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7454 ! Chapter 93: The VARCLUS Procedure procedure tries to maximize the variance that is explained by the cluster components, summed over all the clusters. The cluster components are oblique, not orthogonal, even when the cluster components are first principal components. In an ordinary principal component analysis, all components are computed from the same variables, and the first principal component is orthogonal to the second principal component and to every other principal component. In the VARCLUS procedure, each cluster component is computed from a different set of variables than all the other cluster components. The first principal component of one cluster might be correlated with the first principal component of another cluster. Hence, the VARCLUS algorithm is a type of oblique component analysis. As in principal component analysis, either the correlation or the covariance matrix can be analyzed. If correlations are used, all variables are treated as equally important. If covariances are used, variables with larger variances have more importance in the analysis. The VARCLUS procedure creates an output data set that can be used with the SCORE procedure to compute component scores for each cluster. A second output data set can be used by the TREE procedure to draw a tree diagram of hierarchical clusters. The VARCLUS procedure can be used as a variable-reduction method. A large set of variables can often be replaced by the set of cluster components with little loss of information. A given number of cluster components does not generally explain as much variance as the same number of principal components on the full set of variables, but the cluster components are usually easier to interpret