Clustering
21 methods in the atlas attack this one problem. They are rivals: each wins something the others do not.
Phrasings that mean this problem
Vector clusteringFast clusteringStreaming clusteringRobust clusteringNonlinear clusteringSoft clusteringOverlapping clusteringMode-seeking clusteringExemplar clusteringManifold clusteringLarge-data clusteringArbitrary-shape clusteringVariable-density clusteringHierarchical density clusteringHierarchical clusteringSwarm data organizationK-center approximationK-median approximation
approximation
- Greedy k-centerFarthest-first traversalcanonapproximation
- Local search k-medianSwap improvementsstandardapproximation
kernel-methods
- Kernel k-meansFeature-space clusteringstandardkernel-methods
machine-learning
- K-meansLloyd's iterationcanonmachine-learning
- K-meansK-means++ seedingcanonfull lesson ▸machine-learning
- K-meansElkan triangle-inequality skipsspecialistmachine-learning
- Mini-batch K-meansstandalonestandardmachine-learning
- K-medoidsPAM swap searchstandardmachine-learning
- Fuzzy c-meansSoft membership weightsspecialistmachine-learning
- Gaussian mixture modelExpectation-maximizationcanonfull lesson ▸machine-learning
- DBSCANDensity-reachabilitycanonmachine-learning
- OPTICSReachability orderingstandardmachine-learning
- HDBSCANCondensed cluster treestandardmachine-learning
- Mean shiftKernel density gradientstandardmachine-learning
- Agglomerative clusteringWard linkagecanonmachine-learning
- Agglomerative clusteringComplete linkagestandardmachine-learning
- Agglomerative clusteringSingle linkagestandardmachine-learning
- Spectral clusteringGraph-Laplacian eigenvectorsstandardmachine-learning
- Affinity propagationResponsibility-availability messagesspecialistmachine-learning
- BIRCHClustering-feature treespecialistmachine-learning
unconventional-computing
- Ant-based clusteringPick-drop object sortingspecialistunconventional-computing