Geodesic-dissimilarity-based hard and fuzzy c-means

Yuchi Kanzawa; Yasunori Endo; Sadaaki Miyamoto

Geodesic-dissimilarity-based hard and fuzzy c-means

Yuchi Kanzawa, Yasunori Endo, Sadaaki Miyamoto

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

Abstract

In this paper, the geodesic distance is applied to relational clustering methods. First, it is shown that conventional methods are based on respective three types of relational clustering algorithms among nine ones, and the six rests of the nine ones with the geodesic distance are proposed. Second, geodesic dissimilarity is proposed by assigning the power of the Euclidean distance to the weight of the neighborhood graph of data. Numerical examples show that the proposed geodesicdissimilarity- based relational clustering algorithms successfully cluster the data that conventional squared-Euclidean-distancebased ones cannot.

Original language	English
Pages	401-405
Number of pages	5
Publication status	Published - 2010
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/Territory	Japan
City	Okayama
Period	10/12/8 → 10/12/12

ASJC Scopus subject areas

Artificial Intelligence
Information Systems

Cite this

Geodesic-dissimilarity-based hard and fuzzy c-means. / Kanzawa, Yuchi; Endo, Yasunori; Miyamoto, Sadaaki.
2010. 401-405 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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AB - In this paper, the geodesic distance is applied to relational clustering methods. First, it is shown that conventional methods are based on respective three types of relational clustering algorithms among nine ones, and the six rests of the nine ones with the geodesic distance are proposed. Second, geodesic dissimilarity is proposed by assigning the power of the Euclidean distance to the weight of the neighborhood graph of data. Numerical examples show that the proposed geodesicdissimilarity- based relational clustering algorithms successfully cluster the data that conventional squared-Euclidean-distancebased ones cannot.

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Geodesic-dissimilarity-based hard and fuzzy c-means

Abstract

Conference

ASJC Scopus subject areas

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