Hierarchical Cluster Analysis – Used to identify relatively homogeneous groups of cases (or variables) based on selected characteristics, using an algorithm that starts with each case in a separate cluster and combines clusters until only one is left.Select one of two methods for classifying cases, either updating cluster centers iteratively or classifying only.
Analyze raw variables or choose from a variety of standardizing transformations. Distance or similarity measures are generated by the Proximities procedure. Discriminant – Offers a choice of variable selection methods, statistics at each step and in a final summary output is displayed at each step and/or in final form.TwoStep Cluster Analysis – Group observations into clusters based on nearness criterion, with either categorical or continuous level data specify the number of clusters or let the number be chosen automatically.Statistics are displayed at each stage to help you select the best solution.
