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Discovering High Utility Change Points in Customer Transaction Data

  • Philippe Fournier-VigerEmail author
  • Yimin Zhang
  • Jerry Chun-Wei Lin
  • Yun Sing Koh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11323)

Abstract

High Utility Itemset Mining (HUIM) consists of identifying all sets of items (itemsets) that have a high utility (e.g. have a high profit) in a database of customer transactions. Important limitations of traditional HUIM algorithms is that they do not consider that the utility of itemsets varies as time passes and that itemsets may not have a high utility in the whole database, but in some specific time periods. To overcome these drawbacks, this paper defines the novel problem of discovering change points of high utility itemsets, that is to find the time points where the utility of an itemset is changing considerably. An efficient algorithms named HUCP-Miner is proposed to mine these change points. Experimental results show that the proposed algorithm has excellent performance and can discover interesting patterns that are not identified by traditional HUIM algorithms.

Keywords

Pattern mining High-utility itemsets Change points 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Philippe Fournier-Viger
    • 1
    Email author
  • Yimin Zhang
    • 2
  • Jerry Chun-Wei Lin
    • 3
  • Yun Sing Koh
    • 4
  1. 1.School of Natural Sciences and HumanitiesHarbin Institute of Technology (Shenzhen)ShenzhenChina
  2. 2.School of Computer Sciences 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 SciencesUniversity of AucklandAucklandNew Zealand

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