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:**

- Make each data point a single-point cluster → forms N clusters
- Take the two closest data points and make them one cluster → form N-1 clusters
- Take the two closest clusters and make them one cluster → Forms N-2 clusters.
- 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.