Multivariate Methods • Cluster Variables
Previous
 • 
Next
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 14.1 Example of Correlation Map for Variables
Contents 
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

Previous
 • 
Next
Help created on 7/12/2018