# A K-Means, Ward and DBSCAN repeatability study

arXiv: https://arxiv.org/abs/2512.19772 · HTML: https://mlsanity.com/paper/2512.19772/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 25 references that are in the map.
- First posted on arXiv 22 Dec 2025.

## Builds on (most cited first, 3 of 3)

- [The Fundamental Principles of Reproducibility](https://mlsanity.com/paper/2011.10098/) (2020)
- [What is Reproducibility in Artificial Intelligence and Machine Learning Research?](https://mlsanity.com/paper/2407.10239/) (2024)
- [Reproducibility, Replicability, and Repeatability: A survey of reproducible research with a focus on high performance computing](https://mlsanity.com/paper/2402.07530/) (2024)

## Abstract (arXiv)

Reproducibility is essential in machine learning because it ensures that a model or experiment yields the same scientific conclusion. For specific algorithms repeatability with bitwise identical results is also a key for scientific integrity because it allows debugging. We decomposed several very popular clustering algorithms: K-Means, DBSCAN and Ward into their fundamental steps, and we identify the conditions required to achieve repeatability at each stage. We use an implementation example with the Python library scikit-learn to examine the repeatable aspects of each method. Our results reveal inconsistent results with K-Means when the number of OpenMP threads exceeds two. This work aims to raise awareness of this issue among both users and developers, encouraging further investigation and potential fixes.