Subspace clustering has achieved great success in many computer vision applications. However, most subspace clustering algorithms require well aligned data samples, which is often not straightforward to achieve. This paper proposes a Transformation Invariant Subspace Clustering framework by jointly aligning data samples and learning subspace representation. By alignment, the transformed data samples become highly correlated and a better affinity matrix can be obtained. The joint problem can be reduced to a sequence of Least Squares Regression problems, which can be efficiently solved. We verify the effectiveness of the proposed method with extensive experiments on unaligned real data, demonstrating its higher clustering accuracy than the state-of-the-art subspace clustering and transformation invariant clustering algorithms.