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Mining Compressed Sequential Patterns.
- Source :
- Advanced Data Mining & Applications (9783540370253); 2006, p761-768, 8p
- Publication Year :
- 2006
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISBNs :
- 9783540370253
- Database :
- Complementary Index
- Journal :
- Advanced Data Mining & Applications (9783540370253)
- Publication Type :
- Book
- Accession number :
- 32864331
- Full Text :
- https://doi.org/10.1007/11811305_83