计算机工程2011，Vol.37Issue(15)：23-26,4.DOI:10.3969/j.issn.1000-3428.2011.15.006
无参数聚类边界检测算法的研究
Research on Nonparametric Clustering Boundary Detection Algorithm
摘要
Abstract
In order to detect boundary points of clustering automatically and effectively, and to eliminate the impact of parameters on the results of the boundary detection, a new nonparametric boundary detection algorithm based on delaunay triangulation is presented. This algorithm calculates the boundary degree for each point in the generated delaunay triangulation without any parameters. According to the boundary degree's threshold that is automatically calculated by k-mcans, dataset is divided into two parts: candidate set of boundary points and the set of non-boundary points. Based on the characteristics of the noise points, the noise points are removed from the candidate set of boundary points. It detects out boundary points of clustering. Experimental results show that the algorithm can identify boundary points in noisy datasets containing clustering of different shapes and sizes effectively and efficiently.关键词
边界点/无参数/边界度/聚类/三角剖分Key words
boundary points/nonparametric/boundary degree/clustering/delaunay triangulation分类
计算机与自动化引用本文复制引用
邱保志,许敏..无参数聚类边界检测算法的研究[J].计算机工程,2011,37(15):23-26,4.基金项目
国家自然科学基金资助项目(60673087) （60673087）
河南省教育厅自然科学基金资助项目(2009A520028) （2009A520028）
郑州大学骨干教师基金资助项目 （）
