Do you want to rank clusters by their quality or rather determine whether you have a good clustering (all clusters together)? There are many validation metrics for cluster analysis (Silhouette, C-index, AIC, Gamma, Point BiSerial, Dunn's index, Davies-Bouldin - just to name a few. It depends on your data, which one would be appropriate for you usage.

Recently we've proposed a method for combining these objectives and creating a more robust objective.

http://link.springer.com/chapter/10.1007/978-3-319-19644-2_39

Hi Tomas,

Thank you for your response, and let me elaborate a little more.  From the 8 factors 9 clusters were established based on a CCC value above "2."  However, the problem comes when trying to rank those groupings into some cohesive order.  The 8 factors have a positive to negative scaling associated to the research theme.

My thought was to rescale the factor loadings from (+2 to -2) and develop a contingency table.  This would allow for an overall good to bad ranking of quality.  Does this sounds right or can you offer a different solution?  Thank you again for your time.

Best regards,

Richard