Discovery of maximum length frequent itemsets

  • Authors:

  • Tianming Hu;Sam Yuan Sung;Hui Xiong;Qian Fu

  • Affiliations:

  • East China Normal University, DongGuan University of Technology, China;Department of Computer Science, South Texas College, USA;MSIS Department, Rutgers University, USA;Department of Computer Science, National University of Singapore, Singapore

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2008

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Abstract

The use of frequent itemsets has been limited by the high computational cost as well as the large number of resulting itemsets. In many real-world scenarios, however, it is often sufficient to mine a small representative subset of frequent itemsets with low computational cost. To that end, in this paper, we define a new problem of finding the frequent itemsets with a maximum length and present a novel algorithm to solve this problem. Indeed, maximum length frequent itemsets can be efficiently identified in very large data sets and are useful in many application domains. Our algorithm generates the maximum length frequent itemsets by adapting a pattern fragment growth methodology based on the FP-tree structure. Also, a number of optimization techniques have been exploited to prune the search space. Finally, extensive experiments on real-world data sets validate the proposed algorithm.