The agglomerative hierarchical method, also known as hierarchical clustering, is a clustering technique that groups data points into a hierarchical structure based on their similarity. This method was proposed by Lance and Williams in 1967.
The algorithm starts by assigning each data point to a separate cluster, and then iteratively merges the two closest clusters until all the points belong to a single cluster. The distance between clusters can be measured using various distance metrics, such as Euclidean distance or Manhattan distance.
The algorithm works by defining a linkage criterion that determines how the distance between two clusters is calculated. This linkage criterion is typically based on the distance between the individual data points within each cluster. There are several different linkage criteria that can be used, such as single linkage, complete linkage, and average linkage.