Due to the 3-D property of raw HSI cubes, 3-D spectral-spatial filter becomes an effective way for extracting spectral and spatial signatures from HSI. In this paper, a new spectral-spatial sparse subspace clustering framework based on 3-D edge-preserving filtering is proposed to improve the clustering accuracy of HSI. First, the initial sparse coefficient matrix is obtained in the s-parse representation process of the classical SSC model. Then, a 3-D edge-preserving filtering is conducted on the initial sparse coefficient matrix to get a more accurate one, which is used to build the similarity graph. Finally, the clustering result of H-SI data is achieved by employing the spectral clustering algorithm to the similarity graph. Specifically, the filtered matrix can not only capture the spectral-spatial features but the intensity differences. Experimental results demonstrate the potential of including the proposed 3-D edge-preserving filtering into the SSC framework can improve the clustering accuracy.