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An Efficient Algorithm for Distributed Incremental Updating of Frequent Item-Sets on Massive Database

  • Jiangtao Qiu
  • Changjie Tang
  • Lei Duan
  • Chuan Li
  • Shaojie Qiao
  • Peng Chen
  • Qihong Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4256)

Abstract

Incremental updating of frequent item-sets on a database includes three problems. In this paper, these problems are explored when database stores massive data. The main contributions include: (a) introduces the concept of Interesting Support Threshold; (b) proposes Frequent Item-sets Tree (FITr) with compact structure; (c) proposes and implements algorithm FIIU for frequent item-sets incremental updating; (d) in order to further improve performance, proposes the algorithm DFIIU for distributed incremental updating of frequent Item-sets on massive database; (e) gives extensive experiments to show that FIIU and DFIIU algorithms have better performance than traditional algorithm on massive database when the number of items is less.

Keywords

Association Rule Mining Association Rule Support Threshold Support Count Incremental Mining 
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 assciation rules between sets of items in large database. In: The ACM SIGMOD, Washington (1993)Google Scholar
  2. 2.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proc. the 20th International Conference on VLDB, Santago, pp. 487–499 (1994)Google Scholar
  3. 3.
    Park, J.S., et al.: An efficient hash-based algorithm for mining association rules. In: Proc. 1995 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD 1995), San Jose, CA (1995)Google Scholar
  4. 4.
    Savasere, A., et al.: An efficient algorithm for mining association rules in large databases. In: Proc. 1995 Int. Conf. Very Large Data Bases (VLDB 1995), Zurich, Switzerland (1995)Google Scholar
  5. 5.
    Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: Proc. 2000 ACMSIGMOD Int. Conf. Management of Data (SIGMOD 2000) (2000)Google Scholar
  6. 6.
    Cheung, W., Zaiane, O.R.: Incremental Mining of Frequent Patterns Without Candidate Generation or Support Constraint. In: Proc. IDEAS 2003, Hong Kong (2003)Google Scholar
  7. 7.
    Zhu, Y., Sun, Z., Ji, X.: Incremental Updating Algorithm Based on Frequent Pattern Tree fof Mining Association Rules. Chinease Journal of Computers 26(1), 91–96 (2003)Google Scholar
  8. 8.
    Ma, X.-L., Tong, Y.-H.: Efficient Incremental Maintence of Frequent Patterns with FP-Tree. J. Computer Science and Technology 19(6), 876–884 (2004)CrossRefGoogle Scholar
  9. 9.
    Agrawal, R., Shafer, J.C.: Parallel mining of association rules. IEEE Transaction on Knowledge and Data Engineering 8(6), 962–969 (1996)CrossRefGoogle Scholar
  10. 10.
    Han, E.H., Karypis, G., Kumar, V.: Scalable parallel data minig for association rules. In: Proc. ACM SIGMOD International Conference on Management of Data (SIGMOD 1997) (1997)Google Scholar
  11. 11.
    Zhang, Z.G.: Study of Data Mining Algorithms on Massive Data. Harbin Institute of Technology, Harbin (2003)Google Scholar
  12. 12.
    Brin, S., Motwani, R., Silverstein, C.: Generalizing association rules to correlations. In: Proc. of the ACM SIGMOD Int’l. Conf. on Management of Data (ACM SIGMOD 1997) (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jiangtao Qiu
    • 1
  • Changjie Tang
    • 1
  • Lei Duan
    • 1
  • Chuan Li
    • 1
  • Shaojie Qiao
    • 1
  • Peng Chen
    • 1
  • Qihong Liu
    • 1
  1. 1.School of Computer ScienceSichuan UniversityChengduChina

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