Skip to main content
Springer Nature Link
Log in

Towards Efficient Data Re-mining (DRM)

  • Conference paper
  • First Online:
Advances in Knowledge Discovery and Data Mining (PAKDD 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2035))

Included in the following conference series:

  • 1352 Accesses

Abstract

The problem that we tackle here is a practical one: When users interactively mine association rules, it is often the case that they have to continuously tune two thresholds: minimum support and minimum confidence, which describe the users’ changing requirements. In this paper, we present an efficient data re-mining (DRM) technique for updating previously discovered association rules in light of threshold changes.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Agrawal, R., Imielinski, T., and Swami, T.: Mining association rules between sets of items in large databases. Proceedings of the ACM-SIGMOD International Conference on Management of Data (1993) 207–216.

    Google Scholar 

  2. Agrawal, R. and Srikant, R.: Fast algorithm for mining association rules. Proceedings of the International Conference on Very Large Data Bases (1994) 487–499.

    Google Scholar 

  3. Srikant, R. and Agrawal, R.: Mining generalized association rules. Proceedings of the International Conference on Very Large Data Bases (1995) 407–419.

    Google Scholar 

  4. Park, J.S., Chen, M., and Yu, P.S.: An effective hash-based algorithm for mining association rules. Proceedings of the ACM-SIGMOD International Conference Management of Data (1995) 175–186.

    Google Scholar 

  5. Han, J., Cai, Y., and Cercone, N.: Data-driven discovery of quantitative rules in relational databases. IEEE Transactions on Knowledge and Data Engineering, 5(1):29–40, (1993).

    Article  Google Scholar 

  6. Pei, J., Han, J., and Mao, R.: CLOSET: An efficient algorithm for mining frequent closed itemsets. Proceedings of the ACM-SIGMOD International Workshop on Data Mining and Knowledge Discovery (DMKD’00) (2000).

    Google Scholar 

  7. Lee, S.D. and Cheung, D.W.: Maintenance of discovered association rules: When to update? Proceedings of the ACM-SIGMOD Workshop on Data Mining and Knowledge Discovery (DMKD’97) (1997).

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong

    Jiming Liu

  2. Department of Computer Science, Zhongshan University, Guangzhou, 510275, P. R. China

    Jian Yin

Authors
  1. Jiming Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jian Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Dept. of Computer Science and Information Systems, The University of Hong Kong, Pokfulam, Hong Kong China

    David Cheung

  2. CSIRO Mathematical and Information Sciences, GPO Box 664, Canberra, ACT 2601, Australia

    Graham J. Williams

  3. Department of Computer Science, City University of Hong Kong, 83 Tat Chee Ave., Kowloon, Hong Kong China

    Qing Li

Rights and permissions

Reprints and permissions

Copyright information

© 2001 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Liu, J., Yin, J. (2001). Towards Efficient Data Re-mining (DRM). In: Cheung, D., Williams, G.J., Li, Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2001. Lecture Notes in Computer Science(), vol 2035. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45357-1_43

Download citation

  • DOI: https://doi.org/10.1007/3-540-45357-1_43

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41910-5

  • Online ISBN: 978-3-540-45357-4

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics