Unsupervised Machine Learning for Scientific Discovery: Workflow and Best Practices

TL;DR

This paper proposes a standardized workflow for applying unsupervised machine learning in scientific research, emphasizing best practices for data handling, validation, and reproducibility to enhance discovery across various scientific domains.

Contribution

It introduces a comprehensive, structured workflow for unsupervised learning in science, including validation and reproducibility, demonstrated through an astronomy case study.

Findings

Validated the importance of rigorous validation in unsupervised learning

Demonstrated improved scientific discovery through structured workflow

Case study in astronomy highlights workflow effectiveness

Abstract

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.