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.
Clus- ter analysis and display of genome-wide expression patterns.Proceedings of the National Academy of Sciences, 95(25):14863–14868
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.