<ul><li>Unsupervised machine learning is commonly used to extract insights from large, unlabeled datasets in fields like climate science, biomedicine, astronomy, and chemistry.</li><li>This paper addresses the lack of standardization in unsupervised learning workflows for scientific discoveries by presenting a structured workflow.</li><li>The workflow includes steps such as formulating valid scientific questions, robust data preparation, exploring modeling techniques, rigorous validation, and effective communication of results.</li><li>An astronomy case study on refining Milky Way star globular clusters based on chemical composition is used to demonstrate the importance of validation and how a well-designed workflow can enhance scientific discovery.</li></ul>

Unsupervised Machine Learning for Scientific Discovery: Workflow and Best Practices

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