Rough Cluster Algorithm Based on Kernel Function

摘要：

By means of analyzing kernel clustering algorithm and rough set theory,a novel clustering algorithm,rough kernel k-means clustering algorithm,was proposed for clustering analysis.Through using Mercer kernel functions,samples in the original space were mapped into a highdimensional feature space,which the difference among these samples in sample space was strengthened through kernel mapping,combining rough set with k-means to cluster in feature space.These samples were assigned into up-approximation or low-approximation of corresponding clustering centers,and then these data that were in up-approximation and low-approximation were combined and to update cluster center.Through this method,clustering precision was improved,clustering convergence speed was fast compared with classical clustering algorithms The results of simulation experiments show the feasibility and effectiveness of the kernel clustering algorithm.

关键词： Kernel methods Kernel clustering algorithm K-means Rough set Rough clustering

作者: Tao Zhou Yanning Zhang Huiling Lu Fangan Deng Fengxiao Wang

作者单位: School of Computer Science,Northwestern Polytechnical Univ.,710072 Xian,China; Department of Maths, School of Computer Science,Northwestern Polytechnical Univ.,710072 Xian,China Department of Comp.,Shaanxi Univ.of Tech.723000 Hanzhong,Shaanxi,China Department of Maths,Shaanxi Univ.of Tech.,723000 Hanzhong,Shaanxi,China

会议类型: 国际会议

会议名称: The Third International Conference on Rough Sets and Knowledge Tevhnology(RSKT 2008)(第三届粗糙集与知识技术国际会议)

会议地点: 成都

会议语种:英文

页码: 172-179

在线出版日期: 2008-05-17（万方平台首次上网日期，不代表论文的发表时间）