Gradient based fuzzy c-means algorithm with a mercer kernel

Authors:
Dong-Chul Park;Chung Nguyen Tran;Sancho Park
Affiliations:
Dept. of Information Engineering, Myong Ji University, Korea;Dept. of Information Engineering, Myong Ji University, Korea;Davan Tech Co., Seongnam, Korea
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Year:
2006

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Abstract

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.