A tree-based approach for frequent pattern mining from uncertain data

  • Authors:
  • Carson Kai-Sang Leung;Mark Anthony F. Mateo;Dale A. Brajczuk

  • Affiliations:
  • The University of Manitoba, Winnipeg, MB, Canada;The University of Manitoba, Winnipeg, MB, Canada;The University of Manitoba, Winnipeg, MB, Canada

  • Venue:
  • PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
  • Year:
  • 2008

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Abstract

Many frequent pattern mining algorithms find patterns from traditional transaction databases, in which the content of each transaction--namely, items--is definitely known and precise. However, there are many real-life situations in which the content of transactions is uncertain. To deal with these situations, we propose a tree-based mining algorithm to efficiently find frequent patterns from uncertain data, where each item in the transactions is associated with an existential probability. Experimental results show the efficiency of our proposed algorithm.