Mining compressed sequential patterns

Authors:
Lei Chang;Dongqing Yang;Shiwei Tang;Tengjiao Wang
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:
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Year:
2006

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Abstract

Current sequential pattern mining algorithms often produce a large number of patterns. It is difficult for a user to explore in so many patterns and get a global view of the patterns and the underlying data. In this paper, we examine the problem of how to compress a set of sequential patterns using only K SP-Features(Sequential Pattern Features). A novel similarity measure is proposed for clustering SP-Features and an effective SP-Feature combination method is designed. We also present an efficient algorithm, called CSP( Compressing Sequential Patterns) to mine compressed sequential patterns based on the hierarchical clustering framework. A thorough experimental study with both real and synthetic datasets shows that CSP can compress sequential patterns effectively.