30
Jan

Hierarchical Clustering is a type of unsupervised machine learning algorithm used to group similar data points together. The goal of this algorithm is to create a hierarchy of clusters, where each cluster is a subset of the previous one. The algorithm starts by treating each data point as its own cluster. It then repeatedly merges the two closest clusters, until all points are in the same cluster or a stopping criterion is met. The result is a tree-like structure called a dendrogram, which shows the hierarchy of the clusters. There are two main types of Hierarchical Clustering: Agglomerative and Divisive.…