In many practical applications, data are represented by high-dimensional features. Although the traditional K-means algorithm is simple, it usually gets the approximation solution by eigenvalue decomposition, this method led to the model less efficient. In addition, their loss functions are sensitive to data distribution. In this paper, a clustering model of adaptive K-means with sparse constraints is proposed. The proposed method is designed by combining the dimension reduction with sparse constraints and adaptive clustering. It provides a flexible computational framework for subspace clustering and is suitable for different distribution data sets. Besides, the sparsely constraint in our method can remove redundant features and retain useful information. We develop an effective alternative optimization algorithm to solve our model. Finally, the extended experiments on several benchmark datasets demonstrate the advantages of our method over other clustering algorithms
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