EFFICIENTLY MINING HIGH AVERAGE-UTILITY ITEMSETS WITH AN IMPROVED UPPER-BOUND STRATEGY

Guo-Cheng Lan (), Tzung-Pei Hong () and Vincent S. Tseng ()
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Guo-Cheng Lan: Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701, Taiwan
Tzung-Pei Hong: Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung 811, Taiwan;
Vincent S. Tseng: Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701, Taiwan;

International Journal of Information Technology & Decision Making (IJITDM), 2012, vol. 11, issue 05, 1009-1030

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.

Keywords: Data mining; average-utility mining; high average-utility itemsets; upper-bound strategy; prefix concept (search for similar items in EconPapers)
Date: 2012
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DOI: 10.1142/S0219622012500307

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