Fuzzy c-Means Clustering Using Kernel Functions in Support Vector Machines

Sadaaki Miyamoto; Daisuke Suizu

doi:10.20965/jaciii.2003.p0025

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JACIII Vol.7 No.1 pp. 25-30

doi: 10.20965/jaciii.2003.p0025

(2003)

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Fuzzy c-Means Clustering Using Kernel Functions in Support Vector Machines

Sadaaki Miyamoto^* and Daisuke Suizu^**

^*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, fuzzy c-means, radial basis kernel functions, support vector machine

Abstract

We studied clustering algorithms of fuzzy c-means using a kernel to represent an inner product for mapping into high-dimensional space. Such kernels have been studied in support vector machines used by many researchers in pattern classification. Algorithms of fuzzy c-means are transformed into kernel-based methods by changing objective functions, whereby new iterative minimization algorithms are derived. Numerical examples show that clusters that cannot be obtained without a kernel are generated.

Cite this article as:

S. Miyamoto and D. Suizu, “Fuzzy c-Means Clustering Using Kernel Functions in Support Vector Machines,” J. Adv. Comput. Intell. Intell. Inform., Vol.7, No.1, pp. 25-30, 2003.

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Fuzzy c-Means Clustering Using Kernel Functions in Support Vector Machines

Sadaaki Miyamoto* and Daisuke Suizu**

Sadaaki Miyamoto^* and Daisuke Suizu^**