Unsupervised learning refers to a class of machine learning algorithms that infer patterns from datasets without reference to known, or labeled, outcomes. In the context of catalysis, this can mean identifying hidden structures within experimental data, optimizing reaction conditions, or even discovering new catalytic materials. Unlike supervised learning, unsupervised learning does not rely on pre-labeled data and can be particularly useful for exploratory data analysis.
