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.
Whether you're looking to get your foot in the door, find the right person to talk to, or close the deal — accurate, detailed, trustworthy, and timely information about the organization you're selling to is invaluable.
Use Sumble to: