An effective tree structure for mining high utility itemsets

Chun Wei Lin, Tzung Pei Hong, Wen Hsiang Lu

Research output: Contribution to journalArticlepeer-review

219 Citations (Scopus)


In the past, many algorithms were proposed to mine association rules, most of which were based on item frequency values. Considering a customer may buy many copies of an item and each item may have different profits, mining frequent patterns from a traditional database is not suitable for some real-world applications. Utility mining was thus proposed to consider costs, profits and other measures according to user preference. In this paper, the high utility pattern tree (HUP tree) is designed and the HUP-growth mining algorithm is proposed to derive high utility patterns effectively and efficiently. The proposed approach integrates the previous two-phase procedure for utility mining and the FP-tree concept to utilize the downward-closure property and generate a compressed tree structure. Experimental results also show that the proposed approach has a better performance than Liu et al.'s two-phase algorithm in execution time. At last, the numbers of tree nodes generated from three different item ordering methods are also compared, with results showing that the frequency ordering produces less tree nodes than the other two.

Original languageEnglish
Pages (from-to)7419-7424
Number of pages6
JournalExpert Systems With Applications
Issue number6
Publication statusPublished - 2011 Jun

All Science Journal Classification (ASJC) codes

  • General Engineering
  • Computer Science Applications
  • Artificial Intelligence


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