Mining of Confidence-Closed Correlated Patterns Efficiently

Abstract

Correlated pattern mining has become increasingly important recently as an alternative or an augmentation of association rule mining. Though correlated pattern mining discloses the correlation relationships among data objects and reduces significantly the number of patterns produced by the association mining, it still generates quite a large number of patterns. This paper proposes closed correlated pattern mining to reduce the number of the correlated patterns produced without information loss. A new notion of the confidenceclosed correlated patterns is proposed first, and then an efficient algorithm is present, called CCMine, for mining those patterns. Confidence closed pattern mining reduces the number of patterns by at least an order of magnitude. It also shows that CCMine outperforms a simple method making use of the traditional closed pattern miner. Confidence-closed pattern mining is a valuable approach to condensing correlated patterns.