High utility itemsets mining

Ying Liu, Jianwei Li, Wei Keng Liao*, Alok Choudhary, Yong Shi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

High utility itemsets mining identifies itemsets whose utility satisfies a given threshold. It allows users to quantify the usefulness or preferences of items using different values. Thus, it reflects the impact of different items. High utility itemsets mining is useful in decision-making process of many applications, such as retail marketing and Web service, since items are actually different in many aspects in real applications. However, due to the lack of "downward closure property", the cost of candidate generation of high utility itemsets mining is intolerable in terms of time and memory space. This paper presents a Two-Phase algorithm which can efficiently prune down the number of candidates and precisely obtain the complete set of high utility itemsets. The performance of our algorithm is evaluated by applying it to synthetic databases and two real-world applications. It performs very efficiently in terms of speed and memory cost on large databases composed of short transactions, which are difficult for existing high utility itemsets mining algorithms to handle. Experiments on real-world applications demonstrate the significance of high utility itemsets in business decision-making, as well as the difference between frequent itemsets and high utility itemsets.

Original languageEnglish (US)
Pages (from-to)905-934
Number of pages30
JournalInternational Journal of Information Technology and Decision Making
Volume9
Issue number6
DOIs
StatePublished - Nov 1 2010

Keywords

  • Data mining
  • business intelligence
  • utility mining

ASJC Scopus subject areas

  • Computer Science (miscellaneous)

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