Even-Sized Clustering Based on Optimization and its Variants
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- Endo Yasunori
- Faculty of Engineering, Information and Systems, University of Tsukuba
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- Hamasuna Yukihiro
- Department of Informatics, Kindai University
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- Hirano Tsubasa
- Department of Risk Engineering, University of Tsukuba
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- Kinoshita Naohiko
- Department of Risk Engineering, University of Tsukuba
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Abstract
<p>A clustering method referred to as K-member clustering classifies a dataset into certain clusters, the size of which is more than a given constant K. Even-sized clustering, which classifies a dataset into even-sized clusters, is also considered along with K-member clustering. In our previous study, we proposed Even-sized Clustering Based on Optimization (ECBO) to output adequate results by formulating an even-sized clustering problem as linear programming. The simplex method is used to calculate the belongingness of each object to clusters in ECBO. In this study, ECBO is extended by introducing ideas that were introduced in K-means or fuzzy c-means to resolve problems of initial-value dependence, robustness against outliers, calculation costs, and nonlinear boundaries of clusters. We also reconsider the relation between the dataset size, the cluster number, and K in ECBO. Moreover, we verify the effectiveness of the variants of ECBO based on experimental results using synthetic datasets and a benchmark dataset.</p>
Journal
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- Journal of Advanced Computational Intelligence and Intelligent Informatics
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Journal of Advanced Computational Intelligence and Intelligent Informatics 22 (1), 62-69, 2018-01-20
Fuji Technology Press Ltd.
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Details 詳細情報について
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- CRID
- 1390564238027597440
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- NII Article ID
- 130007494685
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- NII Book ID
- AA12042502
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- ISSN
- 18838014
- 13430130
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- NDL BIB ID
- 028764959
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- Text Lang
- en
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- Data Source
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- JaLC
- NDL
- Crossref
- CiNii Articles
- KAKEN
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- Abstract License Flag
- Disallowed