<p>Graph-based multi-view clustering techniques are widely praised in many clustering tasks for their outstanding performance. The weakness of most existing graph-based multi-view clustering methods is that they tend to generate skewed clusters, which may contain few samples or even only outliers when large overlaps among clusters appear. In this case, the consistent information extracted from multiple views may be biased, potentially misguiding the multi-view clustering procedure and resulting in degraded clustering performance. Besides, conventional graph-based multi-view clustering methods usually employ spectral relaxation to optimize the graph cut problem. This solution may contain mixed signs, requiring additional clustering methods, such as K-means, to get the final results. However, this process may lead to unstable and unbalanced clustering results, even when the given samples are evenly distributed. To address these issues, in this paper, we propose a novel balanced multi-view clustering method with distance graph partition to better mine the correlations among multiple views. By fully exploring the consistent information across multiple views and dynamically evaluating the contribution of each view, the proposed method autonomously learns well-balanced common cluster labels and obtains accurate, robust clustering results. We design an efficient alternating update algorithm to optimize the proposed objective function. Experimental results on synthetic and real-world datasets show that our method has superior performance over several state-of-the-art methods.</p>