Efficiently mining high average-utility itemsets with an improved upper-bound strategy

Guo Cheng Lan, Tzung Pei Hong*, S. Tseng

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

39 Scopus citations

Abstract

Utility mining has recently been discussed in the field of data mining. A utility itemset considers both profits and quantities of items in transactions, and thus its utility value increases with increasing itemset length. To reveal a better utility effect, an average-utility measure, which is the total utility of an itemset divided by its itemset length, is proposed. However, existing approaches use the traditional average-utility upper-bound model to find high average-utility itemsets, and thus generate a large number of unpromising candidates in the mining process. The present study proposes an improved upper-bound approach that uses the prefix concept to create tighter upper bounds of average-utility values for itemsets, thus reducing the number of unpromising itemsets for mining. Results from experiments on two real databases show that the proposed algorithm outperforms other mining algorithms under various parameter settings.

Original languageEnglish
Pages (from-to)1009-1030
Number of pages22
JournalInternational Journal of Information Technology and Decision Making
Volume11
Issue number5
DOIs
StatePublished - 1 Sep 2012

Keywords

  • average-utility mining
  • Data mining
  • high average-utility itemsets
  • prefix concept
  • upper-bound strategy

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