What are different types of Hierarchical Clustering?

Data Science, AI and ML
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The algorithm groups similar objects into groups called clusters. The endpoint is a set of clusters or groups*,* where each cluster is distinct from each other cluster, and the objects within each cluster are broadly similar to each other.

This clustering technique is divided into two types:

Agglomerative Hierarchical Clustering:

  1. Make each data point a single-point cluster → forms N clusters
  2. Take the two closest data points and make them one cluster → form N-1 clusters
  3. Take the two closest clusters and make them one cluster → Forms N-2 clusters.
  4. Repeat step 3 until you are left with only one cluster.

Divisive Hierarchical Clustering:

We can say that Divisive Hierarchical clustering is precisely the opposite of Agglomerative Hierarchical clustering. In Divisive Hierarchical clustering, we take into account all of the data points as a single cluster and in every iteration, we separate the data points from the clusters which aren’t comparable. In the end, we are left with N clusters.