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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, 2002Accepted:November 18, 2002Published: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.Data files:
