Mexican International Conference on Artificial Intelligence

Advances in Artificial Intelligence and Soft Computing pp 530-546 | Cite as

EFIM: A Highly Efficient Algorithm for High-Utility Itemset Mining

  • Souleymane Zida
  • Philippe Fournier-Viger
  • Jerry Chun-Wei Lin
  • Cheng-Wei Wu
  • Vincent S. Tseng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9413)


High-utility itemset mining (HUIM) is an important data mining task with wide applications. In this paper, we propose a novel algorithm named EFIM (EFficient high-utility Itemset Mining), which introduces several new ideas to more efficiently discovers high-utility itemsets both in terms of execution time and memory. EFIM relies on two upper-bounds named sub-tree utility and local utility to more effectively prune the search space. It also introduces a novel array-based utility counting technique named Fast Utility Counting to calculate these upper-bounds in linear time and space. Moreover, to reduce the cost of database scans, EFIM proposes efficient database projection and transaction merging techniques. An extensive experimental study on various datasets shows that EFIM is in general two to three orders of magnitude faster and consumes up to eight times less memory than the state-of-art algorithms d\(^2\)HUP, HUI-Miner, HUP-Miner, FHM and UP-Growth+.


High-utility mining Itemset mining Pattern mining 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Souleymane Zida
    • 1
  • Philippe Fournier-Viger
    • 1
  • Jerry Chun-Wei Lin
    • 2
  • Cheng-Wei Wu
    • 3
  • Vincent S. Tseng
    • 3
  1. 1.Department of Computer ScienceUniversity of MonctonMonctonCanada
  2. 2.School of Computer Science and TechnologyHarbin Institute of Technology Shenzhen Graduate SchoolShenzhenChina
  3. 3.Department of Computer ScienceNational Chiao Tung UniversityHsinchuTaiwan

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