2013 Fiscal Year Final Research Report
Study of semi-supervised clustering and challenge to constrained mixture distributions
| Project/Area Number |
23500269
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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 |
Sensitivity informatics/Soft computing
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| Research Institution | University of Tsukuba |
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Principal Investigator |
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| Co-Investigator(Renkei-kenkyūsha) |
ENDO Yasunori 筑波大学, システム情報系, 教授 (10267396)
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| Project Period (FY) |
2011 – 2013
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| Keywords | 半教師付き分類 / 制約クラスタリング / 階層的技法 / 混合分布モデル / COP K-means / ファジィクラスタリング / インダクティブクラスタリング |
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| Research Abstract |
Existing methods of semi-supervised clustering and proposed methods by the authors are compared using artificial and repository data. Main results are as follows. 1. Agglomerative clustering performs as well as mixture distribution models in constrained clustering. 2.Methods of fuzzy clustering generalize basic mixture distribution models for semi-supervised classification. 3. Extensions of COP K-means have been proposed but they did not perform as well as constrained mixture distribution method. 4. The concept of inductive clustering has been proposed and its methodological usefulness has been shown. 5. Real data using Twitter have been analyzed using semi-supervised clustering and its effects have been investigated.
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[Journal Article]2013
Author(s)
S. Miyamoto, S. Takumi
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Journal Title
Journal of Advanced Computational Intelligence and Intelligent Informatics
Volume: 17
Pages: 504-510
Peer Reviewed
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