Mining Sequential Patterns More Efficiently by Reducing the Cost of Scanning Sequence Databases

• Wang Jiahong

  Iwate Prefectural University

• Asanuma Yoshiaki

  Iwate Prefectural University Presently with Internet Initiative Japan Inc.

• Kodama Eiichiro

  Iwate Prefectural University

• Takata Toyoo

  Iwate Prefectural University

• Li Jie

  University of Tsukuba

Abstract

Sequential pattern mining is a useful technique used to discover frequent subsequences as patterns in a sequence database. Depending on the application, sequence databases vary by number of sequences, number of individual items, average length of sequences, and average length of potential patterns. In addition, to discover the necessary patterns in a sequence database, the support threshold may be set to different values. Thus, for a sequential pattern-mining algorithm, responsiveness should be achieved for all of these factors. For that purpose, we propose a candidate-driven pattern-growth sequential pattern-mining algorithm called FSPM (Fast Sequential Pattern Mining). A useful property of FSPM is that the sequential patterns concerning a user-specified item can be mined directly. Extensive experimental results show that, in most cases FSPM outperforms existing algorithms. An analytical performance study shows that it is the inherent potentiality of FSPM that makes it more effective.

Journal

• Information and Media Technologies

  Information and Media Technologies 2 (1), 163-177, 2007

  Information and Media Technologies Editorial Board

Details

• CRID: 1390001205264127360
• NII Article ID: 130000058323
• DOI: 10.11185/imt.2.163
• ISSN: 18810896
• Text Lang: en
• Data Source:
  • JaLC
  • CiNii Articles
• Abstract License Flag: Disallowed