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In recent decades, multi-view clustering has received a lot of attention. The majority of previous research has assumed that all instances have complete views or at least one view that includes all instances. However, the incomplete multi-view clustering issue arises because real-world data frequently lack instances in each view. We propose a novel anchor-based sparse subspace incomplete multi-view clustering solution to this issue. Through a unified sparse subspace learning framework, the proposed method learns inter-view anchor-to-anchor and intra-view anchor-to-incomplete affinities and fuses them into a consensus sparse anchor graph, which yields a unified clustering result. Our method outperforms other incomplete multi-view clustering methods in three important ways: (1) it uses a small number of hyperparameters to learn a sparse consensus graph from the data; (2) Because of the anchor-based graph construction, it can process large datasets; (3) It is naturally capable of handling both negative entries and multiple views. Last but not least, extensive experiments show that the proposed method is effective, supporting the claim that it consistently outperforms current clustering methods. [ABSTRACT FROM AUTHOR]