Clustering by jointly exploiting information from multiple views can yield better performance than clustering on one single view. Some existing multi-view clustering methods aim at learning a weight for each view to determine its contribution to the final solution. However, the view-weighted scheme can only indicate the overall importance of a view, which fails to recognize the importance of each inner cluster of a view. A view with higher weight cannot guarantee all clusters in this view have higher importance than them in other views. In this paper, we propose a cluster-weighted kernel k-means method for multi-view clustering. Each inner cluster of each view is assigned a weight, which is learned based on the intra-cluster similarity of the cluster compared with all its corresponding clusters in different views, to make the cluster with higher intra-cluster similarity have a higher weight among the corresponding clusters. The cluster labels are learned simultaneously with the cluster weights in an alternative updating way, by minimizing the weighted sum-of-squared errors of the kernel k-means. Compared with the view-weighted scheme, the cluster-weighted scheme enhances the interpretability for the clustering results. Experimental results on both synthetic and real data sets demonstrate the effectiveness of the proposed method.