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The PSP approach for mining sequential patterns

  • F. Masseglia
  • F. Cathala
  • P. Poncelet
Communications Session 7. Sequential and Spatial Data Mining
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1510)

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.

References

  1. 1.
    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.Google Scholar
  2. 2.
    R. Agrawal and R. Srikant. Fast Algorithms for Mining Generalized Association Rules. In Proc. of the VLDB’94, Santiago, Chile, September 1994.Google Scholar
  3. 3.
    R. Agrawal and R. Srikant. Mining Sequential Patterns. In Proc. of the ICDE’95, Tapei, Taiwan, March 1995.Google Scholar
  4. 4.
    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.Google Scholar
  5. 5.
    U.M. Fayad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, editors. Advances in Knowledge Discovery and Data Mining. AAAI Press, 1996.Google Scholar
  6. 6.
    H. Mannila, H. Toivonen, and A.I. Verkamo. Discovery of Frequent Episodes in Event Sequences. Data Mining and Knowledge Discovery, 1(3), 1997.Google Scholar
  7. 7.
    F. Masseglia. Le pré-calcul appliqué à l’extraction de motifs séquentiels en data mining. Technical report, LIRMM, France, June 1998.Google Scholar
  8. 8.
    A. Mueller, Fast Sequential and Parallel Algorithms for Association Rules Mining: A comparison. Technical Report CS-TR-3515, Univ. Maryland-College, 1995.Google Scholar
  9. 9.
    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.Google Scholar
  10. 10.
    R. Srikant and R. Agrawal. Mining Sequential Patterns: Generalizations and Performance Improvements. In Proc. of the EDBT’96, Avignon, France, Sept 1996.Google Scholar
  11. 11.
    H. Toivonen, Sampling Large Databases for Association Rules. In Proc. of the VLDB’96, September 1996.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • F. Masseglia
    • 2
  • F. Cathala
    • 1
    • 4
  • P. Poncelet
    • 1
    • 3
  1. 1.LIM ESA CNRS 6077Marseille Cedex 9France
  2. 2.LIRMM UMR CNRS 5506Montpellier Cedex 5France
  3. 3.IUT d’Aix-en-ProvenceFrance
  4. 4.Cemagref, division Aix-en-ProvenceFrance

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