In this paper, we propose MIC (Multi-Incomplete-view Clustering), an algorithm based on weighted nonnegative matrix factorization with L2;1 regularization. The proposed MIC works by learning the latent feature matrices for all the views and generating a consensus latent feature matrix so that the di fference between each view and the consensus is close. MIC has several advantages comparing with other existing methods. First, MIC incorporates weighted nonnegative matrix factorization, which handles the missing instances in each incomplete view. Second, MIC uses a co-regularized approach, which pushes the learned coeff cient matrices of all the views towards a common consensus. By regularizing the disagreement between the coeff icient matrices and the consensus, MIC can be easily extended to more than two incomplete views. Third, MIC incorporates L2;1 norm into the weighted nonnegative matrix factorization, which makes it robust to noises and outliers. Forth, an iterative optimization framework is used in MIC, which is scalable and proved to converge. Experiments on real datasets demonstrate the advantages of MIC.