A Fast Algorithm for Maintenance of Association Rules in Incremental Databases

  • Xin Li
  • Zhi-Hong Deng
  • Shiwei Tang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4093)


In this paper, we propose an algorithm for maintaining the frequent itemsets discovered in a database with minimal re-computation when new transactions are added to or old transactions are removed from the transaction database. An efficient algorithm called EFPIM (Extending FP-tree for Incremental Mining), is designed based on EFP-tree (extended FP-tree) structures. An important feature of our algorithm is that it requires no scan of the original database, and the new EFP-tree structure of the updated database can be obtained directly from the EFP-tree of the original database. We give two versions of EFPIM algorithm, called EFPIM1 (an easy vision to implement) and EFPIM2 (a fast algorithm), they both mining frequent itemsets of the updated database based on EFP-tree. Experimental results show that EFPIM outperforms the existing algorithms in terms of the execution time.


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  1. 1.
    Agrawal, R., Imielinski, T., Swami, A.: Mining Association Rules between Sets of Items in Large Databases. In: Proceedings of ACM SIGMOD, May 1993, pp. 207–216 (1993)Google Scholar
  2. 2.
    Agrawal, R., Srikant, R.: Fast algorithm for mining Association rules. In: VLDB 1994, pp. 487–499 (1994)Google Scholar
  3. 3.
    Park, J.S., et al.: An effective hash based algorithm for mining of association rules. In: Proceedings of ACM SIGMOD Conference on Management of Data, May 1995, pp. 175–186 (1995)Google Scholar
  4. 4.
    Han, J., Pei, J., Yin, Y.: Mining Frequent Patterns without Candidate Generation. In: Proceedings of the ACM SIGMOD Int. Conf. on Management of Data, pp. 1–12 (2000)Google Scholar
  5. 5.
    Han, J., Pei, J.: Mining frequent patterns by pattern-growth: methodology and implications. In: SIGKDD 2000, pp. 14–20 (2000)Google Scholar
  6. 6.
    Zaki, Gouda, K.: Fast vertical mining using diffsets. In: SIGKDD 2003, pp. 326–335 (2003)Google Scholar
  7. 7.
    Cheung, D.W., Han, J., Ng, V.T., Wong, C.Y.: Maintenance of Discovered Association Rules in Large Databases: An Incremental Update Technique. In: Proceedings of International Conference on Data Engineering, pp. 106–114 (1996)Google Scholar
  8. 8.
    Cheung, D.W., Lee, S.D., Kao, B.: A General Incremental Technique for Maintaining Discovered Association Rules. In: Proc. of the 5th International Conference on Database Systems for Advanced Applications, pp. 185–194 (1997)Google Scholar
  9. 9.
    Thomas, S., Bodagala, S., Alsabti, K., Ranka, S.: An Efficient Algorithm for the Incremental Updation of Association Rules in Large Databases. In: Proc. of 3rd International conference on Knowledge Discovery and Data Mining, pp. 263–266 (1997)Google Scholar
  10. 10.
    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
  11. 11.
    Burdick, D., Calimlim, M., Gehrke, J.: MAFIA: A maximal frequent itemset algorithm for transactional databases. In: ICDE 2001, pp. 443–452 (2001)Google Scholar
  12. 12.
    Zaki, M., Hsiao, C.: CHARM: An efficient algorithm for closed itemset mining. In: SDM 2002, pp. 12–28 (2002)Google Scholar
  13. 13.
    Wang, J.Y., Han, J., Pei, J.: CLOSET+: Searching for the Best Strategies for Mining Frequent Closed Itemsets. In: SIGKDD 2003, pp. 236–245 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xin Li
    • 1
  • Zhi-Hong Deng
    • 1
  • Shiwei Tang
    • 1
  1. 1.National Laboratory on Machine Perception, School of Electronics Engineering and Computer SciencePeking UniversityBeijingChina

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