Clustering with Uniformity- and Neighbor-Based Random Geometric Graphs

ArXi:2501.06268v3 Announce Type: replace We propose a graph-based clustering method based on Cluster Catch Digraphs (CCDs) that extends their applicability to moderate-dimensional data settings. Existing CCD variants, such as RK-CCDs, rely on spatial randomness tests based on Ripley's K function, which exhibit performance degradation as dimensionality increases. To address this limitation, we covering radii, resulting in the proposed Uniformity- and Neighbor-based CCDs (UN-CCDs