A type of machine learning uses unlabeled datasets to allow a program to identify patterns in data without a specific output in mind. Which approach to machine learning does this scenario describe?

Unsupervised learning uses data without labeled outputs to discover structure or patterns. This approach is useful when the goal is exploration rather than matching inputs to predefined answers. Common outcomes include grouping similar items, finding relationships, or detecting unusual patterns. It differs from methods that rely on examples with known labels or reward-based feedback.