Cluster Variablesoverview how to example data see also
Cluster VariablesStat > Multivariate > Cluster Variables
Use Clustering of Variables to classify variables into groups when the groups are initially not known. One reason to cluster variables may be to reduce their number. This technique may give new variables that are more intuitively understood than those found using principal components.
This procedure is an agglomerative hierarchical method that begins with all variables separate, each forming its own cluster. In the first step, the two variables closest together are joined. In the next step, either a third variable joins the first two, or two other variables join together into a different cluster. This process will continue until all clusters are joined into one, but you must decide how many groups are logical for your data. See Determining the final grouping.
Variables or distance matrix: Enter either the columns containing measurement data or a distance matrix on which to perform the hierarchical clustering of variables.
Linkage Method: Choose the linkage method that will determine how the distance between two clusters is defined.
Distance Measure: If you selected columns as input variables, choose the desired distance measure.
Correlation: Choose to use the correlation distance measure.
Absolute correlation: Choose to use the absolute correlation distance measure.
Specify Final Partition by
Number of clusters: Choose to determine the final partition by a specified number of clusters. Enter this number in the box.
Similarity level: Choose to determine the final partition by the specified level of similarity. Enter a value between 0 and 100 in the box.
Show dendrogram: Check to display the dendrogram (tree diagram), showing the amalgamation steps. Use <Customize> to change the default display of the dendrogram.