In Unsupervised Learning, Data Clustering tries to Collect similar data in groups and Collect dissimilar data in other groups. The machine is trained on a set of unlabeled data, which means that the input data is not paired with the desired output.
The machine then learns to find patterns and relationships in the data.
Unsupervised learning is often used for tasks such as clustering, dimensionality reduction, and anomaly detection.