Clustering Variables

Factor rotation is often used to cluster variables, but the resulting clusters are fuzzy. It is preferable to use PROC VARCLUS if you want hard (nonfuzzy), disjoint clusters. Factor rotation is better if you want to be able to find overlapping clusters. It is often a good idea to try both PROC VARCLUS and PROC FACTOR with an oblique rotation, compare the amount of variance explained by each, and see how fuzzy the factor loadings are and whether there seem to be overlapping clusters.

You can use PROC VARCLUS to harden a fuzzy factor rotation; use PROC FACTOR to create an output data set containing scoring coefficients and initialize PROC VARCLUS with this data set as follows:

proc factor rotate=promax score outstat=fact;

proc varclus initial=input proportion=0;

You can use any rotation method instead of the PROMAX method. The SCORE and OUTSTAT= options are necessary in the PROC FACTOR statement. PROC VARCLUS reads the correlation matrix from the data set created by PROC FACTOR. The INITIAL=INPUT option tells PROC VARCLUS to read initial scoring coefficients from the data set. The option PROPORTION=0 keeps PROC VARCLUS from splitting any of the clusters.