Hierarchical clustering is a method of grouping data points into a tree-like structure called a dendrogram. This technique starts by treating each data point as an individual cluster and then progressively merges the closest clusters based on a defined distance metric. The result is a hierarchy of clusters that can be visualized, allowing for easy identification of relationships among the data. There are two main approaches to hierarchical clustering: agglomerative and divisive. Agglomerative clustering begins with individual points and merges them, while divisive clustering starts with one large cluster and splits it into smaller ones. This flexibility makes hierarchical clustering useful for various applications in data analysis.