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 algorithm finds the low dimensional data structure embedded in high-dimensional data by adding l1and l2norm sparseness constraints to the non-negative matrix factorization, and achieves low dimensional representation 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.