HIERARCHICAL
Hierarchical clustering does not partition the dataset into clusters in a single step. Instead, it involves multiple steps which run from a single cluster containing all the data points to n clusters containing single data point.
This algorithm is further classified into Divisive and Agglomerative Methods.
Hierarchical clustering can be shown using the below diagram:
Divisive Method
This method is also known as top-down clustering method. It assigns all the data points to a single cluster and then it partitions the cluster to two least similar clusters. Then the same method is applied recursively on both the clusters until we get the cluster of each data point.
Agglomerative method
It is also known as bottom-up clustering method. Here it assigns n data points to n clusters and joins the most similar clusters by computing the similarity i.e., the distance between each of the clusters. This process is continued until user gets a single cluster.