FUZZY CLUSTERING AND MULTIVARIATE ANALYSIS
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- Sato Mika
- Faculty of Systems and Information Engineering, University of Tsukuba
Bibliographic Information
- Other Title
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- ファジィクラスタリングと多変量解析
- ファジィクラスタリング ト タヘンリョウ カイセキ
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Abstract
Conventionally, fuzzy multivariate data analysis has been proposed along with the issue of positively introducing uncertainty in real data and the methodology itself. Fuzzy Clustering is one method which can capture the uncertainty situation of real data and it is well known that fuzzy clustering can obtain a robust result as compared with conventional hard clustering. Following along with the emphasis on the general problem of data analysis, which is a solution for analyzing a huge amount of complex data, the merit of fuzzy clustering for this is expected. In this paper, we describe fuzzy clustering methods, which are methods in fuzzy multivariate analysis, along with several hybrid methods of fuzzy clustering and conventional multivariate analysis which have recently been proposed by us based on the idea that the multiple merits of methods can cope with the inherent classification structures.
Journal
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- Bulletin of the Computational Statistics of Japan
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Bulletin of the Computational Statistics of Japan 17 (2), 147-156, 2005
Japanese Society of Computational Statistics
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Details 詳細情報について
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- CRID
- 1390001204381022976
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- NII Article ID
- 110002325568
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- NII Book ID
- AN10195854
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- ISSN
- 21899789
- 09148930
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- NDL BIB ID
- 7718596
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- Text Lang
- ja
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- Data Source
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- JaLC
- NDL
- CiNii Articles
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- Abstract License Flag
- Disallowed