Research map / Learning theory and algorithms / Clustering
Nearly-Optimal Hierarchical Clustering for Well-Clustered Graphs
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace arbitrary-vertex bucketing with SpecWRSC (spectral clustering + degree bucketing + WRSC) versus MS, Linkage++, AverageLinkage | Hierarchical clustering on real-world datasets · Iris, Wine, Cancer, Newsgroup | running time: lowest of four methods on all datasets | Helped |
| SpecWRSC algorithm versus MS and Linkage++ baselines | Dasgupta cost on real-world datasets · Iris, Wine, Cancer, Newsgroup | Dasgupta cost: lower cost than MS and Linkage++ (figure only) | Helped |
| SpecWRSC on GEMSEC Facebook artist graph | Scalability · GEMSEC Facebook artist (50,515 nodes) | running time: only method finishing within 12 hours | Helped |
| SpecWRSC on synthetic SBM with 100,000 nodes | Scalability · SBM n=100,000 | running time: finishes in ~4 hours while all others time out after 12 hours | Helped |
| Spectral-gap condition: SpecWRSC benefits when lambda_{k+1}/lambda_k is large | SpecWRSC on real-world datasets · Iris, Wine, Cancer, Newsgroup | Dasgupta cost relative to gap | Mixed |
Closest papers by meaning
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