Hierarchical Clustering

Hierarchical Clustering
It is a clustering technique that divides that data set into several clusters, where the user doesn’t specify the number of clusters to be generated before training the model. This type of clustering technique is also known as connectivity-based methods. In this method, simple partitioning of the data set will not be done, whereas it provides us with the hierarchy of the clusters that merge after a certain distance. After the hierarchical clustering is done on the dataset, the result will be a tree-based representation of data points, divided into clusters. This is how hierarchical clustering looks like after training is done.