Fuzzy c-Means Algorithms for Data with Tolerance Based on Opposite Criterions(Soft Computing,<Special Section>Nonlinear Theory and its Applications)

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概要

• 論文の詳細を見る

• In this paper, two new clustering algorithms are proposed for the data with some errors. In any of these algorithms, the error is interpreted as one of decision variables-called "tolerance"-of a certain optimization problem like the previously proposed algorithm, but the tolerance is determined based on the opposite criterion to its corresponding previously proposed one. Applying our each algorithm together with its corresponding previously proposed one, a reliability of the clustering result is discussed. Through some numerical experiments, the validity of this paper is discussed.

• 社団法人電子情報通信学会の論文
• 2007-10-01

著者

• Endo Yasunori

  University Of Tsukuba

• Kanzawa Yuchi

  Shibaura Institute Of Technology

• MIYAMOTO Sadaaki

  University of Tsukuba

• KANZAWA Yuchi

  Faculty of Engineering, Shibaura Institute of Technology

• ENDO Yasunori

  Faculty of Systems and Information Engineering, University of Tsukuba

• MIYAMOTO Sadaaki

  Faculty of Systems and Information Engineering, University of Tsukuba

関連論文

• Fuzzy c-Means Algorithms for Data with Tolerance Using Kernel Functions
• Fuzzy classification function of fuzzy c-means algorithms for data with tolerance (特集 クラスタリング)
• Fuzzy c-Means Algorithms for Data with Tolerance Based on Opposite Criterions(Soft Computing,Nonlinear Theory and its Applications)

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