How does Hierarchical Clustering work?

The hierarchical clustering algorithm is an iterative algorithm that starts by treating each data point as a single cluster. In each iteration, the algorithm identifies the pair of clusters that are closest to each other and then merges them into a single cluster. This process continues until all the data points are grouped into a single cluster or a pre-defined number of clusters.

The algorithm uses a distance metric to measure the similarity between the data points and to determine the closest pair of clusters. Some common distance metrics used in hierarchical clustering are Euclidean distance, Manhattan distance, and cosine similarity.

Once all the data points are grouped into clusters, the algorithm creates a dendrogram that shows the hierarchical structure of the clusters. A dendrogram is a tree-like diagram that shows the sequence of cluster merges and the distance at which the clusters are merged.

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.