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A Survey of High Utility Itemset Mining

  • Philippe Fournier-VigerEmail author
  • Jerry Chun-Wei Lin
  • Tin Truong-Chi
  • Roger Nkambou
Chapter
Part of the Studies in Big Data book series (SBD, volume 51)

Abstract

High utility pattern mining is an emerging data science task, which consists of discovering patterns having a high importance in databases. The utility of a pattern can be measured in terms of various objective criterias such as its profit, frequency, and weight. Among the various kinds of high utility patterns that can be discovered in databases, high utility itemsets are the most studied. A high utility itemset is a set of values that appears in a database and has a high importance to the user, as measured by a utility function. High utility itemset mining generalizes the problem of frequent itemset mining by considering item quantities and weights. A popular application of high utility itemset mining is to discover all sets of items purchased together by customers that yield a high profit. This chapter provides an introduction to high utility itemset mining, reviews the state-of-the-art algorithms, their extensions, applications, and discusses research opportunities. This chapter is aimed both at those who are new to the field of high utility itemset mining, as well as researchers working in the field.

Notes

Acknowledgements

This work is supported by the National Science Fundation of China and Harbin Institute of Technology.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Philippe Fournier-Viger
    • 1
    Email author
  • Jerry Chun-Wei Lin
    • 2
  • Tin Truong-Chi
    • 3
  • Roger Nkambou
    • 4
  1. 1.Harbin Institute of Technology (Shenzhen)ShenzhenChina
  2. 2.Department of Computing Mathematics and PhysicsWestern Norway University of Applied Sciences (HVL)BergenNorway
  3. 3.University of DalatDalatVietnam
  4. 4.University of QuebecMontrealCanada

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