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 as a tree structure known as a dendrogram, which illustrates the arrangement of the clusters.

There are two primary approaches to hierarchical clustering: agglomerative and divisive. Agglomerative clustering is a bottom-up approach where each data point starts in its own cluster. The algorithm then merges the closest clusters iteratively until only one cluster remains. In contrast, divisive clustering takes a top-down approach, starting with all data points in a single cluster and recursively splitting it into smaller clusters using a method like K-Means ³.

Hierarchical clustering is beneficial for identifying natural groupings in data, as it allows for the discovery of hidden patterns. It is important to note that the clusters formed should ideally reflect the inherent structure of the data, with similar data points grouped together and dissimilar points placed in different clusters ²³.