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 .%为了提高K-M eans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-M eans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-M eans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.
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