Hi, Data_guy,
1. Generally speaking it is hard to say which one of average, centroid or Ward is best, although I often lean towards ward. Average method often is more susceptible to some patterns of outliers. Both average method and centroid methods are often used to generate guide trees. You may want to look at the resulting clusters, profil them by some "KPI". In other words, often you need to study the 'configuration' of the clusters to decide which method is best for your application. Yielding different numbers of clusters is well expected off different methods.
2. Interval variables, that is, variables that not only rank but also measure "by how much", should be used for clustering. Ordinal or norminal variables should be avoided in clustering, since clustering essentially is to computer distance among observations with respect to the variables you specify.
You can use continuous variables to build some clusters. Then use the cluster variable as TARGET and build a DTree using the norminal/ rank variables you left out. Keep in mind that when clustering, you did not really conduct any variable selection, and DT at default setting is selecting variables, so you may want to 'relax' a little bit.
3. "Result of clustering" : could you explain what details you like exported and in what file structure (regular sas data set vs. special sas data set) ? what would you like to do with them in Excel? Pivotal report?
Hope this help.
Jason Xin
SAS Institute
Financial Services and Banking Unit
Boston