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Closed Frequent Subgraph Mining by Graph Closure Operation and Its Parallelization

Research Project

Project/Area Number 21700167
Research Category

Grant-in-Aid for Young Scientists (B)

Allocation TypeSingle-year Grants
Research Field Intelligent informatics
Research InstitutionAoyama Gakuin University

Principal Investigator

OHARA Kouzou  Aoyama Gakuin University, 理工学部, 准教授 (30294127)

Project Period (FY) 2009 – 2010
Project Status Completed (Fiscal Year 2010)
Budget Amount *help
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2010: ¥2,340,000 (Direct Cost: ¥1,800,000、Indirect Cost: ¥540,000)
Fiscal Year 2009: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
Keywordsグラフマイニング / データマイニング / 機械学習 / パターン発見 / クラフマイニング
Research Abstract

In this work, we proposed the graph closure operator, as well as an efficient method for graph isomorphism checking that takes advantage of occurrences of a graph pattern. Given a subgraph, the operator generates a closed subgraph including it, where the closed subgraph is the maximum subgraph among those which have the same frequency as the given one. Then, we developed an efficient method for enumerating all possible closed frequent subgraphs in a graph database by means of them, and parallelized its inner processes to improve its scalability.

Report

(3 results)
  • 2010 Annual Research Report   Final Research Report ( PDF )
  • 2009 Annual Research Report
  • Research Products

    (6 results)

All 2010 2009

All Presentation (6 results)

  • [Presentation] Selecting Information Diffusion Models over Social Networks for Behavioral Analysis2010

    • Author(s)
      Kazumi Saito, Masahiro Kimura, Kouzou Ohara, Hiroshi Motoda
    • Organizer
      The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2010 (ECML/PKDD2010)
    • Place of Presentation
      スペイン・バルセロナ自治大学
    • Year and Date
      2010-09-22
    • Related Report
      2010 Final Research Report
  • [Presentation] Selecting Information Diffusion Models over Social Networks for Behavioral Analysis.2010

    • Author(s)
      Kazumi Saito
    • Organizer
      The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2010
    • Place of Presentation
      バルセロナ自治大学(バルセロナ・スペイン)
    • Year and Date
      2010-09-22
    • Related Report
      2010 Annual Research Report
  • [Presentation] Behavioral Analyses of Information Diffusion Models by Observed Data of Social Network2010

    • Author(s)
      Kazumi Saito, Masahiro Kimura, Kouzou Ohara, Hiroshi Motoda
    • Organizer
      The International Conference on Social Computing, Behavioral Modeling, and Prediction (SBP10)
    • Place of Presentation
      アメリカ・アメリカ国立衛生研究所
    • Year and Date
      2010-03-31
    • Related Report
      2010 Final Research Report
  • [Presentation] Behavioral Analyses of Information Diffusion Models by Observed Data of Social Network2010

    • Author(s)
      Hiroshi Motoda
    • Organizer
      2010 International Conference on Social Computing, Behavioral Modeling, and Prediction
    • Place of Presentation
      アメリカ国立衛生研究所(メリーランド州・アメリカ)
    • Year and Date
      2010-03-31
    • Related Report
      2009 Annual Research Report
  • [Presentation] Learning Continuous-Time Information Diffusion Model for Social Behavioral Data Analysis2009

    • Author(s)
      Kazumi Saito, Masahiro Kimura, Kouzou Ohara, Hiroshi Motoda
    • Organizer
      The 2nd Asian Conference on Machine Learning (ACML2010)
    • Place of Presentation
      中国・南京大学
    • Year and Date
      2009-11-03
    • Related Report
      2010 Final Research Report
  • [Presentation] Learning Continuous-Time Information Diffusion Model for Social Behavioral Data Analysis2009

    • Author(s)
      Kazumi Saito
    • Organizer
      The 1st Asian Conference on Machine Learning
    • Place of Presentation
      南京大学(中国)
    • Year and Date
      2009-11-03
    • Related Report
      2009 Annual Research Report

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Published: 2009-04-01   Modified: 2016-04-21  

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