期刊名称：International Journal of Database Management Systems
印刷版ISSN：0975-5985
电子版ISSN：0975-5705
出版年度：2015
卷号：7
期号：1
页码：29
DOI：10.5121/ijdms.2015.7103
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Sequential pattern mining is studied widely in the data mining community. Finding sequential patterns is abasic data mining method with broad applications. Closed sequential pattern mining is an importanttechnique among the different types of sequential pattern mining, since it preserves the details of the fullpattern set and it is more compact than sequential pattern mining. In this paper, we propose an efficientalgorithm CSpan for mining closed sequential patterns. CSpan uses a new pruning method calledoccurrence checking that allows the early detection of closed sequential patterns during the miningprocess. Our extensive performance study on various real and synthetic datasets shows that the proposedalgorithm CSpan outperforms the CloSpan and a recently proposed algorithm ClaSP by an order ofmagnitude.
