Unsupervised learning is a paradigm within machine learning focused on inferring patterns and structures from datasets that lack explicit output labels or targets. This methodological approach employs algorithms to discover hidden relationships, group similar data points (clustering), reduce data complexity (dimensionality reduction), or identify anomalies, enabling the exploration and understanding of data without prior knowledge of desired outcomes.