The conventional fuzzy clustering algorithms(FCM) fall short when clustering is performed in high dimensional spaces, because they use the Euclidean metric to compute the distance between data points. In this paper, a fuzzy subspace clustering algorithm is proposed by introducing the feature weighted distance and the power exponent and into the objective function of FCM. The global convergence property of the proposed algorithm is discussed. The experimental results on real dataset show the effectiveness of the algorithm.