VMSP: Efficient Vertical Mining of Maximal Sequential Patterns

  • Philippe Fournier-Viger
  • Cheng-Wei Wu
  • Antonio Gomariz
  • Vincent S. Tseng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8436)

Abstract

Sequential pattern mining is a popular data mining task with wide applications. However, it may present too many sequential patterns to users, which makes it difficult for users to comprehend the results. As a solution, it was proposed to mine maximal sequential patterns, a compact representation of the set of sequential patterns, which is often several orders of magnitude smaller than the set of all sequential patterns. However, the task of mining maximal patterns remains computationally expensive. To address this problem, we introduce a vertical mining algorithm named VMSP (Vertical mining of Maximal Sequential Patterns). It is to our knowledge the first vertical mining algorithm for mining maximal sequential patterns. An experimental study on five real datasets shows that VMSP is up to two orders of magnitude faster than the current state-of-the-art algorithm.

Keywords

vertical mining maximal sequential pattern mining candidate pruning 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Philippe Fournier-Viger
    • 1
  • Cheng-Wei Wu
    • 2
  • Antonio Gomariz
    • 3
  • Vincent S. Tseng
    • 2
  1. 1.Dept. of Computer ScienceUniversity of MonctonCanada
  2. 2.Dept. of Computer Science and Information EngineeringNational Cheng Kung UniversityTaiwan
  3. 3.Dept. of Information and Communication EngineeringUniversity of MurciaSpain

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