| Project/Area Number |
26330270
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Soft computing
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| Research Institution | University of Tsukuba |
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Principal Investigator |
MIYAMOTO Sadaaki 筑波大学, システム情報系(名誉教授), 名誉教授 (60143179)
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| Co-Investigator(Kenkyū-buntansha) |
遠藤 靖典 筑波大学, システム情報系, 教授 (10267396)
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| Research Collaborator |
Torra Vicenc University of Skovde, Professor
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| Project Period (FY) |
2014-04-01 – 2017-03-31
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| Project Status |
Completed (Fiscal Year 2016)
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| Budget Amount *help |
¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2016: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2015: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2014: ¥2,600,000 (Direct Cost: ¥2,000,000、Indirect Cost: ¥600,000)
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| Keywords | ソーシャル・ネットワーク / クラスタリング / 階層的クラスタリング / メドイド法 / 2段階階層的クラスタリング / Ward法 / ファジィクラスタリング / ネットワーククラスタリング / 2段階クラスタリング / メドイドクラスタリング / 非対称関係クラスタリング / ネットワークｃ－回帰問題 / 2段階階層クラスタリング / 非対称類似度 / SNSデータ / SNS / modularity / コアポイントクラスタリング / グラフc-回帰モデル / クラスター逐次抽出 |
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| Outline of Final Research Achievements |
Analysis of social networking services (SNS) is recently focused on by many researchers and techniques of network clustering have been developed. In contrast, traditional methods of clustering are considered to be inappropriate for network clustering. In this study new methods based on traditional ideas of clustering are developed and performances are compared with known methods of network clustering. Main results of this study include the development of two-stage clustering methods using the medoids and agglomerative hierarchical clustering which is effective in SNS clustering. Moreover the Ward method of agglomerative hierarchical clustering is shown to be applicable directly to networks without the use of a positive-definite kernel.
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