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Mining Frequent Itemsets from Uncertain Data

  • Chun-Kit Chui
  • Ben Kao
  • Edward Hung
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4426)

Abstract

We study the problem of mining frequent itemsets from uncertain data under a probabilistic framework. We consider transactions whose items are associated with existential probabilities and give a formal definition of frequent patterns under such an uncertain data model. We show that traditional algorithms for mining frequent itemsets are either inapplicable or computationally inefficient under such a model. A data trimming framework is proposed to improve mining efficiency. Through extensive experiments, we show that the data trimming technique can achieve significant savings in both CPU cost and I/O cost.

Keywords

Association Rule Frequent Itemsets Uncertain Data Support Threshold Support Count 
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 Berlin Heidelberg 2007

Authors and Affiliations

  • Chun-Kit Chui
    • 1
  • Ben Kao
    • 1
  • Edward Hung
    • 2
  1. 1.Department of Computer Science, The University of Hong Kong, PokfulamHong Kong
  2. 2.Department of Computing, Hong Kong Polytechnic University, KowloonHong Kong

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