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Mining Generalized Association Rules with Multiple Minimum Supports

  • Ming-Cheng Tseng
  • Wen-Yang Lin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2114)

Abstract

Mining generalized association rules in the presence of the taxonomy has been recognized as an important model in data mining. Earlier work on generalized association rules confined the minimum support to be uniformly specified for all items or for items within the same taxonomy level. In this paper, we extended the scope of mining generalized association rules in the presence of taxonomy to allow any form of user-specified multiple minimum supports. We discussed the problems of using classic Apriori itemset generation and presented two algorithms, MMS Cumulate and MMS Stratify, for discovering the generalized frequent itemsets. Empirical evaluation showed that these two algorithms are very effective and have good linear scale-up characteristic....

Keywords

Association Rule Minimum Support Frequent Itemsets Mining Association Rule Large Data Base 
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.
    Agrawal, R., Imielinski T., Swami, A.: Mining association rules between sets of items in large databases. Proc. 1993 ACM-SIGMOD Int. Conf. Management of Data. Washington, D.C. (1993) 207–216Google Scholar
  2. 2.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. Proc. 20th Int. Conf. Very Large Data Bases. Santiago, Chile (1994) 487–499Google Scholar
  3. 3.
    Brin, S., Motwani, R., Ullman, J.D., Tsur, S.: Dynamic Itemset Counting and Implication Rules for Market-Basket Data. Proc. 1997 ACM-SIGMOD Int. Conf. Management of Data. (1997) 207–216Google Scholar
  4. 4.
    Han, J., Fu Y.: Discovery of multiple-level association rules from large databases. Proc. 21st Int. Conf. Very Large Data Bases. Zurich, Swizerland (1995) 420–431Google Scholar
  5. 5.
    Liu, B., Hsu, W., Ma, Y.: Mining association rules with multiple minimum supports. Proc. 1999 Int. Conf. Knowledge Discovery and Data Mining. (1999) 337–341Google Scholar
  6. 6.
    Park, J.S., Chen, M.S., Yu, P.S.: An effective hash-based algorithm for mining association rules. Proc. 1995 ACM-SIGMOD Int. Conf. Management of Data San Jose (1995) 175–186Google Scholar
  7. 7.
    Savasere, A., Omiecinski, E., Navathe, S.: An efficient algorithm for mining association rules in large databases. Proc. 21st Int. Conf. Very Large Data Bases. Zurich, Switzerland (1995) 432–444Google Scholar
  8. 8.
    Srikant, R., Agrawal, R.: Mining generalized association rules. Proc. 21st Int. Conf. Very Large Data Bases. Zurich, Switzerland (1995) 407–419Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Ming-Cheng Tseng
    • 1
  • Wen-Yang Lin
    • 2
  1. 1.Institute of Information EngineeringI-Shou UniversityKaohsiungTaiwan
  2. 2.Department of Information ManagementI-Shou UniversityKaohsiungTaiwan

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