Loan Nguyen1,2, Bay Vo3, Tzung-Pei Hong4,5

1Division of Knowledge and System Engineering for ICT, Ton Duc Thang University, Vietnam

2Faculty of Information Technology, Ton Duc Thang University, Vietnam

3Faculty of Information Technology, Ho Chi Minh City University of Technology, Vietnam

4Department of CSIE, National University of Kaohsiung, Taiwan

5Department of CSE, National Sun Yat-sen University, Taiwan

Abstract: Classification based on association rules can often achieve higher accuracy than some traditional rule-based methods such as C4.5 and ILA. The right-hand-side part of an association rule is a value of the target (or class) attribute. This study proposes a general algorithm for mining class-association rules based on a variety of interestingness measures. The proposed algorithm uses a tree structure for maintaining the related information of itemsets in the nodes, thus speeding up the process of generation of rules. The proposed algorithm can be easily extended to integrate some measures together for ranking of rules. Experiments are also conducted to show the efficiency of the proposed approach under various settings.

Keywords: Accuracy, classification, class-association rule, interestingness measure, integration.

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