A Novel Method for Mining Sequential Patterns in Datasets

  • Authors:

  • Xiaoyu Chang;Chunguang Zhou;Zhe Wang;Ping Hu

  • Affiliations:

  • Jilin University, China;Jilin University, China;Jilin University, China;Jilin University, China

  • Venue:
  • ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 01
  • Year:
  • 2006

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Abstract

Sequential pattern mining is one of the most important fields in data mining. In this paper, we propose a novel algorithm FSPAN (Fast Sequential Pattern mining algorithm) to do the sequence mining. FSPAN can mine all the frequent sequential patterns in large datasets and it integrates a depth-first traversal approach with an effective pruning mechanism. This pruning mechanism solves the problem of searching frequent sequences in a sequence database by searching frequent items or frequent itemsets, which makes this method very efficient. Moreover, the databases scanned via FSPAN keep shrinking quickly, which makes the algorithm more efficient when the sequential patterns are longer. Experiments on standard test data show that FSPAN is very effective.