# Robust Hierarchical Clustering

arXiv: https://arxiv.org/abs/1401.0247 · HTML: https://mlsanity.com/paper/1401.0247/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jan 2014.

## Built on by (most cited first, 7 of 7)

- [Replicable Clustering](https://mlsanity.com/paper/2302.10359/) (2023)
- [Hierarchical and Density-based Causal Clustering](https://mlsanity.com/paper/2411.01250/) (2024)
- [FedCCL: Federated Dual-Clustered Feature Contrast Under Domain Heterogeneity](https://mlsanity.com/paper/2404.09259/) (2024)
- [Accelerating data-driven algorithm selection for combinatorial partitioning problems](https://mlsanity.com/paper/2402.14332/) (2024)
- [Robust convex biclustering with a tuning-free method](https://mlsanity.com/paper/2212.03122/) (2022)
- [Robust Hierarchical Clustering for Directed Networks: An Axiomatic Approach](https://mlsanity.com/paper/2108.07247/) (2021)
- [Quality check of a sample partition using multinomial distribution](https://mlsanity.com/paper/2404.07778/) (2024)

## Abstract (arXiv)

One of the most widely used techniques for data clustering is agglomerative clustering. Such algorithms have been long used across many different fields ranging from computational biology to social sciences to computer vision in part because their output is easy to interpret. Unfortunately, it is well known, however, that many of the classic agglomerative clustering algorithms are not robust to noise. In this paper we propose and analyze a new robust algorithm for bottom-up agglomerative clustering. We show that our algorithm can be used to cluster accurately in cases where the data satisfies a number of natural properties and where the traditional agglomerative algorithms fail. We also show how to adapt our algorithm to the inductive setting where our given data is only a small random sample of the entire data set. Experimental evaluations on synthetic and real world data sets show that our algorithm achieves better performance than other hierarchical algorithms in the presence of noise.