In recent years, subspace clustering and multi-task clustering have received extensive attention due to their wide practical applications. Traditional subspace clustering is limited to exploring the underlying subspaces within a single task, while traditional multi-task clustering usually ignores the data distributed in some low-dimensional subspaces. However, multiple subspace clustering tasks, in reality, may be related. To resolve this issue, we introduce a multi-task subspace clustering method, which unifies transfer learning, local structure learning, and self-representation learning into a single paradigm. In particular, we suggest learning a projection matrix to perform dimensionality reduction, and exploring correlations among multiple tasks simultaneously. In addition, we design an efficient algorithm to solve the objective. The experimental findings demonstrate that the approach suggested in this study exhibits superior performance compared to the existing state-of-the-art methods in the domains of subspace clustering and multi-task clustering. Our MATLAB codes are available at https://github.com/HeKind/MTSC.