Hierarchical Clustering

Hierarchical clustering is a method of grouping data points into clusters based on their similarity, building a hierarchy of clusters. It starts by treating each data point as its own cluster and then iteratively merges the closest clusters until only one cluster remains, or a stopping criterion is met. This process creates a tree-like structure called a dendrogram, which visually represents the hierarchy of clusters. Scikit-learn provides an implementation of agglomerative hierarchical clustering through its AgglomerativeClustering class, which allows you to specify the linkage criterion (e.g., ward, complete, average) to determine how the distance between clusters is calculated.