云南民族大学学报（自然科学版）2018，Vol.27Issue(2)：125-128,153,5.DOI:10.3969/j.issn.1672-8513.2018.02.008
一种基于海量高维数据的软子空间聚类改进算法
A new soft subspace clustering algorithm for mass high-dimensional data
摘要
Abstract
The weighted soft subspace clustering method is an effective tool to process high-dimensional data. In this paper a new subspace clustering algorithm is proposed by improving the objective function of the original sub-space clustering algorithm, and the new algorithm has a good anti -noised performance. Compared with several typical soft subspace clustering algorithms,the experiment results show that the efficiency of mass high-dimension-al data clustering is fairly improved.关键词
高维数据；软子空间聚类；特征加权Key words
high-dimensional data/soft subspace clustering/feature weighting分类
信息技术与安全科学引用本文复制引用
容会,沈江炎,韩珂,周祖坤,殷洪杰..一种基于海量高维数据的软子空间聚类改进算法[J].云南民族大学学报（自然科学版）,2018,27(2):125-128,153,5.基金项目
国家自然科学基金(61662088) （61662088）
云南省应用基础研究项目(2013FZ107) （2013FZ107）
昆明冶金高等专科学校科研基金(14B004). （14B004）
