Mining minimal high-utility itemsets

Philippe Fournier-Viger*, Jerry Chun Wei Lin, Cheng Wei Wu, S. Tseng, Usef Faghihi

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationDatabase and Expert Systems Applications - 27th International Conference, DEXA 2016, Proceedings
EditorsSven Hartmann, Hui Ma
PublisherSpringer Verlag
Pages88-101
Number of pages14
ISBN (Print)9783319444024
DOIs
StatePublished - 1 Jan 2016
Event27th International Conference on Database and Expert Systems Applications, DEXA 2016 - Porto, Portugal
Duration: 5 Sep 20168 Sep 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9827 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference27th International Conference on Database and Expert Systems Applications, DEXA 2016
CountryPortugal
CityPorto
Period5/09/168/09/16

Keywords

  • High-utility itemsets
  • Minimal itemsets
  • Utility mining

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  • Cite this

    Fournier-Viger, P., Lin, J. C. W., Wu, C. W., Tseng, S., & Faghihi, U. (2016). Mining minimal high-utility itemsets. In S. Hartmann, & H. Ma (Eds.), Database and Expert Systems Applications - 27th International Conference, DEXA 2016, Proceedings (pp. 88-101). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9827 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-44403-1_6