New Bounds on the Cohesion of Complete-link and Other Linkage Methods for Agglomerative Clustering
Sanjoy Dasgupta, Eduardo Sany Laber
Abstract
Linkage methods are among the most popular algorithms for hierarchical clustering. Despite their relevance the current knowledge regarding the quality of the clustering produced by these methods is limited. Here, we improve the currently available bounds on the maximum diameter of the clustering obtained by complete-linkage for metric spaces. One of our new bounds, in contrast to the existing ones, allows us to separate complete-linkage from single-linkage in terms of approximation for the diameter, which corroborates the common perception that the former is more suitable than the latter when the goal is producing compact clusters. We also show that our techniques can be employed to derive upper bounds on the cohesion of a class of linkage methods that includes the quite popular average-linkage.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 106c17df-b4aa-47e5-9e8e-8fc157103d45Cited by top-tier papers2
- On the cohesion and separability of average-link for hierarchical agglomerative clusteringEduardo Laber, Miguel BatistaNeurIPS 2024 · 2 citations
- Adversarially Robust Approximate Furthest NeighborKiarash Banihashem, Jeff Michael Giliberti, Prashant Gokhale, Samira Goudarzi et al.ICML 2026
Builds on1
Related papers
- Hierarchical Agglomerative Graph Clustering in Nearly-Linear TimeLaxman Dhulipala, David Eisenstat, Jakub Lacki, Vahab S. Mirrokni et al.ICML 2021 · 30 citations
- Hierarchical Agglomerative Graph Clustering in Poly-Logarithmic DepthLaxman Dhulipala, David Eisenstat, Jakub Lacki, Vahab Mirrokni et al.NeurIPS 2022 · 24 citations
- Learning to LinkMaria-Florina Balcan, Travis Dick, Manuel LangICLR 2020
- Hierarchical clustering with dot products recovers hidden tree structureAnnie Gray, Alexander Modell, Patrick Rubin-Delanchy, Nick WhiteleyNeurIPS 2023 · 3 citations
- Improving Ultrametrics Embeddings Through CoresetsVincent Cohen-Addad, Rémi de Joannis de Verclos, Guillaume LagardeICML 2021 · 10 citations
