Hierarchical Clustering Algorithm

Hierarchical clustering is a method of cluster analysis that builds a hierarchy of clusters, following one of two general strategies. Agglomerative clustering takes a bottom up approach, starting with every data point as its own cluster and repeatedly merging the two most similar clusters by some distance measure until all points belong to one cluster or a stopping criterion is reached. Divisive clustering takes the opposite, top down approach, starting with all points in a single cluster and recursively splitting it into smaller ones; agglomerative methods are more commonly used for their simplicity, while divisive methods see use when the goal is to find large, distinct clusters first.