<p>Multi-view clustering has been extensively studied to group large amounts of multi view data using multiple information sources. Traditional multi-view clustering algorithms often exhibit quadratic or even cubic complexity, posing challenges for large-scale datasets. Recently, several algorithms have employed anchor graphs to reduce expensive time complexity. However, these algorithms fail to respect the intrinsic geometric structure within individual views. To address this issue, this paper proposes a novel multi-view clustering algorithm, called fast multi-view clustering with geometric structures (FMCGS). FMCGS constructs an affinity graph to model the geometric structure within each view separately. Moreover, it can adaptively assign different weights to different views, expecting views that play a more important role in clustering to have greater weights. We also present an optimization scheme based on iterative updating of three factor matrices to solve the proposed model. Extensive experiments on nine real-world datasets validate the superiority of the proposed approach over state-of-the-art methods.</p>