Research map / Learning theory and algorithms / Clustering
Nearest-Neighbour-Induced Isolation Similarity and Its Impact on Density-Based Clustering
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace DBSCAN's Euclidean distance with nearest-neighbour-induced Isolation Dissimilarity (aNNE) | DBSCAN (MBSCAN) · All datasets | F1: 0.653 -> 0.867 | Helped |
| Replace DBSCAN's Euclidean distance with nearest-neighbour-induced Isolation Dissimilarity (aNNE) | DBSCAN (MBSCAN) · High-dimensional data (average) | F1: 0.446 -> 0.727 | Helped |
| Use aNNE instead of iForest to induce Isolation Similarity (ablation) | MBSCAN · High-dimensional data (average) | F1: 0.568 -> 0.727 | Helped |
| Use aNNE instead of iForest to induce Isolation Similarity (ablation) | MBSCAN · All datasets | F1: 0.805 -> 0.867 | Helped |
| Replace DP with MBSCAN using aNNE | Clustering · All datasets | F1: 0.823 -> 0.867 | Helped |
| Replace DP with MBSCAN using aNNE | Clustering · Hard distribution | F1: 0.994 -> 0.992 | Hurt |
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