hierarchical clustering

What is hierarchical clustering?

Hierarchical clustering (also called hierarchical cluster analysis or HCA) is a method of cluster analysis which seeks to build a hierarchy of clusters. Strategies for hierarchical clustering generally fall into two types: * **Agglomerative:** This is a "bottom-up" approach. Each observation starts in its own cluster, and pairs of clusters are merged as one moves up the hierarchy. * **Divisive:** This is a "top-down" approach. All observations start in one cluster, and splits are performed recursively as one moves down the hierarchy. Generally, the merges or splits are determined in a greedy manner. The results of hierarchical clustering are usually presented in a dendrogram. Hierarchical clustering is commonly used in exploratory data analysis to discover groupings within data without requiring pre-defined clusters. It is also used in bioinformatics, pattern recognition, and information retrieval.