Mining Weighted Closed Sequential Patterns in Large Databases

Abstract

Previous algorithms mine the complete set of sequential patterns in large database efficiently, but when mining long sequential patterns in dense databases or using low minimum supports, it may produce many redundant patterns and some uninterested patterns. In this paper, a novel weighted closed sequential pattern mining algorithm (WCSpan) is presented, which implements the closed sequential pattern mining with weight constraints, so the uninterested patterns could be pruned and the redundancy could be reduced. This algorithm can find fewer but interested weighted sequential patterns by weighted pruning method and hash structure. The experimental results show that WCSpan algorithm is more efficient than CloSpan and WSpan.