# Inapproximability for Local Correlation Clustering and Dissimilarity Hierarchical Clustering

arXiv: https://arxiv.org/abs/2010.01459 · HTML: https://mlsanity.com/paper/2010.01459/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Oct 2020.

## Built on by (most cited first, 1 of 1)

- [Hierarchical Clustering: New Bounds and Objective](https://mlsanity.com/paper/2111.06863/) (2021)

## Abstract (arXiv)

We present hardness of approximation results for Correlation Clustering with local objectives and for Hierarchical Clustering with dissimilarity information. For the former, we study the local objective of Puleo and Milenkovic (ICML '16) that prioritizes reducing the disagreements at data points that are worst off and for the latter we study the maximization version of Dasgupta's cost function (STOC '16). Our APX hardness results imply that the two problems are hard to approximate within a constant of 4/3 ~ 1.33 (assuming P vs NP) and 9159/9189 ~ 0.9967 (assuming the Unique Games Conjecture) respectively.