Clustering on R^d
94.9ARISpectral
Evaluation Results
| Method | Links | |
|---|---|---|
| SpectralSpace=R^d, Train=O(n^3), Memory=O(n^2)2026.05 | 94.9 | |
| HDBSCANSpace=Metric, Train=O(n), Memory=O(n)2026.05 | 87 | |
| DBSCANSpace=Metric, Train=O(n), Memory=O(n)2026.05 | 84.4 | |
| OPTICSSpace=R^d, Train=O(n^2), Memory=O(n)2026.05 | 80.9 | |
| MMD GMM (Gaussian)Space=Hilbert, Train=O(nK), Infer=O(nK), Memory=O(nK)2026.05 | 69.2 | |
| Gaussian Mixture (EM)Space=R^d, Train=O(nK), Infer=O(nK), Memory=O(nK)2026.05 | 68.3 | |
| MMD GMM (Polynomial)Space=Hilbert, Train=O(nK), Infer=O(nK), Memory=O(nK)2026.05 | 61.5 | |
| WardSpace=R^d, Train=O(n^2), Memory=O(n^2)2026.05 | 61.3 | |
| K-MedoidsSpace=Metric, Train=O(n^2 K), Infer=O(nK), Memory=O(n^2)2026.05 | 56.9 | |
| Kernel k-GroupsSpace=Metric, Train=O(Kn^2), Memory=O(n^2)2026.05 | 56.7 | |
| MeanShiftSpace=R^d, Train=O(n^2), Infer=O(n), Memory=O(n)2026.05 | 56.4 | |
| Hierarchical (Avg)Space=Metric, Train=O(n^2 log n), Memory=O(n^2)2026.05 | 55.5 | |
| MiniBatch KMeansSpace=R^d, Train=O(nK), Infer=O(nK), Memory=O(n)2026.05 | 55.4 | |
| BIRCHSpace=R^d, Train=O(n), Infer=O(nK), Memory=O(n)2026.05 | 52.5 | |
| Affinity PropagationSpace=R^d, Train=O(n^2), Memory=O(n^2)2026.05 | 48.9 | |
| K-CenterSpace=Metric, Train=O(nK), Infer=O(nK), Memory=O(n)2026.05 | 39.2 |