In this study, we propose a simple and efficient clustering framework on Grassmann manifold space. Initially, we address the transformation of distance metrics from Euclidean space to Grassmann manifold space, thereby extending existing clustering methods in Euclidean space to Grassmann manifold space. For convenience, we refer to this method as the Metric-Based Clustering Framework on Grassmann Manifold (MCFG). To further enhance the performance of the clustering framework, we introduce the Uniform Manifold Approximation and Projection on Grassmann Manifold (GUMAP). GUMAP is employed to extract key features from image-set data, which are subsequently utilized within the aforementioned clustering method. We designate this integrated approach as MCFG with GUMAP. This method is applicable to all clustering analyses, thereby presenting a straightforward and effective clustering framework. Experimental results on multiple image-set datasets demonstrate that MCFG with GUMAP outperforms both MCFG and existing clustering methods on Grassmann manifold. MCFG with GUMAP effectively transfers clustering methods from Euclidean space to Grassmann manifold, establishing itself as a potent tool for clustering tasks on Grassmann manifold.