In simple words, Divisive Hierarchical Clustering is working in exactly the opposite way as Agglomerative Hierarchical Clustering. In Divisive Hierarchical Clustering, we consider all the data points as a single cluster, and after each iteration, we separate the data points from the cluster which are not similar. Each data point that is separated is considered as an individual cluster. Finally, we’ll be remaining with n number of clusters.
Since we’re dividing the single clusters into n clusters, it is named Divisive Hierarchical Clustering. This technique is not much used in real-world applications.