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. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Park, D. C., Tran, C. N., & Park, S. (2006). Gradient based fuzzy C-means algorithm with a mercer kernel. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3971 LNCS, pp. 1038–1043). Springer Verlag. https://doi.org/10.1007/11759966_152
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