman, J.D., Tsur, S.: Dynamic itemset counting and implication rules for market basket data. In: Proc. of the ACM SIGMOD conference on Management of data, pp. 255–264 (1997)Google Scholar
  5. 5.
    Han, J., Fu, Y.: Discovery of multiple-level association rules from large databases. In: Proc. of 1995 international conference on Very Large Databases, pp. 420–431 (1995)Google Scholar
  6. 6.
    Liu, B., Hsu, W., Ma, Y.: Mining association rules with multiple minimum supports. In: Proc. of the ACM SIGKDD conference on KDD, pp. 337–341 (1999)Google Scholar
  7. 7.
    Tao, F., Murtagh, F., Farid, M.: Weighted association rule mining using weighted support and significance framework. In: Proc. of The ACM SIGKDD conference on KDD, pp. 661–666 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jin-Guk Jung
    • 1
  • Supratip Ghose
    • 1
  • Geun-Sik Jo
    • 2
  1. 1.Intelligent e-Commerce Systems Laboratory, School of Computer EngineeringInha UniversityIncheonKorea
  2. 2.School of Computer EngineeringInha UniversityIncheonKorea

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