Abstract:  Subspace clustering methods have been widely employed in many fields involved in high­dimensional data clustering and attracted more and more attentions.Subspace clustering method is a clustering analysis technique with feature selection and can achieve better performances by selecting a subset of salient features and performing clustering on the low­dimensional representation of the high­dimensional data.In many practical applications，it is known that soft clustering can provide more meaningful partition of complex data than hard clustering.In this paper，we extend the k­means clustering and present a novel reliability­based regularized weighted soft k­means clustering algorithm(RRWSKM)．The method can calculate the contribution of each dimension in each cluster and find different subsets of salient dimensions relevant to different clusters.Furthermore，it can also identify the exact data patterns by tuning model parameters and exhibit good performance.These are achieved by incorporating dimension weight entropy and partition entropy terms as regularizations into the objective function to avoid overfitting and stimulate more dimensions to contribute to identify the clusters.In addition，the reliability of dimension weights is retained by exploiting the data reliability measure，and the initial dimension weights can be determined，enhancing the performances and robustness of the proposed algorithm greatly.Since the optimization problem of RRWSKM is non­convex，the optimal solution is achieved by solving the optimization problem through an iterative update formulations.Some experiments on real­world data sets are conducted to verify the novel algorithm.The results of the experiments showed that the proposed method can exhibit the low­dimensionality representations of high­dimensional data and achieve better clustering performances than other subspace clustering methods and can handle with the high­dimensional data well.