If you're using decision trees for segmentation, you might like to have a look at our critique Clustering versus Decision Trees, where we show how the decision tree approach can produce very simplistic segmentation compared with hierarchical cluster analysis.
Cluster can be used to complete hierarchical cluster analyses with very large surveys. For example, our banking study involved 16 000 cases and we anticipate that even larger studies are feasible. Because cluster centres and statistics are computed directly from the data matrix, other unique advantages follow:
Our procedure Classify also links with Cluster to identify new cases by reference to the resulting tree. Our retail banking case study describes a real application of Cluster and Classify in which we formed a tree with 16,000 cases taken from a corporate database and then classified all 4m of the bank's customers.
For further details, please see ourCluster Tutorial and the Clustan User Manual.