Enhancing the pruning performance in on-shelf utility mining

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


In real-world applications, not all the products in stores are always on shelf for sale. On-shelf utility mining was thus proposed to deal with the problem. In this study, we extend our previous approaches with an on-shelf utility upper bound to early prune the unpromising candidates in the mining process. Especially, all candidates generated in each time period are utilized to get more accurate upper bound values of itemsets. Experimental results also show its performance.

Original languageEnglish
Pages (from-to)3749-3754
Number of pages6
JournalICIC Express Letters
Issue number10
StatePublished - 1 Oct 2011


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

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