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HUE-Span: Fast High Utility Episode Mining

  • Philippe Fournier-VigerEmail author
  • Peng Yang
  • Jerry Chun-Wei Lin
  • Unil Yun
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11888)

Abstract

High utility episode mining consists of finding episodes (sub-sequences of events) that have a high importance (e.g high profit) in a sequence of events with quantities and weights. Though it has important real-life applications, the current problem definition has two critical limitations. First, it underestimates the utility of episodes by not taking into account all timestamps of minimal occurrences for utility calculations, which can result in missing high utility episodes. Second, the state-of-the-art UP-Span algorithm is inefficient on large databases because it uses a loose upper bound on the utility to reduce the search space. This paper addresses the first issue by redefining the problem to guarantee that all high utility episodes are found. Moreover, an efficient algorithm named HUE-Span is proposed to efficiently find all patterns. It relies on a novel upper-bound to reduce the search space and a novel co-occurrence based pruning strategy. Experimental results show that HUE-Span not only finds all patterns but is also up to five times faster than UP-Span.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Philippe Fournier-Viger
    • 1
    Email author
  • Peng Yang
    • 2
  • Jerry Chun-Wei Lin
    • 3
  • Unil Yun
    • 4
  1. 1.School of Humanities and Social SciencesHarbin Institute of Technology (Shenzhen)ShenzhenChina
  2. 2.School of Computer Science and TechnologyHarbin Institute of Technology (Shenzhen)ShenzhenChina
  3. 3.Department of Computing, Mathematics and PhysicsWestern Norway University of Applied Sciences (HVL)BergenNorway
  4. 4.Department of Computer EngineeringSejong UniversitySeoulRepublic of Korea

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