In order to solve the problem of clustering multi-view data, many multi-view subspace clustering methods have been proposed and achieved great success. However, they cannot well fix the following two problems. 1) How to leverage the difference between different views to learn a shared coefficient matrix with high quality. 2) How to further enforce the low rank property of the common coefficient matrix. To handle the above problems, an effective method dubbed dual weighted multi-view subspace clustering is proposed. In detail, the coefficient matricesare first learned for each view by self-representation model, and then they are fusedinto a common representation with a self-weighted strategy, finally weighted nuclear norm instead of nuclear norm is employed to approximate the rank of the common coefficient matrix, so that the performance of clustering can be improved. An augmented Lagrange multiplier based optimal algorithm is imposed to solve the established objective function. Experiments conducted on six real world datasets validate the superiority of the proposed method.