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 Sparseness Constraints

作　　者：韩素青[1] 贾茹 

机构地区：[1]太原师范学院计算机科学与技术系,太原030619

出　　处：《数据采集与处理》2017年第6期1216-1222,共7页Journal of Data Acquisition and Processing

基　　金：国家自然科学基金(61273294)资助项目

摘　　要：为了提高K-Means聚类算法在高维数据下的聚类效果,提出一种基于稀疏约束非负矩阵分解的K-Means聚类算法。该算法在最优保持原始数据本质的前提下,通过在非负矩阵分解过程中对基矩阵列向量施加l1与l2范数稀疏约束,首先挖掘嵌入在高维数据中的低维数据结构,实现高维数据的低维表示,然后利用在低维数据聚类中性能良好的K-Means算法对稀疏降维后的数据进行聚类。实验结果表明提出的算法可行,并且在处理高维数据上有效。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 algorithm in low dimensional data, is used to cluster the low dimensional representation of high dimensional 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 

分 类 号：TP274[自动化与计算机技术—检测技术与自动化装置]