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A kernel-based subtractive clustering method
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DOI：10.1016/j.patrec.2004.10.001.png)
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Abstract
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In this paper the conventional subtractive clustering method is extended by calculating the mountain value of each data point based on a kernel-induced distance instead of the conventional sum-of-squares distance. The kernel function is a generalization of the distance metric that measures the distance between two data points as the data points are mapped into a high dimensional space. Use of the kernel function makes it possible to cluster data that is linearly non-separable in the original space into homogeneous groups in the transformed high dimensional space. Application of the conventional subtractive method and the kernel-based subtractive method to well-known data sets showed the superiority of the proposed approach. (c) 2004 Elsevier B.V. All rights reserved.
Keywords:
clustering
mountain method
subtractive method
kernel function
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