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Improved Data Mining Algorithms For Frequent Patterns With Composite Items

In: Challenges In Information Technology Management

Author

Listed:
  • KE WANG

    (School of Economics and Management, Beihang University, Beijing, P.R.China)

  • JAMES N. K. LIU

    (Department of Computing, Hong Kong Polytechnic University, Hong Kong, P.R.China)

  • WEI-MIN MA

    (School of Economics and Management, Tongji University, Shanghai, P.R.China)

Abstract

Mining association rules are used to analyze the data in a database to discover interesting rules. The algorithms for mining association rules with composite items have the potential to discover rules which cannot be found out by algorithms without composite items. Algorithms for finding large composite items should scan the database for every candidate composite item to determine whether it is large. In this paper, we design some improved algorithms for finding large composite items which only need to scan the database one time to find all the large composite items. This algorithm also allows the reduction of many more redundant candidate composite items.

Suggested Citation

  • Ke Wang & James N. K. Liu & Wei-Min Ma, 2008. "Improved Data Mining Algorithms For Frequent Patterns With Composite Items," World Scientific Book Chapters, in: Man-Chung Chan & Ronnie Cheung & James N K Liu (ed.), Challenges In Information Technology Management, chapter 2, pages 10-16, World Scientific Publishing Co. Pte. Ltd..
    • Handle: RePEc:wsi:wschap:9789812819079_0002
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