Fuzzy C-means based clustering for linearly and nonlinearly separable data
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摘要
In this paper we present a new distance metric that incorporates the distance variation in a cluster to regularize the distance between a data point and the cluster centroid. It is then applied to the conventional fuzzy C-means (FCM) clustering in data space and the kernel fuzzy C-means (KFCM) clustering in a high-dimensional feature space. Experiments on two-dimensional artificial data sets, real data sets from public data libraries and color image segmentation have shown that the proposed FCM and KFCM with the new distance metric generally have better performance on non-spherically distributed data with uneven density for linear and nonlinear separation.
论文关键词:Clustering,Fuzzy C-means,Kernel fuzzy C-means,Distance metric
论文评审过程:Received 21 June 2010, Revised 18 January 2011, Accepted 9 February 2011, Available online 16 February 2011.
论文官网地址:https://doi.org/10.1016/j.patcog.2011.02.009