Skip to main content

On-Line Association Rules Mining with Dynamic Support

  • Conference paper
Knowledge-Based Intelligent Information and Engineering Systems (KES 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4252))

  • 2868 Accesses

Abstract

In this paper, we use maximal itemsets to represent itemsets in a database. We show that the set of supreme covers, which are the maximal itemsets whose proper subsets are not maximal itemsets, induces an equivalence relation on the set itemsets. Based on maximal itemsets, we propose a large itemset generation algorithm with dynamic support, which runs in time O(M′2N+MlogM), where N is the maximum number of items in a maximal itemset, M′ is the number of the maximal itemsets with minimum support greater than the required support, and M is the number of the maximal itemsets.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Agrawal, R., Imielinksi, T., Swami, A.: Mining association rules between sets of items in large database. In: ACM SIGMOD conference, Washington DC, USA, pp. 207–216 (1993)

    Google Scholar 

  2. Agrawal, R., Imielinksi, T., Swami, A.: Database mining: a performance perspective. IEEE Transactions on Knowledge and Data Engineering 5(6), 914–925 (1993)

    Article  Google Scholar 

  3. Arawal, R., Srikant, R.: Fast algorithm for mining association rules. In: ACM International conference on Very Large Data Bases, pp. 487–499 (1994)

    Google Scholar 

  4. Agrawal, R., Srikant, R.: Mining sequential patterns. In: IEEE International Conferences on Data Engineering, pp. 3–14 (1995)

    Google Scholar 

  5. Brin, S., Motwani, R., Ullman, J.D., Tsur, S.: Dynamic itemset counting and implication rules for market basket data. In: ACM SIGMOD Conference, Tucson, Arizona, USA, pp. 255–264 (1997)

    Google Scholar 

  6. Cheung, D.W., Han, J., Ng, V.T., Wong, C.Y.: Maintenance of discovered association rules in large databases: an incremental updating approach. In: IEEE International Conference on Data Engineering, pp. 106–114 (1996)

    Google Scholar 

  7. Cheung, D.W., Lee, S.D., Kao, B.: A general incremental technique for maintaining discovered association rules. In: Proceedings of Database Systems for Advanced Applications, Melbourne, Australia, pp. 185–194 (1997)

    Google Scholar 

  8. Feldman, R., Aumann, Y., Amir, A., Mannila, H.: Efficient algorithms for discovering frequent sets in incremental databases. In: ACM SIGMOD Workshop on DMKD, USA, pp. 59–66 (1997)

    Google Scholar 

  9. Hong, T.P., Wang, C.Y., Tao, Y.H.: A new incremental data mining algorithm using pre-large itemsets. International Journal on Intelligent Data Analysis (2001)

    Google Scholar 

  10. Lee, H.-S.: Incremental Association Mining Based on Maximal Itemsets. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds.) KES 2005. LNCS (LNAI), vol. 3681, pp. 365–371. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  11. Sarda, N.L., Srinvas, N.V.: An adaptive algorithm for incremental mining of association rules. In: IEEE International Workshop on Database and Expert Systems, pp. 240–245 (1998)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Lee, HS. (2006). On-Line Association Rules Mining with Dynamic Support. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science(), vol 4252. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893004_114

Download citation

  • DOI: https://doi.org/10.1007/11893004_114

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-46537-9

  • Online ISBN: 978-3-540-46539-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics