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Mining Sequential Patterns in Large Datasets

  • Xiao-Yu Chang
  • Chun-Guang Zhou
  • Zhe Wang
  • Yan-Wen Li
  • Ping Hu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4223)

Abstract

A novel algorithm FFSPAN (Fast Frequent Sequential Pattern mining algorithm) is proposed in this paper. FFSPAN mines all the frequent sequential patterns in large datasets, and solves the problem of searching frequent sequences in a sequence database by searching frequent items or frequent itemsets. Moreover, the databases that FFSPAN scans keep shrinking quickly, which makes the algorithm more efficient when the sequential patterns are longer. Experiments on standard test data show that FFSPAN is very effective.

Keywords

Sequential Pattern Frequent Itemsets Mining Association Rule Frequent Item Vary Support 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xiao-Yu Chang
    • 1
  • Chun-Guang Zhou
    • 1
  • Zhe Wang
    • 1
  • Yan-Wen Li
    • 1
    • 2
  • Ping Hu
    • 1
  1. 1.College of Computer Science and technologyJilin University, Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of EducationChangchunP.R. China
  2. 2.Department of Computer ScienceNortheast Normal UniversityChangchunP.R. China

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