数据采集与处理2017，Vol.32Issue(6)：1216-1222,7.DOI:10.16337/j.1004-9037.2017.06.017
基于稀疏约束非负矩阵分解的K-Means聚类算法
K-Means Clustering Algorithm Based on Non-negative Matrix Factorization with Sparse-ness Constraints
摘要
Abstract
To improve the quality of K-Means clustering in high-dimensional data ,a K-Means clustering algorithm is presented based on non-negative matrix factorization with sparseness constraints .The algo-rithm finds the low dimensional data structure embedded in high-dimensional data by adding l1 and l2 norm sparseness constraints to the non-negative matrix factorization ,and achieves low dimensional representa-tion of high dimensional data .Then the K-Means algorithm ,which is the high performance clustering al-gorithm in low dimensional data ,is used to cluster the low dimensional representation of high dimension-al data .The experimental results show that the proposed algorithm is feasible and effective in dealing with high-dimensional data .关键词
高维数据/非负矩阵分解/稀疏约束/k-means聚类Key words
high dimensional data/non-negative matrix factorization/sparse constraint/K-Means cluste-ring分类
信息技术与安全科学引用本文复制引用
韩素青,贾茹..基于稀疏约束非负矩阵分解的K-Means聚类算法[J].数据采集与处理,2017,32(6):1216-1222,7.基金项目
国家自然科学基金(61273294)资助项目. （61273294）
