Hierarchical Clustering is one of four platforms that JMP provides for clustering observations. For a comparison of all four methods, see Overview of Platforms for Clustering Observations.

The hierarchical clustering method starts with each observation forming its own cluster. At each step, the clustering process calculates the distance between all pairs of clusters and combines the two clusters that are closest together. This process continues until all the points are contained in one cluster. Hierarchical clustering is also called agglomerative clustering because of the combining approach that it uses.

Tip: The hierarchical clustering process starts with n(n + 1)/2 distances for n observations, except when the Fast Ward method is used. For this reason, this method can take a long time to run when n is large. For large numbers of numeric observations, consider K Means Cluster or Normal Mixtures.