The hierarchical clustering technique is based on the fundamental idea of objects or features that are more related to those nearby than others far away. Bisecting K-means is an example of such hierarchical clustering algorithm that connects data objects to form clusters based on their corresponding distance.

In the hierarchical clustering technique, a cluster can be described trivially by the maximum distance needed to connect parts of the cluster. As a result, different clusters will be formed at different distances. Graphically, these clusters can be represented using a dendrogram. Interestingly, the common name hierarchical clustering evolves from the concept of the dendrogram.