Clustering is inherently a process of exploratory data analysis. It has attracted more attention recently because much real-world data consists of multiple representations or views. However, it becomes increasingly problematic when dealing with large and heterogeneous data. It is worth noting that several approaches have been developed to increase computational efficiency, although most of them have some drawbacks: (1) Most existing techniques consider equal or static weights to quantify importance across different views and samples, so common and complementary features cannot be used. (2) The clustering task is performed by arbitrary initialization without caring about the rich structure of the joint discrete representation, and thus poorly executed. In this paper, we propose a novel approach called “Auto-Weighted Binary Multi-View Clustering Via Deep Initialization” for large-scale multi-view clustering based on two main scenarios. First, we consider the distinction between different views based on the importance of samples, and therefore apply a dynamic learning strategy for the automatic weighting of views and samples. Second, in the context of initializing binary clustering, we develop a new CNN feature and use a low-dimensional binary embedding by exploiting the efficient capabilities of Fourier mapping. Moreover, our approach simultaneously learns a joint discrete representation and performs direct clustering using a constrained binary matrix factorization; the optimization problem is perfectly solved in a unified learning model. Experimental results conducted on several challenging datasets demonstrate the effectiveness and superiority of the proposed approach over state-of-the-art methods in terms of accuracy, normalized mutual information, and purity.