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Journal of Information Science and Engineering, Vol. 28 No. 1, pp. 193-209 (January 2012)

A Projection-Based Approach for Discovering High Average-Utility Itemsets

GUO-CHENG LAN1, TZUNG-PEI HONG* AND VINCENT S. TSENG1,2
1Department of Computer Science and Information Engineering
2Institute of Medical Informatics
National Cheng Kung University
Tainan, 701 Taiwan
E-mail: rrfoheiay@gmail.com; tsengsm@mail.ncku.edu.tw
*Department of Computer Science and Information Engineering
National University of Kaohsiung
Kaohsiung, 811 Taiwan
*Department of Computer Science and Engineering
National Sun Yat-sen University
Kaohsiung, 804 Taiwan
E-mail: tphong@nuk.edu.tw

Utility mining has recently been an important issue due to its wide applications. An itemset in traditional utility mining considers individual profits and quantities of items in transactions regardless of its length. The average-utility measure, which is the total utility of an itemset divided by its number of items within it, was then proposed to reveal a better utility effect than the original utility measure. A mining algorithm was also proposed to find high average-utility itemsets from a transaction database. However, the previous mining approach was based on the principle of level-wise processing to find high average-utility itemsets from a database. In this paper, we thus propose an efficient average-utility mining approach which adopts a projection technique and an indexing mechanism to speed up the execution and reduce the memory requirement in the mining process. The proposed approach can project relevant sub-databases for mining, thus avoiding some unnecessary checking. In addition, a pruning strategy is also designed to reduce the number of unpromising itemsets in mining. Finally, the experimental results on synthetic datasets and two real datasets show the superior performance of the proposed approach.

Keywords: data mining, association rule mining, utility mining, average utility, indexing mechanism

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Received March 1, 2011; revised August 7, 2011; accepted August 27, 2011.
Communicated by I-Chen Wu.