K-Means Cluster Analysis: Related Procedures

The main advantage of the K-Means Cluster Analysis procedure is that it is much faster than the Hierarchical Cluster Analysis procedure. On the other hand, the hierarchical procedure allows much more flexibility in your cluster analysis: you can use any of a number of distance or similarity measures, including options for binary and count data, and you do not need to specify the number of clusters a priori. Once you have identified groups, you can build a model useful for identifying new cases using the Discriminant procedure. You can also use saved cluster membership information to explore other relationships in subsequent analyses, such as Crosstabs or GLM Univariate.