Clustering on user-knowledge (ARI, FM, VM)
0.1968ARIGTSA-PCA
Evaluation results
| Method | Links | |||
|---|---|---|---|---|
| GTSA-PCAFeature Space=Two-dimensional, Clustering Algorithm=HDBSCAN2026.04 | 0.1968 | 0.4466 | 0.2644 | |
| GTSA-PCADimensionality Reduction Method=GTSA-PCA, Projection Dimension=2D, Clustering Algorithm=Agglomerative Clustering, Linkage=Ward2026.04 | 0.1815 | 0.3773 | 0.2748 | |
| Kernel PCA (RBF)Dimensionality Reduction Method=Kernel PCA (RBF), Projection Dimension=2D, Clustering Algorithm=Agglomerative Clustering, Linkage=Ward2026.04 | 0.1218 | 0.3281 | 0.2078 | |
| Regular PCADimensionality Reduction Method=Regular PCA, Projection Dimension=2D, Clustering Algorithm=Agglomerative Clustering, Linkage=Ward2026.04 | 0.1137 | 0.3539 | 0.2167 | |
| Kernel PCA (RBF)Feature Space=Two-dimensional, Clustering Algorithm=HDBSCAN2026.04 | 0.0322 | 0.322 | 0.1417 | |
| Regular PCAFeature Space=Two-dimensional, Clustering Algorithm=HDBSCAN2026.04 | -0.0114 | 0.3871 | 0.0817 |