Why should the clustering of variables be used?
What will be an ideal response?
Cluster analysis is also used for clustering variables to identify homogeneous groups. Hierarchical clustering of variables can aid in the identification of unique variables, or variables that make a unique contribution to the data. Clustering can also be used to reduce the number of variables. Associated with each cluster is a linear combination of the variables in the cluster, called the cluster components. A large set of variables can often be replaced by the set of cluster components with little loss of information. However, a given number of cluster components does not generally explain as much variance as the same number of principal components. Why, then, should the clustering of variables be used? Cluster components are usually easier to interpret than the principal components even if the latter are rotated.
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