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

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概要

• 論文の詳細を見る

• 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.

• 一般社団法人情報処理学会の論文
• 2006-12-15

著者

• LI Jie

  University of Electro-Communications

• Takata Toyoo

  Faculty Of Software And Information Science Iwate Prefectural University

• Takata Toyoo

  Iwate Prefectural University

• WANG JIAHONG

  Iwate Prefectural University

• ASANUMA YOSHIAKI

  Iwate Prefectural University

• KODAMA EIICHIRO

  Iwate Prefectural University

• Asanuma Yoshiaki

  Iwate Prefectural University:(presently)internet Initiative Japan Inc.

• Li Jie

  University Of Tsukuba

• Wang Jiahong

  Faculty of Software and Information Science, Iwate Prefectural University

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• Mining Sequential Patterns More Efficiently by Reducing the Cost of Scanning Sequence Databases(Data Mining)
• Mining Frequent Patterns Securely in Distributed System(Data Mining)
• Mining Sequential Patterns More Efficiently by Reducing the Cost of Scanning Sequence Databases
• Mining Sequential Patterns More Efficiently by Reducing the Cost of Scanning Sequence Databases
• A High Collusion-Resistant Approach to Distributed Privacy-preserving Data Mining
• A High Collusion-Resistant Approach to Distributed Privacy-preserving Data Mining

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