How to Do Hierarchical Clustering in Python ? 5 Easy Steps Only

Hierarchical Clustering uses the distance based approach between the neighbor datapoints for clustering. Each data point is linked to its nearest neighbors. There are two ways you can do Hierarchical clustering Agglomerative that is bottom-up approach clustering and Divisive uses top-down approaches for clustering. In this tutorial, I will use the popular approach Agglomerative way. In order to find the number of subgroups in the dataset, you use dendrogram. It allows you to see linkages, relatedness using the tree graph. You will find many use cases for this type of clustering and some of them are DNA sequencing, Sentiment Analysis, Tracking Virus Diseases e.t.c. Popular Use Cases are Hospital Resource Management, Business Process Management, and Social Network Analysis. Here we are importing dendrogram, linkage, cluster, and cophenet from the scipy.cluster.hierarchy