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.
This work is supported by the National Natural Science Foundation of China under Grant No. 60473051.
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© 2006 Springer-Verlag Berlin Heidelberg
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Chang, L., Yang, D., Tang, S., Wang, T. (2006). Mining Compressed Sequential Patterns. In: Li, X., Zaïane, O.R., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2006. Lecture Notes in Computer Science(), vol 4093. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11811305_83
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DOI: https://doi.org/10.1007/11811305_83
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37025-3
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