Unsupervised learning
Algorithms that model the inputs alone, without target outputs, to discover clusters, compress the data, estimate its density or detect anomalies. Clustering and dimensionality reduction are the classic examples.
Example
Running k-means on customers’ purchase histories, with no labels at all, might reveal three segments such as bargain hunters, weekly regulars and big-ticket buyers.