Multivariate Methods > Hierarchical Cluster
Publication date: 03/23/2021

Hierarchical Cluster

Group Observations Using a Tree of Clusters

Clustering is a multivariate technique that groups together observations that share similar values across a number of variables. Use it to understand the clumping structure of your data.

Hierarchical clustering combines clusters successively. The method begins by treating each observation as its own cluster. Then, at each step, the two clusters that are closest in terms of distance are combined into a single cluster. The result is depicted as a tree, called a dendrogram.

Use hierarchical clustering for small data tables with no more than several tens of thousands of rows. The algorithm is time-intensive and can run slowly for larger data tables. For larger data tables, use K Means Cluster or Normal Mixtures.

Note: Hierarchical cluster supports character columns; K Means Cluster or Normal Mixtures require numeric columns.

Figure 12.1 Example of a Constellation PlotĀ 

Example of a Constellation Plot


Overview of the Hierarchical Clustering Platform

Overview of Platforms for Clustering Observations

Example of Clustering

Launch the Hierarchical Cluster Platform

Clustering Method
Method for Distance Calculation
Data Structure
Transformations to Y, Columns Variables

Hierarchical Cluster Report

Dendrogram Report
Illustration of Dendrogram and Distance Graph
Clustering History Report

Hierarchical Cluster Options

Additional Examples of the Hierarchical Clustering Platform

Example of a Distance Matrix
Example of Wafer Defect Classification Using Spatial Measures

Statistical Details for the Hierarchical Clustering Platform

Spatial Measures
Distance Method Formulas
Want more information? Have questions? Get answers in the JMP User Community (