Summary
SPSS Statistics offers three procedures for cluster analysis.
The CLUSTER procedure performs hierarchical clustering. Hierarchical clustering starts with the casewise proximities matrix and combines cases and clusters into clusters using one of the seven clustering methods. Schedule, Dendogram, and icicle plots are aids to identifying the tentative number of clusters. Consider using CLUSTER when you are unsure of the number of clusters at the start and are willing to compute the proximity matrix.
The QUICK CLUSTER procedure performs K-means clustering, which requires specification of an explicit tentative number of clusters. K-means clustering avoids forming the proximities matrix along with all the steps of agglomeration, and so it can be used on files with lots of cases. K-means clustering is not invariant to scaling, and furthermore, can impose a spherical structure on the observed clusters even when the natural clusters in the data are of some other shape.
The TWOSTEP CLUSTER procedure...
