# Expanding the class of global objective functions for dissimilarity-based hierarchical clustering

arXiv: https://arxiv.org/abs/2207.14375 · HTML: https://mlsanity.com/paper/2207.14375/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 32 references that are in the map.
- First posted on arXiv 28 Jul 2022.

## Builds on (most cited first, 7 of 7)

- [A cost function for similarity-based hierarchical clustering](https://mlsanity.com/paper/1510.05043/) (2015)
- [Approximate Hierarchical Clustering via Sparsest Cut and Spreading Metrics](https://mlsanity.com/paper/1609.09548/) (2016)
- [Hierarchical Clustering via Spreading Metrics](https://mlsanity.com/paper/1610.09269/) (2016)
- [Hierarchical Clustering better than Average-Linkage](https://mlsanity.com/paper/1808.02227/) (2018)
- [Hierarchical Clustering with Structural Constraints](https://mlsanity.com/paper/1805.09476/) (2018)
- [Hierarchical Clustering: a 0.585 Revenue Approximation](https://mlsanity.com/paper/2006.01933/) (2020)
- [Hierarchical Clustering: O(1)-Approximation for Well-Clustered Graphs](https://mlsanity.com/paper/2112.09055/) (2021)

## Abstract (arXiv)

Recent work on dissimilarity-based hierarchical clustering has led to the introduction of global objective functions for this classical problem. Several standard approaches, such as average linkage, as well as some new heuristics have been shown to provide approximation guarantees. Here we introduce a broad new class of objective functions which satisfy desirable properties studied in prior work. Many common agglomerative and divisive clustering methods are shown to be greedy algorithms for these objectives, which are inspired by related concepts in phylogenetics.