New Bounds on the Cohesion of Complete-link and Other Linkage Methods for Agglomerative Clustering
Sanjoy Dasgupta, Eduardo Sany Laber
摘要
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.
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- On the cohesion and separability of average-link for hierarchical agglomerative clusteringEduardo Laber, Miguel BatistaNeurIPS 2024 · 被引用 2 次
- Adversarially Robust Approximate Furthest NeighborKiarash Banihashem, Jeff Michael Giliberti, Prashant Gokhale, Samira Goudarzi 等ICML 2026
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