标题： 科学发现的无监督机器学习：工作流程与最佳实践 显示英文标题

标题： Unsupervised Machine Learning for Scientific Discovery: Workflow and Best Practices

摘要： 无监督机器学习被广泛用于挖掘大规模的未标记数据集，在气候科学、生物医学、天文学、化学等领域实现数据驱动的发现。 然而，尽管其广泛应用，无监督学习工作流在做出可靠且可重复的科学发现方面缺乏标准化。 本文提出了一种用于科学领域应用无监督学习技术的结构化工作流。我们从制定可验证的科学问题开始，强调并讨论最佳实践，包括进行稳健的数据准备和探索、使用多种建模技术、通过评估无监督学习结论的稳定性和泛化能力来进行严格的验证，并促进结果的有效沟通和记录，以确保可重复的科学发现。 为了说明我们提出的流程，我们展示了一个来自天文学的案例研究，旨在根据银河系恒星的化学成分来精炼球状星团。 我们的案例研究突显了验证的重要性，并展示了精心设计的无监督学习工作流如何推动科学发现。 显示英文摘要

摘要： Unsupervised machine learning is widely used to mine large, unlabeled datasets to make data-driven discoveries in critical domains such as climate science, biomedicine, astronomy, chemistry, and more. However, despite its widespread utilization, there is a lack of standardization in unsupervised learning workflows for making reliable and reproducible scientific discoveries. In this paper, we present a structured workflow for using unsupervised learning techniques in science. We highlight and discuss best practices starting with formulating validatable scientific questions, conducting robust data preparation and exploration, using a range of modeling techniques, performing rigorous validation by evaluating the stability and generalizability of unsupervised learning conclusions, and promoting effective communication and documentation of results to ensure reproducible scientific discoveries. To illustrate our proposed workflow, we present a case study from astronomy, seeking to refine globular clusters of Milky Way stars based upon their chemical composition. Our case study highlights the importance of validation and illustrates how the benefits of a carefully-designed workflow for unsupervised learning can advance scientific discovery.