Characterizing Admissible Objective Functions for Hierarchical Clustering
Hierarchical clustering is a fundamental task in data analysis, but classical methods have long lacked a principled objective function. Dasgupta [STOC 2016] took an important step toward addressing this gap by proposing a well-motivated objective function for cluster trees. Cohen-Addad et al. [J. ACM 2019] subsequently introduced the notion of admissibility: an objective function is admissible if, whenever the input similarity matrix admits generating trees, its minimizers are precisely those generating trees. They also gave a necessary and sufficient condition for admissibility within a family of objective functions based on aggregate intercluster similarity. We refer to this family as s...
| Authors | Ryuki Tsukuba, Kazutoshi Ando |
|---|---|
| Primary category | cs.DS |
| Published | 26 April 2026 |
| Last updated at source | 16 June 2026 |
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