PHIDA uses persistent homology to constrain node-to-cluster mapping in ART-based online clustering and reports top average ranks on 24 benchmarks in both stationary and nonstationary settings.
Hierarchical grouping to optimize an objective function.Journal of the American Statistical Association, 58(301):236–244
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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.
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PHIDA: Persistence-Guided Node-to-Cluster Mapping for Online Clustering
PHIDA uses persistent homology to constrain node-to-cluster mapping in ART-based online clustering and reports top average ranks on 24 benchmarks in both stationary and nonstationary settings.
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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.