Towards Efficient Re-mining of Frequent Patterns upon Threshold Changes

  • Xiu-li Ma
  • Shi-wei Tang
  • Dong-qing Yang
  • Xiao-ping Du
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2419)

Abstract

Mining of frequent patterns has been studied popularly in data mining area. However, very little work has been done on the problem of updating mined patterns upon threshold changes, in spite of its practical benefits. When users interactively mine frequent patterns, one difficulty is how to select an appropriate minimum support threshold. So, it is often the case that they have to continuously tune the threshold. A direct way is to re-execute the mining procedure many times with varied thresholds, which is nontrivial in large database. In this paper, an efficient Extension and Re-mining algorithm is proposed for update of previously discovered frequent patterns upon threshold changes. The algorithm proposed in this paper has been implemented and its performance is compared with re-running FP-growth algorithm under different thresholds. The study shows that our algorithm is significantly faster than the latter, especially when mining long frequent patterns in large databases.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    R. Agrawal and R. Srikant. Fast algorithm for mining Association rules. In VLDB’94, (1994) 487–499.Google Scholar
  2. 2.
    S. Brin, R. Motwani, and C. Silverstein. Beyond market basket: Generatalizing association rules to correlations. In SIGMOD’97, (1997) 265–276.Google Scholar
  3. 3.
    R. Agrawal and R. Srikant. Mining sequential patterns. In ICDE’95, (1995) 3–14.Google Scholar
  4. 4.
    J. Han, J. Pei, Y. Yin. Mining frequent patterns without candidate generation. In SIGMOD’00, (2000) 1–12Google Scholar
  5. 5.
    J. Han and J. Pei Mining frequent patterns by pattern-growth: methodology and implications In SIGKDD’00, (2000) 14–20.Google Scholar
  6. 6.
    D. Cheung, J. Han, V. Ng, C. Wong Maintenance of discovered association rules in large databases: An incremental updating technique. In ICDE’96. (1996)Google Scholar
  7. 7.
    D. Cheung, S. Lee, B. Kao A general incremental technique for maintaining discovered association rules. In Proceedings of the 5th International Conference on Database Systems for Advanced Applications, (1997).Google Scholar
  8. 8.
    S. Lee and D. Cheung Maintenance of discovered association rules: When to update? In DMKD’97. (1997)Google Scholar
  9. 9.
    Feng Yu-cai, Feng Jian-lin Incremental updating algorithms for Mining association rules In Journal Of Software, Vol. 9, No. 4, (1998) 301–306.Google Scholar
  10. 10.
    OU-YANG Weimin, CAI Qing-sheng An incremental updating technique for discovered generalized sequential patterns In Journal Of Software, Vol. 9, No. 10, (1998) 777–780.Google Scholar
  11. 11.
    J. Liu and J. Yin Towards efficient data re-mining (DRM) In PAKDD’01, (2001). 406–412.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Xiu-li Ma
    • 1
  • Shi-wei Tang
    • 1
    • 2
  • Dong-qing Yang
    • 1
  • Xiao-ping Du
    • 2
  1. 1.Department of Computer Science and TechnologyPeking UniversityBeijingChina
  2. 2.National Laboratory on Machine Perception, Center for Information SciencePeking UniversityBeijingChina

Personalised recommendations