On-shelf utility mining with an upper-bound measure

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


This paper handles the problem of finding high on-shelf utility itemsets, which considers not only individual profit and quantity of each item in a transaction but also on-shelf periods of the items. We have extended our previous approach by designing an efficient pruning strategy based on an on-shelf utility upper-bound measure to early prune the unpromising candidates in the mining process. Experimental results also show its performance. ICIC International

Original languageEnglish
Pages (from-to)2445-2450
Number of pages6
JournalICIC Express Letters
Issue number6 B
StatePublished - 1 Dec 2010


  • Data mining
  • High utility itemsets
  • On shelf
  • Upper bound
  • Utility mining

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