Hierarchical clustering is a method used to build a hierarchy of clusters, often visualized in a tree-like structure known as a dendrogram. There are two main types of this method: Agglomerative (a bottom-up approach where each observation starts as its own cluster and pairs are iteratively merged based on similarity) and Divisive (a top-down approach where all observations start in one cluster, and splits are performed recursively).

It is categorized as a Hard clustering type. In the clustering summary, it is noted as being used for customer segmentation and time series analysis.