Soft subspace clustering is a key for high-dimensional data analysis. The existing algorithms usually require users to estimate some key global parameters in advance, and ignore the optimization of subspaces. A novel objective function, to be optimized by the soft subspace clustering algorithms, is proposed in this paper by taking into account both minimization of the compact subspace clusters and maximization of the subspaces in which the clusters exist. Based on this, a new locally feature weighting scheme is derived, and an adaptive algorithm for k-means type soft subspace clustering is presented. In the new algorithm, the optimal values of parameter are automatically computed, according with the dataset and its partitions. Experimental results carried out on some real-world and synthesis datasets demonstrate that the proposed method significantly improves the accuracy as well as the stability of the clustering results.