UN-CCDs extend Cluster Catch Digraphs by using nearest-neighbor-distance Monte Carlo tests instead of Ripley's K to determine covering radii, yielding competitive performance on moderate-dimensional data with complex clusters and uniform noise.
On spectral clustering: Analysis and an algorithm.Advances in neural information processing systems, 14, 2001
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Clustering with Uniformity- and Neighbor-Based Random Geometric Graphs
UN-CCDs extend Cluster Catch Digraphs by using nearest-neighbor-distance Monte Carlo tests instead of Ripley's K to determine covering radii, yielding competitive performance on moderate-dimensional data with complex clusters and uniform noise.