Research map / Learning theory and algorithms / Clustering
A Three-Level Optimization Model for Nonlinearly Separable Clustering
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use NKM-NSC instead of accelerated spectral clustering SC+N | NKM-NSC · Agg | ARI: 0.5131±0.2423 -> 0.9920±0.0014 | Helped |
| use NKM-NSC instead of SC+RF | NKM-NSC · MNIST | ARI: 0.3234±0.0386 -> 0.5427±0.0362 | Helped |
| use NKM-NSC instead of SC+KM | NKM-NSC · MNIST | NMI: 0.6134±0.0091 -> 0.6632±0.0222 | Helped |
| use NKM-NSC instead of exact SC | NKM-NSC · MNIST | ARI: 0.5174±0.0067 -> 0.5427±0.0362 | Helped |
| use NKM-NSC instead of exact SC | NKM-NSC · T4.8k | ARI: 1.0000±0.0000 -> 0.8807±0.0406 | Hurt |
| use NKM-NSC instead of exact SC | NKM-NSC · Iris | ARI: 0.9038±0.0042 -> 0.8921±0.0193 | No clear effect |
| use NKM-NSC instead of KKM | NKM-NSC · Ring | ARI: 0.4801±0.0589 -> 1.0000±0.0000 | Helped |
| use NKM-NSC instead of DP+KD on large dataset | NKM-NSC · MNIST 10,000 objects | running time (s): 118.11 -> 92.34 | Helped |
| use NKM-NSC instead of exact SC | NKM-NSC · MNIST 10,000 objects | running time (s): 20,261.00 -> 92.34 | Helped |
| use NKM-NSC instead of SC+KM for speed | NKM-NSC · MNIST 10,000 objects | running time (s): 15.81 -> 92.34 | Hurt |
| increase number of linear clusters p and ensemble clusterings T (ablation) | NKM-NSC · MNIST | NMI and ARI | Helped |
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