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arXiv · 2512.19772

A K-Means, Ward and DBSCAN repeatability study

Anthony Bertrand·Engelbert Mephu Nguifo·Violaine Antoine·David Hill
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Abstract

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.

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Anthony Bertrand, Engelbert Mephu Nguifo, Violaine Antoine, David Hill. 2025-12-22. A K-Means, Ward and DBSCAN repeatability study. https://arxiv.org/abs/2512.19772

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