Kernelized Fuzzy Fisher Criterion based Clustering Algorithm

摘要：

Fuzzy Fisher Criterion(FFC) based clustering method uses the fuzzy Fishers linear discriminant(FLD) as its clustering objective function and is more robust to noises and outliers than fuzzy c-means clustering(FCM). But FFC can only be used in linear separable dataset. In this paper, a novel fuzzy clustering algorithm, called Kernelized Fuzzy Fisher Criterion(KFFC) based clustering algorithm, is proposed. With kernel methods KFFC can perform clustering in kernel feature space while FFC makes clustering in Euclidean space. The experimental results show that the proposed algorithm can deal with the linear non-separable problem better than FFC.

关键词： fuzzy Fisher criterion kernel methods fuzzy clustering

作者: Su-Qun Cao Zhi-Wei Hou Liu-Yang Wang Quan-Yin Zhu

作者单位: Faculty of Mechanical Engineering Huaiyin Institute of Technology Huaian 223003, China Faculty of Computer Engineering Huaiyin Institute of Technology Huaiai 223003, China

会议类型: 国际会议

会议名称: 第九届分布式计算及其应用国际学术研讨会

会议地点: 香港

会议语种:英文

页码: 87-91

在线出版日期: 2010-08-12（万方平台首次上网日期，不代表论文的发表时间）