Hierarchical clustering is a family of clustering methods that organize data points into a tree-like structure of nested groups. It may proceed by progressively merging small clusters or by recursively splitting a larger cluster. The method is useful where relationships among clusters have natural levels or taxonomic structure.
A category of clustering algorithms that create a tree of clusters. Hierarchical clustering is well-suited to hierarchical data, such as botanical taxonomies. There are two types of hierarchical clustering algorithms: - Agglomerative clustering first assigns every example to its own cluster, and iteratively merges the closest clusters to create a hierarchical tree. - Divisive clustering first groups all examples into one cluster and then iteratively divides the cluster into a hierarchical tree. Contrast with centroid-based clustering. See Clustering algorithms in the Clustering course for more information.