Hierarchical Clustering
Overview
Hierarchical clustering builds nested groups, often represented as a tree, so users can inspect structure at different levels rather than choose only one flat partition. Common approaches include agglomerative methods such as single, average, and Ward linkage, divisive methods, and graph- or density-based methods that use connectivity to handle irregular shapes and outliers. Current work also learns merge rules for tasks such as phylogenetics and vision, constructs hierarchies in hyperbolic space, and studies guarantees for fairness, approximation, and selecting cuts from a tree. A central tension is that methods must balance meaningful, robust clusters and useful multiscale structure against computational cost, parameter sensitivity, and the difficulty of choosing the right hierarchy or cut for a task.
Latest papers
28 papers6 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.