Abstract
In this paper, we present an approach, called PSP, for mining sequential patterns embedded in a database. Close to the problem of discovering association rules, mining sequential patterns requires handling time constraints. Originally introduced in [3], the issue is addressed by the GSP approach [10]. Our proposal resumes the general principles of GSP but it makes use of a different intermediary data structure which is proved to be more efficient than in GSP.
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R. Agrawal, T. Imielinski, and A. Swami. Mining Association Rules between Sets of Items in Large Databases. In Proc. of the SIGMOD’93, Washington, 1993.
R. Agrawal and R. Srikant. Fast Algorithms for Mining Generalized Association Rules. In Proc. of the VLDB’94, Santiago, Chile, September 1994.
R. Agrawal and R. Srikant. Mining Sequential Patterns. In Proc. of the ICDE’95, Tapei, Taiwan, March 1995.
S. Brin, R. Motwani, J.D. Ullman, and S. Tsur. Dynamic Itemset Counting and Implication Rules for Market Basket Data. In Proc. of the SIGMOD’97.
U.M. Fayad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, editors. Advances in Knowledge Discovery and Data Mining. AAAI Press, 1996.
H. Mannila, H. Toivonen, and A.I. Verkamo. Discovery of Frequent Episodes in Event Sequences. Data Mining and Knowledge Discovery, 1(3), 1997.
F. Masseglia. Le pré-calcul appliqué à l’extraction de motifs séquentiels en data mining. Technical report, LIRMM, France, June 1998.
A. Mueller, Fast Sequential and Parallel Algorithms for Association Rules Mining: A comparison. Technical Report CS-TR-3515, Univ. Maryland-College, 1995.
A Savasere, E. Omiecinski, and S. Navathe. An Efficient Algorithm for Mining Association Rules in Large Databases. In Proc. of the VLDB’95, Zurich, 1995.
R. Srikant and R. Agrawal. Mining Sequential Patterns: Generalizations and Performance Improvements. In Proc. of the EDBT’96, Avignon, France, Sept 1996.
H. Toivonen, Sampling Large Databases for Association Rules. In Proc. of the VLDB’96, September 1996.
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Masseglia, F., Cathala, F., Poncelet, P. (1998). The PSP approach for mining sequential patterns. In: Żytkow, J.M., Quafafou, M. (eds) Principles of Data Mining and Knowledge Discovery. PKDD 1998. Lecture Notes in Computer Science, vol 1510. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0094818
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DOI: https://doi.org/10.1007/BFb0094818
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