Unsupervised learning technique that groups similar data into clusters without prior labels. The algorithm determines how many groups exist and which elements belong to each one based on similarity.
In AI-901: a typical use case is customer segmentation or grouping similar news articles. It's unsupervised because you don't predefine the groups. K-means is the most commonly mentioned algorithm, without needing to understand how it works internally.
Clustering (ML) is a concept that can show up on the exam. Check the full guide to see it in context.