Mining sequential patterns in large datasets

  • Authors:

  • Xiao-Yu Chang;Chun-Guang Zhou;Zhe Wang;Yan-Wen Li;Ping Hu

  • Affiliations:

  • College of Computer Science and technology, Jilin University, Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education, Changchun, P.R. China;College of Computer Science and technology, Jilin University, Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education, Changchun, P.R. China;College of Computer Science and technology, Jilin University, Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education, Changchun, P.R. China;College of Computer Science and technology, Jilin University, Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education, Changchun, P.R. China;College of Computer Science and technology, Jilin University, Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education, Changchun, P.R. China

  • Venue:
  • FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
  • Year:
  • 2006

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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.