FHM: Faster High-Utility Itemset Mining Using Estimated Utility Co-occurrence Pruning

  • Philippe Fournier-Viger
  • Cheng-Wei Wu
  • Souleymane Zida
  • Vincent S. Tseng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8502)

Abstract

High utility itemset mining is a challenging task in frequent pattern mining, which has wide applications. The state-of-the-art algorithm is HUI-Miner. It adopts a vertical representation and performs a depth-first search to discover patterns and calculate their utility without performing costly database scans. Although, this approach is effective, mining high-utility itemsets remains computationally expensive because HUI-Miner has to perform a costly join operation for each pattern that is generated by its search procedure. In this paper, we address this issue by proposing a novel strategy based on the analysis of item co-occurrences to reduce the number of join operations that need to be performed. An extensive experimental study with four real-life datasets shows that the resulting algorithm named FHM (Fast High-Utility Miner) reduces the number of join operations by up to 95 % and is up to six times faster than the state-of-the-art algorithm HUI-Miner.

Keywords

Frequent pattern mining high-utility itemset mining co-occurrence pruning transaction database 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Philippe Fournier-Viger
    • 1
  • Cheng-Wei Wu
    • 2
  • Souleymane Zida
    • 1
  • Vincent S. Tseng
    • 2
  1. 1.Dept. of Computer ScienceUniversity of MonctonCanada
  2. 2.Dept. of Computer Science and Information EngineeringNational Cheng Kung UniversityTaiwan

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