Research map / Learning theory and algorithms / Clustering
The SpectACl of Nonconvex Clustering: A Spectral Approach to Density-Based Clustering
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use unnormalized rather than normalized SpectACl (ablation) | SpectACl · Pulsar | NMI: 0.005 -> 0.151 | Helped |
| use unnormalized rather than normalized SpectACl (ablation) | SpectACl · Sloan | NMI: 0.071 -> 0.224 | Helped |
| use unnormalized rather than normalized SpectACl (ablation) | SpectACl · MNIST | NMI: 0.756 -> 0.339 | Hurt |
| use unnormalized rather than normalized SpectACl (ablation) | SpectACl · SNAP | NMI: 0.104 -> 0.232 | Mixed |
| use unnormalized rather than normalized SpectACl (ablation) | SpectACl · two circles | F-measure | Helped |
| increase the projected-eigenvector embedding dimension beyond 25 (ablation) | SpectACl · synthetic datasets | F-measure | Helped |
| increase the embedding dimension beyond 75 (ablation) | normalized SpectACl · two circles | F-measure | Hurt |
| vary the adjacency-defining neighborhood radius or nearest-neighbor count (ablation) | SpectACl · synthetic datasets | F-measure | No clear effect |
| use unnormalized SpectACl rather than competing clustering methods | synthetic datasets · moons, circles, and blobs with noise | F-measure | Mixed |
| use normalized SpectACl rather than Spectral Clustering | two circles · two circles with noise | F-measure | Helped |
| learn the graph structure jointly with spectral clustering | Robust Spectral Clustering · three blobs | F-measure | Hurt |
| replace class-oriented grouping with density-based grouping | SpectACl · MNIST | NMI: 0.339 vs 0.756 | Hurt |
Closest papers by meaning
- DBSCAN++: Towards fast and scalable density clustering (ICML 2019 · similarity 0.88)
- Internal Evaluation of Density-Based Clusterings with Noise (ICLR 2026 · similarity 0.88)
- Efficient Clustering Based On A Unified View Of $K$-means And Ratio-cut (NeurIPS 2020 · similarity 0.87)
- Scalable DBSCAN with Random Projections (NeurIPS 2024 · similarity 0.86)
- Optimal Graph Clustering without Edge Density Signals (NeurIPS 2025 · similarity 0.85)
- Structure-Aware Spectral Sparsification via Uniform Edge Sampling (NeurIPS 2025 · similarity 0.85)
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