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Mining Frequent Itemsets with Category-Based Constraints

  • Tien Dung Do
  • Siu Cheung Hui
  • Alvis Fong
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2843)

Abstract

The discovery of frequent itemsets is a fundamental task of association rule mining. The challenge is the computational complexity of the itemset search space. One of the solutions for this is to use constraints to focus on some specific itemsets. In this paper, we propose a specific type of constraints called category-based as well as the associated algorithm for constrained rule mining based on Apriori. The Category-based Apriori algorithm reduces the computational complexity of the mining process by bypassing most of the subsets of the final itemsets. An experiment has been conducted to show the efficiency of the proposed technique.

Keywords

Association Rule Rule Mining Frequent Itemsets Association Rule Mining Support Threshold 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Tien Dung Do
    • 1
  • Siu Cheung Hui
    • 1
  • Alvis Fong
    • 1
  1. 1.School of Computer EngineeringNanyang Technological UniversitySingapore

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