Research map / Graph learning / Graph algorithms and clustering
Simultaneous Clustering and Ensemble
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add raw-feature similarity matrix to the co-association matrix (SCE) instead of ensemble only | NSCE · tr12 | NMI (%): 51.39 -> 58.05 | Helped |
| add raw-feature similarity to co-association matrix (SCE) vs best EC method | LSCE · ImageNet | ACC (%): 78.86 -> 82.18 | Helped |
| add raw-feature similarity to co-association matrix (SCE) vs best EC method | NSCE · la2 | NMI (%): 33.58 -> 32.90 | Hurt |
| increase trade-off alpha from 1e-4..1e+2 to very large values (ablation) | LSCE · tr12 and ImageNet | ACC | Hurt |
| vary alpha within 1e-4 to 1e+2 (ablation) | LSCE · tr12 and ImageNet | ACC | No clear effect |
| increase number of basic partitions r from 10 to 90 (ablation) | SCE · la1, mm | ACC | Helped |
| generate BPs by random feature selection instead of random parameter selection (ablation) | NSCE · mm | NMI: 51.71 -> over 30% improvement | Helped |
Closest papers by meaning
- Generalization Performance of Ensemble Clustering: From Theory to Algorithm (ICML 2025 · similarity 0.89)
- Clustering Ensemble Meets Low-rank Tensor Approximation (AAAI 2021 · similarity 0.89)
- GoT: a Growing Tree Model for Clustering Ensemble (AAAI 2021 · similarity 0.88)
- Enhancing Ensemble Clustering with Adaptive High-Order Topological Weights (AAAI 2024 · similarity 0.87)
- k-HyperEdge Medoids for Clustering Ensemble (AAAI 2025 · similarity 0.84)
- SEC: More Accurate Clustering Algorithm via Structural Entropy (AAAI 2024 · similarity 0.83)
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