IMCS: incremental mining of closed sequential patterns

Authors:
Lei Chang;Dongqing Yang;Tengjiao Wang;Shiwei Tang
Affiliations:
Department of Computer Science & Technology, Peking University, Beijing, China;Department of Computer Science & Technology, Peking University, Beijing, China;Department of Computer Science & Technology, Peking University, Beijing, China;Department of Computer Science & Technology, Peking University, Beijing, China
Venue:
APWeb/WAIM'07 Proceedings of the joint 9th Asia-Pacific web and 8th international conference on web-age information management conference on Advances in data and web management
Year:
2007

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Abstract

Recently, mining compact frequent patterns (for example closed patterns and compressed patterns) has received much attention from data mining researchers. These studies try to address the interpretability and efficiency problems encountered by traditional frequent pattern mining methods. However, to the best of our knowledge, how to efficiently mine compact sequential patterns in a dynamic sequence database environment has not been explored yet. In this paper, we examine the problem how to mine closed sequential patterns incrementally. A compact structure CSTree is designed to keep the closed sequential patterns, and an efficient algorithm IMCS is developed to maintain the CSTree when the sequence database is updated incrementally. A thorough experimental study shows that IMCS outperforms the state-of-the-art algorithms - PrefixSpan, CloSpan, BIDE and a recently proposed incremental mining algorithm IncSpan by about a factor of 4 to more than an order of magnitude.