Development of methods for mining characteristic patterns from network structure changing with time
| Project/Area Number |
20700136
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Single-year Grants |
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| Research Field |
Intelligent informatics
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| Research Institution | Osaka University |
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Principal Investigator |
INOKUCHI Akihiro Osaka University, 産業科学研究所, 助教 (70452456)
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| Project Period (FY) |
2008 – 2009
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| Project Status |
Completed (Fiscal Year 2009)
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| Budget Amount *help |
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2009: ¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
Fiscal Year 2008: ¥2,210,000 (Direct Cost: ¥1,700,000、Indirect Cost: ¥510,000)
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| Keywords | 人工知能 / 機械学習 / アルゴリズム / 知識発見とデータマイニング / データマイニング / グラフ構造 / グラフ時系列データ |
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| Research Abstract |
There are many real-world applications suitable to model objects by using graph sequences. For example, a human network is represented by a graph where each human and each relationship between two humans correspond to vertices and an edge, respectively. If a person joins or leaves a human community, the numbers of vertices and edges in the graph change with time. In this study, we developed efficient methods for mining characteristic patterns from graph sequences, and evaluated their efficiencies applying them to artificial and real-world datasets.
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Report
(3 results)
Research Products
(21 results)
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[Book] New Frontiers in Applied Data Mining, PAKDD 2008 International Workshops, Osaka, Japan, May 20-23, 2008.Revised Selected Papers2009
Author(s)
Sanjay Chawla, Takashi Washio, Shin-ichi Minato, Shusaku Tsumoto, Takashi Onoda, Seiji Yamada, Akihiro Inokuchi
Total Pages
213
Publisher
Springer
Related Report
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