Co-clustering algorithms cluster a data matrix into row clusters and column clusters simultaneously. In this paper, we propose TLWCC, a two-level subspace weighting co-clustering algorithm, and introduces the idea of a two-level subspace weighting method into the co-clustering process. TLWCC adds the first level of weights on co-clusters, and then adds the second level of weights on rows and columns. The three types of weights (co-cluster, row and column weights) are computed in the clustering progress, according to the distances between co-clusters (or rows, columns) and their centers. The larger the distance is, the stronger noise it implies, so a smaller weight is given and vice verse. Thus, by giving small weights to noise, TLWCC filters out the noise and improves the co-clustering result. We propose an iterative algorithm to optimize the model. We carried out four experiments to learn more about TLWCC. The first experiment investigated the properties of three types of weights. The second experiment studied how the clustering result was influenced by the parameters. The third experiment compared the clustering performance of TLWCC with other three algorithms. The fourth experiment examined the computational efficiency of our proposed algorithm.