Two-phase algorithms for a novel utility-frequent mining model

Authors:
Jieh-Shan Yeh;Yu-Chiang Li;Chin-Chen Chang
Affiliations:
Department of Computer Science and Information Management, Providence University, Taichung, Taiwan;Department of Computer Science and Information Engineering, Southern Taiwan University, Yung-Kang City, Tainan, Taiwan;Department of Information Engineering and Computer Science, Feng Chia University, Taichung, Taiwan and Department of Computer Science and Information Engineering, National Chung Cheng University, ...
Venue:
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
Year:
2007

Citing 13
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Abstract

When companies seek for the combination of products which can constantly generate high profit, the association rule mining (ARM) or the utility mining will not achieve such task. ARM mines frequent itemsets without knowing the producing profit. On the other hand, the utility mining seeks high profit items but no guarantee the frequency. In this paper, we propose a novel utility-frequent mining model to identify all itemsets that can generate a user specified utility in transactions, in which the percentage of such transactions in database is not less than a minimum support threshold. A utility-frequent itemset indicates that such combination of products can constantly generate high profit. For finding all utility-frequent itemsets, there is no efficient strategy due to the nonexistence of "downward/upward closure property". In order to tackle such challenge, we propose a bottom-up two-phase algorithm, BU-UFM, for efficiently mining utility-frequent itemsets. We also introduce a novel concept, quasi-utility-frequency, which is upward closed with respect to the lattice of all itemsets. In fact, each utility-frequent itemset is also quasi-utility-frequent. A top-down two-phase algorithm, TD-UFM, for mining utility-frequent itemsets is also presented in the paper.