Mining Class-Association Rules with Constraints

Dang Nguyen, Bay Vo

Abstract: Numerous fast algorithms for mining class-association rules (CARs) have been developed recently. However, in the real world, end-users are often interested in a subset of class-association rules. Particularly, they may consider only rules that contain a specific item or a specific set of items. The nave strategy is to apply such item constraints into the post-processing step. However, such approaches require much effort and time. This paper proposes an effective method for integrating constraints that express the presence of user-defined items (for example (Bread AND Milk)) into the class-association rule mining process. First, we design a tree structure in that each node contains the constrained itemset. Second, we develop a theorem and a proposition for quickly pruning infrequent nodes and weak rules. Final, an efficient algorithm for mining CARs with item constraints is proposed. Experiments show that the proposed algorithm outperforms the post-processing approach.