西南交通大学学报(英文版)2007，Vol.15Issue(2)：96-101,6.
Kernel Generalized Noise Clustering Algorithm
Kernel Generalized Noise Clustering Algorithm
摘要
Abstract
To deal with the nonlinear separable problem, the generalized noise clustering (GNC) algorithm is extended to a kernel generalized noise clustering (KGNC) model. Different from the fuzzy c-means (FCM) model and the GNC model which are based on Euclidean distance, the presented model is based on kernel-induced distance by using kernel method. By kernel method the input data are nonlinearly and implicitly mapped into a high-dimensional feature space, where the nonlinear pattern appears linear and the GNC algorithm is performed. It is unnecessary to calculate in high-dimensional feature space because the kernel function can do itjust in input space. The effectiveness of the proposed algorithm is verified by experiments on three data sets. It is concluded that the KGNC algorithm has better clustering accuracy than FCM and GNC in clustering data sets containing noisy data.关键词
Fuzzy clustering/Pattern recognition/Kernel methods/Noise clustering/Kernel generalized noise clusteringKey words
Fuzzy clustering/Pattern recognition/Kernel methods/Noise clustering/Kernel generalized noise clustering分类
交通工程引用本文复制引用
WU Xiao-hong,ZHOU Jian-jiang..Kernel Generalized Noise Clustering Algorithm[J].西南交通大学学报(英文版),2007,15(2):96-101,6.基金项目
The 15th Plan National Defence Preventive Research Project ( No. 413030201 ) （ No. 413030201 ）
