The multiple types of social media data have abundant information, but learning multi-modal social data is challenging due to data heterogeneity and noise in user-generated data. To address this problem, we propose a multi-view network-based clustering approach that is robust to noise and fully reflects the underlying structure of the comprehensive network. To demonstrate the proposed approach, we experimented with clustering challenging tagged images of landmarks. The results show that the proposed method outperforms other previously reported multi-view clustering algorithms and better utilizes the advantages of the network for each view. Furthermore, the tagged-image network constructed by the proposed method and the clustering results are extensively analyzed.