Hierarchical clustering is a method of grouping data points into a tree-like structure called a dendrogram. This technique organizes data based on their similarities, allowing for a visual representation of how closely related different items are. It can be performed in two ways: agglomerative, which starts with individual points and merges them into clusters, and divisive, which begins with one large cluster and splits it into smaller ones. This clustering method is widely used in various fields, including biology for classifying species and marketing for segmenting customers. By providing a clear hierarchy, it helps researchers and analysts understand the relationships within their data more effectively.