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Traditional fuzzy clustering algorithms have poor effects on nonlinearly separable data sets. In this paper, a kernelbased possibilistic fuzzy c-means clustering algorithm is proposed. Through Mercer kernel functions, the samples in the original space are firstly mapped into a high-dimensional feature space. Then the samples in the feature space are clustered using the possibilistic fuzzy c-means...
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