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Multivariate Methods > Cluster Variables
Publication date: 07/30/2020

Cluster Variables

Group Similar Variables into Representative Groups

Variable clustering provides a method for grouping similar variables into representative groups. Each cluster can be represented by a single component or variable. The component is a linear combination of all variables in the cluster. Alternatively, the cluster can be represented by the variable identified to be the most representative member in the cluster.

You can use Cluster Variables as a dimension-reduction method. Instead of using a large set of variables in modeling, either the cluster components or the most representative variable in the cluster can be used to explain most of the variation in the data. In addition, dimension reduction using Cluster Variables is often more interpretable than dimension reduction using principal components.

Figure 16.1 Example of Correlation Map for Variables 


Overview of the Cluster Variables Platform

Example of the Cluster Variables Platform

Launch the Cluster Variables Platform

The Cluster Variables Report

Color Map on Correlations
Cluster Summary
Cluster Members
Standardized Components

Cluster Variables Platform Options

Additional Examples of the Cluster Variables Platform

Example of Color Map on Correlations
Example of Cluster Variables Platform for Dimension Reduction

Statistical Details for the Cluster Variables Platform

Variable Clustering Algorithm
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