CARIM : an efficient algorithm for mining class-association rules with interestingness measures

Abstract EN

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.