To demonstrate how to easily implement Hierarchical clustering in Python using a real-world dataset
In the last article, we have talked about how to implement K-Means clustering, an easy but very popular unsupervised machine learning algorithm, with scikit-learn, a popular Python library for machine learning. In this article, we will explore how to implement hierarchical clustering, another popular unsupervised machine learning technique using scikit-learn,
Hierarchical clustering is used to group similar data points together into clusters based on their similarity. It is a clustering algorithm that creates a tree-like structure of clusters, known as a dendrogram, by recursively partitioning the data points based on their similarity. There are two main types of hierarchical clustering: agglomerative and divisive.
Agglomerative hierarchical clustering is the most commonly used approach. It starts with each data point in its own cluster and then iteratively merges the two closest clusters until all the data points are in one cluster. Divisive hierarchical clustering, on the other hand, starts with all the data points in one cluster and then recursively splits the clusters into smaller ones until each data point is in its own cluster.