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Kernel Generalized Noise Clustering Algorithm
作者姓名:武小红  周建江
作者单位:College of Information Science and Technology Nanjing University of Aeronautics and Astronautics,College of Information Science and Technology Nanjing University of Aeronautics and Astronautics,Nanjing 210016 China School of Electrical and Information Engineering Jiangsu University Zhenjiang 212013 China,Nanjing 210016 China
基金项目:The 15th Plan National Defence Prvenrive Research Project ( No. 413030201 )
摘    要:IntroductionFuzzy clustering is one of the important methodsin pattern recognition. The most widely used fuzzyclustering is the fuzzy c-means (FCM) algorithm1]which is conceived by Dunn2]and generalized byBezdek3]. Based on an objective function, the F…

关 键 词:模糊聚类  广义噪声聚类法  核方法  模式识别
文章编号:1005-2429(2007)02-0096-06
收稿时间:2006-06-03

Kernel Generalized Noise Clustering Algorithm
WU Xiao-hong,ZHOU Jian-jiang.Kernel Generalized Noise Clustering Algorithm[J].Journal of Southwest Jiaotong University,2007,15(2):96-101.
Authors:WU Xiao-hong  ZHOU Jian-jiang
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 it just 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.
Keywords:Fuzzy clustering  Pattern recognition  Kernel methods  Noise clustering  Kernel generalized noise clustering
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