2025/12/22 by Anthony Bertrand, Engelbert Mephu Nguifo, Bertrand, Anthony +5
Computer Science · Decision Sciences · Materials Science · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning and Data Classification #Machine Learning in Materials Science #Scientific Computing and Data Management
paper · doi:10.48550/arxiv.2512.19772
openalex publication_date 2025/12/22 · openalex created_date 2025/12/25 · openalex updated_date 2026/07/28
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.