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.
Clarans: A method for clustering objects for spatial data mining.IEEE Transactions on Knowledge and Data Engineering, 14(5):1003– 1016, 2002
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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.