single-jc.php

JACIII Vol.7 No.1 pp. 25-30
doi: 10.20965/jaciii.2003.p0025
(2003)

Paper:

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:
Sadaaki Miyamoto and Daisuke 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.
Data files:

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Jun. 19, 2021