Hierarchical clustering is a method of cluster analysis that seeks to build a hierarchy of clusters. It is particularly useful for discovering the structure in data by grouping similar data points together. The process can be visualized using a dendrogram, which is a tree-like diagram that illustrates the arrangement of clusters.

There are two main approaches to hierarchical clustering: agglomerative and divisive. Agglomerative clustering is a bottom-up approach where each data point starts in its own cluster, and pairs of clusters are merged as one moves up the hierarchy. In contrast, divisive clustering is a top-down approach where all data points start in a single cluster, which is then recursively split into smaller clusters ³.

Hierarchical clustering is an unsupervised learning technique, meaning it does not rely on labeled data. Instead, it identifies patterns and similarities among data points based on their characteristics ⁴. This method is widely used in various applications, including biology, marketing, and social sciences, to uncover hidden structures in data.