Performance of kernel-based fuzzy clustering

被引：14
|
作者：
Graves, D. [1 ]
Pedrycz, W. [1 ]
机构：
[1] Univ Alberta, Dept Elect & Comp Engn, 9107-116 St, Edmonton, AB T6G 2V4, Canada
来源：
ELECTRONICS LETTERS | 2007年 / 43卷 / 25期
关键词：
D O I：
10.1049/el:20073093
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
An evaluation and comparative study of kernel-based fuzzy clustering algorithms is presented. The main objective is to evaluate the performance gains provided by kernelised FCM (fuzzy C-means). It is shown that kernelised FCM provides marginal improvements in the classification rate for several popular Machine Learning data sets. It is observed that the performance of kernelised FCM depends greatly on the selection of the kernel parameters.
引用
收藏
页码：1445 / 1446
页数：2
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