What is Hierarchical Clustering?
What is Hierarchical Clustering? How does Hierarchical Clustering work? What is meant by Hierarchical Clustering? Types of Hierarchical Clustering.
Hierarchical Clustering may be defined as a way to group similar objects into clusters. So it requires the whole concept of successively merging all the clusters into one single cluster. There are 2 types of hierarchical clustering agglomerative and divisive.
Types of Hierarchical Clustering :
- Agglomerative Hierarchical Clustering: It is a type of hierarchical clustering which uses a bottom-up approach to make clusters. It uses an approach of the partitioning of 2 most similar clusters and repeats this step until there is only one cluster.
- Divisive Hierarchical Clustering: It is a type of hierarchical clustering that uses a top-down approach to make clusters. It uses an approach of the partitioning of 2 least similar clusters and repeats this step until there is only one cluster.
How does Hierarchical Clustering work?
There are steps that help to form Hierarchical Clusters.
Step 1. Make each data point a single-point cluster, that forms ’N’ number of clusters.
Step 2. Take the two closest data points and make them one cluster so for that, we are using Euclidean Distance.
Now we in order to take the closest clusters we are using 4 options.
Option 1. Take the distance between Closest Points.
Option 2. Take the distance between Furthest Points.
Option 3. Take the distance between Average Distance that is take the average of all the distance between the points and take the closest one.
Option 4. Take the distance between Centroids.
Now, back to Step 2 applying this distance formula and choose one of the above approaches.
Step 3.Take the 2 closest clusters and make them one cluster, which is 4 clusters.
Step 4. Repeat Step 3 until there is only one cluster.
Again Repeat the steps.
Now your model is ready when all the clusters are combined to one single cluster.
