Constructing Complete FP-Tree for Incremental Mining of Frequent Patterns in Dynamic Databases

  • Muhaimenul Adnan
  • Reda Alhajj
  • Ken Barker
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4031)


This paper proposes a novel approach that extends the FP-tree in two ways. First, the tree is maintained to include every attribute that occurs at least once in the database. This facilitates mining with different support values without constructing several FP-trees to satisfy the purpose. Second, the tree is manipulated in a unique way that reflects updates to the corresponding database by scanning only the updated portion, thereby reducing execution time in general. Test results on two datasets demonstrate the applicability, efficiency and effectiveness of the proposed approach.


Association Rule Frequent Pattern Frequent Itemsets Cumulative Frequency Association Rule 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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Adnan, M., Alhajj, R., Barker, K.: Performance Analysis of Incremental Update of Association Rules Mining Approaches. In: Proceedings IEEE INES, Greece (September 2005)Google Scholar
  2. 2.
    Amir, Feldman, R., Kashi, R.: A New and Versatile Method for Association Generation. Information Systems 22(6), 333–347 (1999)Google Scholar
  3. 3.
    Agrawal, R., Imielinski, T., Swami, A.: Mining Association Rules between Sets of Items in Large Databases. In: Proceedings of ACM-SIGMOD, Washington D.C, pp. 207–216 (May 1993)Google Scholar
  4. 4.
    Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: Proceedings of VLDB, pp. 487–499 (September 1994)Google Scholar
  5. 5.
    Brijs, T., Swinnen, G., Vanhoof, K., Wets, G.: The Use of Association Rules for Product Assortment Decisions: A Case Study. In: Proceedings of ACM-KDD, San Diego, pp. 254–260 (1999)Google Scholar
  6. 6.
    Cheung, D.W., Han, J., Ng, V.T., Wong, C.Y.: Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique. In: Proceedings of IEEE-ICDE, pp. 106–114 (1996)Google Scholar
  7. 7.
    Cheung, D.W., Ng, V.T., Tam, B.W.: Maintenance of Discovered Knowledge: A case in Multi-level Association Rules. In: Proceedings of ACM-KDD, pp. 307–310 (1996)Google Scholar
  8. 8.
    Cheung, D.W., Lee, S.D., Kao, B.: A general Incremental Technique for Mining Discovered Association Rules. In: Proceedings of DASFAA, pp. 185–194 (1997)Google Scholar
  9. 9.
    Koh, J.-L., Shieh, S.-F.: An Efficient Approach for Maintaining Association Rules Based on Adjusting FP-Tree Structures. In: Lee, Y., Li, J., Whang, K.-Y., Lee, D. (eds.) DASFAA 2004. LNCS, vol. 2973, pp. 417–424. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  10. 10.
    Das, Ng, W.K., Woon, Y.K.: Rapid Association Rule Mining. In: Proceedings of ACM-CIKM, pp. 474–481 (2001)Google Scholar
  11. 11.
    Ayan, N.F., Tansel, A.U., Arkun, E.: An Efficient Algorithm to Update Large Itemsets with Early Pruning. In: Proceedings of ACM SIGKDD (1999)Google Scholar
  12. 12.
    Feldman, R., Aumann, Y., Amir, A., Mannila, H.: Efficient Algorithms for Discovering Frequent Sets in Incremental Databases. In: Proceedings of the International Workshop Research Issues on Data Mining and Knowledge Discovery (1997)Google Scholar
  13. 13.
  14. 14.
    Ganti, V., Gehrke, J.E., Ramakrishnan, R.: DEMON: Mining and Monitoring Evolving Data. IEEE TKDE 13(1), 50–63 (2001)Google Scholar
  15. 15.
    Han, J., Pei, J., Yin, Y.: Mining Frequent Patterns without Candidate Generation. In: Proc. of ACM-SIGMOD, Dallas, TX, pp. 1–12 (May 2000)Google Scholar
  16. 16.
    Thomas, S., Bodagala, S., Alsabti, K., Ranka, S.: An Efficient Algorithm for the Incremental Updation of Association Rules in Large Databases. In: Proceedings of ACM-SIGKDD, pp. 263–266 (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Muhaimenul Adnan
    • 1
  • Reda Alhajj
    • 1
    • 2
  • Ken Barker
    • 1
  1. 1.Department of Computer ScienceUniversity of CalgaryCalgaryCanada
  2. 2.Department of Computer ScienceGlobal UniversityBeirutLebanon

Personalised recommendations