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Erschienen in:
2006 | OriginalPaper | Buchkapitel
Gradient Based Fuzzy C-Means Algorithm with a Mercer Kernel
verfasst von : Dong-Chul Park, Chung Nguyen Tran, Sancho Park
Erschienen in: Advances in Neural Networks - ISNN 2006
Verlag: Springer Berlin Heidelberg
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In this paper, a clustering algorithm based on Gradient Based Fuzzy C-Means with a Mercer Kernel, called GBFCM (MK), is proposed. The kernel method adopted in this paper implicitly performs nonlinear mapping of the input data into a high-dimensional feature space. The proposed GBFCM(MK) algorithm is capable of dealing with nonlinear separation boundaries among clusters. Experiments on a synthetic data set and several real MPEG data sets show that the proposed algorithm gives better classification accuracies than both the conventional k-means algorithm and the GBFCM.