Mining Closed Sequential Patterns in Large Sequence Databases

Abstract

Sequential pattern mining is studied widely in the data mining community. Finding sequential patterns is a basic data mining method with broad applications. Closed sequential pattern mining is an important technique among the different types of sequential pattern mining, since it preserves the details of the full pattern set and it is more compact than sequential pattern mining. In this paper, we propose an efficient algorithm CSpan for mining closed sequential patterns. CSpan uses a new pruning method called occurrence checking that allows the early detection of closed sequential patterns during the mining process. Our extensive performance study on various real and synthetic datasets shows that the proposed algorithm CSpan outperforms the CloSpan and a recently proposed algorithm ClaSP by an order of magnitude.