A K-Means, Ward and DBSCAN repeatability study
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866912783430320128 |
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| author | Bertrand, Anthony Nguifo, Engelbert Mephu Antoine, Violaine Hill, David |
| author_facet | Bertrand, Anthony Nguifo, Engelbert Mephu Antoine, Violaine Hill, David |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_19772 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A K-Means, Ward and DBSCAN repeatability study Bertrand, Anthony Nguifo, Engelbert Mephu Antoine, Violaine Hill, David Machine Learning Artificial Intelligence 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. |
| title | A K-Means, Ward and DBSCAN repeatability study |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2512.19772 |