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Abstract
Mining sequential patterns is to discover sequential purchasing behaviors of most customers from a large amount of customer transactions. The previous approaches for mining sequential patterns need to repeatedly scan the large database, and take a large amount of computation time to find frequent sequences, which are very time consuming. In this paper, we present an algorithm SSLP to find sequential patterns, which can significantly reduce the number of the database scans. The experimental results show that our algorithms are more efficient than the other algorithms.
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Authors and Affiliations
Department of Computer Science and Information Engineering, Fu Jen Catholic University, 242, Taipei, Taiwan ROC
Yen Show-Jane & Cho Chung-Wen
- Yen Show-Jane
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- Cho Chung-Wen
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Editors and Affiliations
Department of Computer and Information Science, Norwegian University of Science and Technology, O.S. Bragstads plass 2E, 7491, Trondheim, Norway
Jan Komorowski
Department of Computer Science, University of North Carolina, Charlotte, NC 28223, USA
Jan Żytkow
Laboratoire ERIC, Université Lyon 2, 5 avenue Pierre Mendès-France, 69676, Bron, France
Djamel A. Zighed
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Show-Jane, Y., Chung-Wen, C. (2000). An Efficient Approach to Discovering Sequential Patterns in Large Databases. In: Zighed, D.A., Komorowski, J., Żytkow, J. (eds) Principles of Data Mining and Knowledge Discovery. PKDD 2000. Lecture Notes in Computer Science(), vol 1910. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45372-5_85
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