自动化学报2017，Vol.43Issue(11)：1962-1972,11.DOI:10.16383/j.aas.2017.c160443
基于矩阵模型的高维聚类边界模式发现
Clustering Boundary Pattern Discovery for High Dimensional Space Base on Matrix Model
摘要
Abstract
Manifold learning aims to find a reasonable embed mode to map a high-dimensional space to a low dimensional space. However,the dimension of the latter may still be so high that any data mining task cannot be effectively finished. This paper proposes a simple matrix model to judge the symmetry of data object and its k nearest neighbors space,and use the symmetry rate to extract the clustering boundary. Finally,the MMC algorithm is developed. Theoretical analysis and experimental results show that the MMC can effectively detect the clustering boundary of low and high dimensional spaces.关键词
高维空间/聚类边界/矩阵模型/k近邻/对称率Key words
High dimensional space/clustering boundary/martin model/k nearest neighbors/symmetry rate引用本文复制引用
李向丽,曹晓锋,邱保志..基于矩阵模型的高维聚类边界模式发现[J].自动化学报,2017,43(11):1962-1972,11.基金项目
河南省基础与前沿技术研究项目(152300410191) 资助 Supported by Basic and Advanced Technology Research Project of Henan Province (152300410191) （152300410191）
