Research map / Learning theory and algorithms / Clustering
Approximation Bounds for Hierarchical Clustering: Average Linkage, Bisecting K-means, and Local Search
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use the local-search split objective (split revenue) with improving single-node moves for divisive clustering | divisive hierarchical clustering · revenue hierarchical clustering objective | approximation ratio | Helped |
| split clusters by the k-means similarity objective (bisecting k-means) instead of split revenue | divisive hierarchical clustering · revenue hierarchical clustering objective | approximation ratio | Hurt |
| use average linkage agglomerative merging | agglomerative hierarchical clustering · revenue hierarchical clustering objective | approximation ratio | Helped |
Closest papers by meaning
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- On the cohesion and separability of average-link for hierarchical agglomerative clustering (NeurIPS 2024 · similarity 0.91)
- Ultrametric Cluster Hierarchies: I Want ‘em All! (NeurIPS 2025 · similarity 0.91)
- Average Sensitivity of Hierarchical $k$-Median Clustering (ICML 2025 · similarity 0.90)
- Hierarchical Overlapping Clustering on Graphs: Cost Function, Algorithm and Scalability (ICML 2025 · similarity 0.90)
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