What is Hierarchical Clustering?

Hierarchical clustering is a type of clustering algorithm that is used to group similar data points into clusters. It is a bottom-up approach that starts by treating each data point as a single cluster and then merges the closest pair of clusters until all the data points are grouped into a single cluster or a pre-defined number of clusters.

Hierarchical clustering can be divided into two types: 

• Agglomerative Clustering 
• Divisive Clustering

 Agglomerative clustering is a bottom-up approach that starts by treating each data point as a single cluster and then merges the closest pair of clusters until all the data points are grouped into a single cluster or a pre-defined number of clusters. Divisive clustering is a top-down approach that starts by treating all the data points as a single cluster and then splits the cluster into smaller clusters until each cluster contains only one data point.

Agglomerative clustering is a hierarchical clustering algorithm that is used to group similar data points into clusters. It is a bottom-up approach that starts by treating each data point as a single cluster and then merges the closest pair of clusters until all the data points are grouped into a single cluster or a pre-defined number of clusters.

In this blog, we will discuss how to perform agglomerative clustering in Scikit-Learn, a popular machine-learning library for Python. We will also discuss the differences between agglomerative clustering with and without structure.

Before diving into the details of agglomerative clustering in Scikit-Learn, let’s first understand the basics of hierarchical clustering and how it works.