VMSP: Efficient vertical mining of maximal sequential patterns

Philippe Fournier-Viger, Cheng Wei Wu, Antonio Gomariz, S. Tseng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

49 Scopus citations


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.

Original languageEnglish
Title of host publicationAdvances in Artificial Intelligence - 27th Canadian Conference on Artificial Intelligence, Canadian AI 2014, Proceedings
PublisherSpringer Verlag
Number of pages12
ISBN (Print)9783319064826
StatePublished - 1 Jan 2014
Event27th Canadian Conference on Artificial Intelligence, AI 2014 - Montreal, QC, Canada
Duration: 6 May 20149 May 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8436 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference27th Canadian Conference on Artificial Intelligence, AI 2014
CityMontreal, QC


  • candidate pruning
  • maximal sequential pattern mining
  • vertical mining

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