A novel method for constrained class association rule mining

Abstract

To create a classifier using an associative classification algorithm, a complete set of class association rules (CARs) is obtained from the training dataset. Most generated rules, however, are either redundant or insignificant. They not only confuse end users during decision-making but also decrease the performance of the classification process. Thus, it is necessary to eliminate redundant or unimportant rules as much as possible before they are used. A related problem is the discovery of interesting or useful rules. In existing classification systems, the set of such rules may not be discovered easily. However, in real world applications, end users often consider the rules with consequences that contain one of particular classes. For example, in cancer screening applications, researchers are very interested in rules that classify genes into the “cancer” class. This paper proposes a novel approach for mining relevant CARs that considers constraints on the rule consequent. A tree structure for storing frequent itemsets from the dataset is designed. Then, some theorems for pruning tree nodes that cannot generate rules satisfying the class constraints are provided and proved. Finally, an efficient algorithm for mining constrained CARs is presented. Experiments show that the proposed method is faster than existing methods.