The paper introduces a novel approach called Projected Cross-View Learning for Unbalanced Incomplete Multi-View Clustering (PCL_UIMVC) to address the challenge of incomplete multi-view clustering (IMVC) with unbalanced missing rates across different views. The proposed method integrates a reconstruction term to fill in missing samples, a projection matrix to harmonize feature dimensions, and a graph regularization term to preserve the geometric structure of the data. An iterative algorithm is developed to solve the optimization model, which includes updating the consensus representation, the reconstruction matrix, and the projection matrix. Extensive experiments on eight benchmark datasets with various missing rates validate the superior clustering performance of PCL_UIMVC compared to ten state-of-the-art methods. The results demonstrate that PCL_UIMVC effectively handles unbalanced incomplete multi-view data and outperforms existing methods in terms of accuracy, Normalized Mutual Information (NMI), and other clustering metrics.The paper introduces a novel approach called Projected Cross-View Learning for Unbalanced Incomplete Multi-View Clustering (PCL_UIMVC) to address the challenge of incomplete multi-view clustering (IMVC) with unbalanced missing rates across different views. The proposed method integrates a reconstruction term to fill in missing samples, a projection matrix to harmonize feature dimensions, and a graph regularization term to preserve the geometric structure of the data. An iterative algorithm is developed to solve the optimization model, which includes updating the consensus representation, the reconstruction matrix, and the projection matrix. Extensive experiments on eight benchmark datasets with various missing rates validate the superior clustering performance of PCL_UIMVC compared to ten state-of-the-art methods. The results demonstrate that PCL_UIMVC effectively handles unbalanced incomplete multi-view data and outperforms existing methods in terms of accuracy, Normalized Mutual Information (NMI), and other clustering metrics.