Mining Minimal High-Utility Itemsets

  • Philippe Fournier-Viger
  • Jerry Chun-Wei Lin
  • Cheng-Wei Wu
  • Vincent S. Tseng
  • Usef Faghihi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9827)


Mining high-utility itemsets (HUIs) is a key data mining task. It consists of discovering groups of items that yield a high profit in transaction databases. A major drawback of traditional high-utility itemset mining algorithms is that they can return a large number of HUIs. Analyzing a large result set can be very time-consuming for users. To address this issue, concise representations of high-utility itemsets have been proposed such as closed HUIs, maximal HUIs and generators of HUIs. In this paper, we explore a novel representation called the minimal high utility itemsets (MinHUIs), defined as the smallest sets of items that generate a high profit, study its properties, and design an efficient algorithm named MinFHM to discover it. An extensive experimental study with real-life datasets shows that mining MinHUIs can be much faster than mining other concise representations or all HUIs, and that it can greatly reduce the size of the result set presented to the user.


Utility mining High-utility itemsets Minimal itemsets 


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Philippe Fournier-Viger
    • 1
  • Jerry Chun-Wei Lin
    • 2
  • Cheng-Wei Wu
    • 3
  • Vincent S. Tseng
    • 3
  • Usef Faghihi
    • 4
  1. 1.School of Natural Sciences and HumanitiesHarbin Institute of Technology Shenzhen Graduate SchoolShenzhenChina
  2. 2.School of Computer Science and Technology, Shenzhen Graduate SchoolHarbin Institute of Technology Shenzhen Graduate SchoolShenzhenChina
  3. 3.Department of Computer ScienceNational Chiao Tung UniversityHsinchuTaiwan
  4. 4.Department of Computer Science and MathematicsUniversity of IndianapolisIndianapolisUSA

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