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An Efficient Approach for Maintaining Association Rules Based on Adjusting FP-Tree Structures

  • Jia-Ling Koh
  • Shui-Feng Shieh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2973)

Abstract

In this study, a general incremental updating technique is proposed for maintaining the frequent itemsets discovered in a database in the cases including insertion, deletion, and modification of transactions in the database. An efficient algorithm, called AFPIM (Adjusting FP-tree for Incremental Mining), is designed based on adjusting FP-tree structures. Our approach uses a FP-tree structure to store the compact information of transactions involving frequent and pre-frequent items in the original database. In most cases, without needing to rescan the original database, the new FP-tree structure of the updated database can be obtained by adjusting FP-tree of the original database according to the changed transactions. Experimental results show that AFPIM outperforms the existing algorithms in terms of the execution time.

Keywords

Association Rule Child Node Minimum Support Frequent Itemsets Frequent Item 
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.

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References

  1. 1.
    Agrawal, R., Srikant, R.: Fast Algorithm for Mining Association Rule in Large Databases. In: Proc. of The 20th International Conference on Very Large Data Bases (1994)Google Scholar
  2. 2.
    Ayan, N.F., Tansel, A.U., Arkun, E.: An Efficient Algorithm to Update Large Itemsets with Early Pruning. In: Proc. of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (1999)Google Scholar
  3. 3.
    Cheung, D.W., Han, J., Ng, V.T., Wong, C.Y.: Maintenance of Discovered Association Rules in Large Databases: An Incremental Update Technique. In: Proc. of the 12th International Conference on Data Engineering (1996)Google Scholar
  4. 4.
    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 (1997)Google Scholar
  5. 5.
    Han, J., Pei, J., Yin, Y.: Mining Frequent Patterns without Candidate Generation. In: Proc. of the ACM SIGMOD Int. Conf. on Management of Data (2000)Google Scholar
  6. 6.
    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 (1997)Google Scholar
  7. 7.
    Wang, K., Tang, L., Han, J., Liu, J.: Top down FP-Growth for Association Rule Mining. In: To appear in the 6th Pacific Area Conference on Knowledge Discovery and Data Mining (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Jia-Ling Koh
    • 1
  • Shui-Feng Shieh
    • 1
  1. 1.Department of Information and Computer EducationNational Taiwan Normal UniversityTaipeiTaiwan R.O.C

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