Advertisement

Research and Application in Web Usage Mining of the Incremental Mining Technique for Association Rule

  • Sulan Zhang
  • Zhongzhi Shi
Conference paper
Part of the IFIP International Federation for Information Processing book series (IFIPAICT, volume 163)

Abstract

The paper analyzes some existing incremental mining algorithms for association rule and presents an incremental mining algorithm for association rule fit for Web Usage Mining. Because there are some characteristics of web logs which are dynamic, attributed, smaller and updated frequently, the algorithm uses BORDERS algorithm when mining single log file, and takes advantage of partition algorithm when mining many log files simultaneously.

Key words

Web Usage Mining association rule access patterns incremental mining 

References

  1. 1.
    D. Cheung, J. Han, V. Ng and C.Y. Wong. Maintenance of discovered association rules in large databases: An incremental updating technique. In ICDE’96, New Orleans, Louisiana, USA, Feb. 1996Google Scholar
  2. 2.
    D. Cheung, S. Lee and B. Kao. A general incremental technique for maintaining discovered association rules. In Proc. Of the 5th International Conference on Database Systems for Advanced Applications, Melbourne, Australia, April 1–4, 1997Google Scholar
  3. 3.
    S. Lee and D. Cheung. maintenance of discovered association rules: When to update? In DMKD’97, Tucson, Arizona, May. 1997Google Scholar
  4. 4.
    S. Thomas, S. Bodagala, K. Alsabti and S. Ranka. An Efficient Algorithm for the Incremental Updation of Association Rules in Large Databases. In KDD’97, New Port Beach, California, Aug. 1997Google Scholar
  5. 5.
    R. Feldman, Y. Aumann, A. Amir and H. Mannila. Efficient Algorithms for Discovering Frequent Sets in Incremental Databases. In DMKD’97, Tucson, Arizona, May. 1997Google Scholar
  6. 6.
    H. Toivonen. Sampling Large Databases for Association Rules. In VLDB’96, pp. 134–145Google Scholar
  7. 7.
    A. Savasere, E. Omiecinski, and S. Navathe. An efficient algorithm for mining association rules in large databases. Proceedings of the 21st International Conference on Very large Database, 1995, pp.432–444Google Scholar

Copyright information

© International Federation for Information Processing 2005

Authors and Affiliations

  • Sulan Zhang
    • 1
  • Zhongzhi Shi
    • 1
  1. 1.Key Lab of Intelligent Information Processing, Institute of Computing TechnologyChinese Academy of SciencesBeijingChina

Personalised recommendations