动态权值混合C-均值模糊核聚类算法  

Mercer kernel based hybrid C-means fuzzy clustering algorithm with dynamic weight

作　　者：王亮[1] 王士同[2] 

机构地区：[1]无锡机电高等职业技术学校信息工程系,江苏无锡214151 [2]江南大学数字媒体学院,江苏无锡214122

出　　处：《计算机应用研究》2011年第8期2852-2855,共4页Application Research of Computers

基　　金：国家自然科学基金资助项目(60773206);国家"863"计划资助项目(2007AA1Z158)

摘　　要：PCM算法存在聚类重叠的缺陷,PFCM算法同时利用隶属度与典型值把数据样本划分到不同的类中,提高了算法的抗噪能力,但PFCM算法对样本分布不均衡的聚类效果并不十分理想。针对此不足,可以通过Mercer核把原来的数据空间映射到特征空间,并为特征空间的每个向量分配一个动态权值,从而得到特征空间内的目标函数。理论分析和实验结果表明,相对于其他经典模糊聚类算法,新算法具有更好的健壮性和聚类效果。PCM algorithm often tends to find the identical cluster.Proposed PFCM,which divides the data set into different clusters through producing memberships and possibilities simultaneously,along with the cluster centers.But when two highly imbalanced samples clusters are given,PFCM fails to give the desired results.In order to overcome the weakness,this paper firstly mapped the original data space to a high-dimensional feature space by Mercer kernel functions,and assigned an addtional weighting factor to each vector in the feature space.Then introduced a modified objective function for fuzzy clustering in the feature space.Theoretical analysis and experimented results testify that the new algorithm has more robust and higher clustering accuracy compared with those classic fuzzy clustering algorithm.

关 键 词：模糊聚类 权值 核函数 核参数 特征空间 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]