Clustering

Clustering seeks useful groupings when labels are absent or incomplete, helping reveal structure in data such as text, images, graphs, time series, and scientific measurements. Methods include centroid-based k-means, density and hierarchical clustering, graph and spectral methods, mixture models, and newer approaches that learn embeddings or use optimal transport; fairness, privacy, streaming, and federated settings add further constraints. Work also studies how to choose the number of clusters and assess stability, uncertainty, interpretability, and statistical significance. No single method works best across data types: deep methods do not consistently beat strong conventional algorithms, and reliable validation remains a central challenge.