Algorithms of Hard c-Means Clustering Using Kernel Functions in Support Vector Machines

Sadaaki Miyamoto; Youichi Nakayama

doi:10.20965/jaciii.2003.p0019

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JACIII Vol.7 No.1 pp. 19-24

doi: 10.20965/jaciii.2003.p0019

(2003)

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Algorithms of Hard c-Means Clustering Using Kernel Functions in Support Vector Machines

Sadaaki Miyamoto^* and Youichi Nakayama^**

^*Institute of Engineering Mechanics and Systems, University of Tsukuba, Ibaraki 305-8573, Japan

^**Graduate School of Systems and Information Engineering, University of Tsukuba, Ibaraki 305-8573, Japan

Received:

August 28, 2002

Accepted:

November 18, 2002

Published:

February 20, 2003

Keywords:

clustering, hard c-means, support vector machine

Abstract

We discuss hard c-means clustering using a mapping into a high-dimensional space considered within the theory of support vector machines. Two types of iterative algorithms are developed. Effectiveness of the proposed method is shown by numerical examples.

Cite this article as:

S. Miyamoto and Y. Nakayama, “Algorithms of Hard c-Means Clustering Using Kernel Functions in Support Vector Machines,” J. Adv. Comput. Intell. Intell. Inform., Vol.7, No.1, pp. 19-24, 2003.

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Algorithms of Hard c-Means Clustering Using Kernel Functions in Support Vector Machines

Sadaaki Miyamoto* and Youichi Nakayama**

Sadaaki Miyamoto^* and Youichi Nakayama^**