Two semi-supervised entropy regularized fuzzy c-means

Yuchi Kanzawa; Yasunori Endo; Sadaaki Miyamoto

Two semi-supervised entropy regularized fuzzy c-means

Yuchi Kanzawa, Yasunori Endo, Sadaaki Miyamoto

Research output: Contribution to conference › Paper › peer-review

Abstract

In this paper, two semi-supervised clustering methods are proposed, which are based on entropy regularized fuzzy c-means algorithm. First, two fuzzy c-means algorithms are introduced. The one is the standard one and the other is the entropy regularized one. Second, two semi-supervised standard fuzzy c-means algorithms are introduced, which are derived from adding loss function of memberships to the original optimization problem. Third, two new optimization problems are proposed, in which one is derived from adding new loss function of memberships to the original optimization problem and the other is derived from adding the loss function used in the latter semisupervised standard fuzzy c-means algorithm. Last, two iterative algorithms are proposed by solving the optimization problems.

Original language	English
Pages	61-65
Number of pages	5
Publication status	Published - 2010 Dec 1
Event	Joint 5th International Conference on Soft Computing and Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2010 - Okayama, Japan Duration: 2010 Dec 8 → 2010 Dec 12

Conference

Conference	Joint 5th International Conference on Soft Computing and Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2010
Country	Japan
City	Okayama
Period	10/12/8 → 10/12/12

ASJC Scopus subject areas

Artificial Intelligence
Information Systems

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Two semi-supervised entropy regularized fuzzy c-means. / Kanzawa, Yuchi; Endo, Yasunori; Miyamoto, Sadaaki.

2010. 61-65 Paper presented at Joint 5th International Conference on Soft Computing and Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2010, Okayama, Japan.

Research output: Contribution to conference › Paper › peer-review

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