Clustering on mfeat-factors (ARI, FM, VM)
20.02ARIGTSA-PCA
Evaluation Results
| Method | Links | |||
|---|---|---|---|---|
| GTSA-PCAFeature Space=Two-dimensional, Clustering Algorithm=HDBSCAN2026.04 | 20.02 | 35.32 | 40.29 | |
| Kernel PCA (RBF)Feature Space=Two-dimensional, Clustering Algorithm=HDBSCAN2026.04 | 5.36 | 31 | 21.29 | |
| Regular PCAFeature Space=Two-dimensional, Clustering Algorithm=HDBSCAN2026.04 | 1.39 | 29.43 | 8.68 | |
| GTSA-PCADimensionality Reduction Method=GTSA-PCA, Projection Dimension=2D, Clustering Algorithm=Agglomerative Clustering, Linkage=Ward2026.04 | 0.2888 | 0.369 | 0.4569 | |
| Kernel PCA (RBF)Dimensionality Reduction Method=Kernel PCA (RBF), Projection Dimension=2D, Clustering Algorithm=Agglomerative Clustering, Linkage=Ward2026.04 | 0.0956 | 0.2085 | 0.267 | |
| Regular PCADimensionality Reduction Method=Regular PCA, Projection Dimension=2D, Clustering Algorithm=Agglomerative Clustering, Linkage=Ward2026.04 | 0.0357 | 0.2046 | 0.1257 |