**January 16, 2021**

Hierarchical clustering algorithm adopts agglomerative learning approach. Following are the steps involved in training process of this algorithm:

- For the first iteration it starts with points and finds the closest pairs and combines them into single cluster. For later iterations it does the same with clusters.
- The process goes on till all the points are clustered into one giant cluster
- Desired number of clusters are deduced from the dendrogram, are achieved.
- The output with desired number of cluster is chosen.
- The criteria for closeness is based on the chosen method of linkage.

Following are the methods of linkage used by Hierarchical clustering algorithm:

- Single Linkage: It is a distance based criteria and measures minimum pairwise distance.
- Complete Linkage: It is a distance based criteria and measures maximum pairwise distance.
- Centroid Linkage: It is a distance based criteria and measures distance between centroids.
- Ward’s Linkage: It is a cluster similarity based criteria and measures distance between centroids.

by : Monis Khan

**Quick Summary**:

Hierarchical clustering algorithm adopts agglomerative learning approach. Following are the steps involved in training process of this algorithm: For the first iteration it starts with points and finds the closest pairs and combines them into single cluster. For later iterations it does the same with clusters. The process goes on till all the points are […]