Exploiting succinct constraints using FP-trees

Abstract

Since its introduction, frequent-set mining has been generalized to many forms, which include constrained data mining. The use of constraints permits user focus and guidance, enables user exploration and control, and leads to effective pruning of the search space and efficient mining of frequent itemsets. In this paper, we focus on the use of succinct constraints. In particular, we propose a novel algorithm called FPS to mine frequent itemsets satisfying succinct constraints. The FPS algorithm avoids the generate-and-test paradigm by exploiting succinctness properties of the constraints in a FP-tree based framework. In terms of functionality, our algorithm is capable of handling not just the succinct aggregate constraint, but any succinct constraint in general. Moreover, it handles multiple succinct constraints. In terms of performance, our algorithm is more efficient and effective than existing FP-tree based constrained frequent-set mining algorithms.