研究三种聚类算法的可复现性，发现K-Means在多线程下结果不一致。
A K-Means, Ward and DBSCAN repeatability study
- 分解K-Means、DBSCAN、Ward算法步骤，分析各阶段可复现条件。
- 发现当OpenMP线程数超过2时，K-Means结果不一致。
- 适合关注算法可复现性的研究人员和开发者参考。
可复现性是机器学习中的关键问题，确保模型或实验能得出相同科学结论。对于特定算法，比特级完全一致的结果对科学严谨性尤为重要，有助于调试。本文将K-Means、DBSCAN和Ward三种主流聚类算法分解为基本步骤，识别每个阶段实现可复现所需的条件。通过scikit-learn库的实现案例，考察各方法的可复现性。结果显示，当OpenMP线程数超过2时，K-Means会出现不一致结果。本工作旨在提高用户与开发人员对该问题的认识，推动进一步研究与修复。
原文摘要 · Abstract (English)
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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