Mining frequent itemsets from uncertain data

  • Authors:
  • Chun-Kit Chui;Ben Kao;Edward Hung

  • Affiliations:
  • Department of Computer Science, The University of Hong Kong, Hong Kong;Department of Computer Science, The University of Hong Kong, Hong Kong;Department of Computing, Hong Kong Polytechnic University, Kowloon, Hong Kong

  • Venue:
  • PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
  • Year:
  • 2007

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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.