Two remarks: 1) On cores/consensus: Similar methods tend to produce similar results, whatever the data say. I think these core/consensus methods work well only if the compared CA methods are sufficiently distinct, and then there is a good chance that no "cores" are found (if the clustering in the data is not obvious).
2) On clustering with R1=R2=R3=R. k-means clustering implicitly assumes clusters to have unit matrix correlation. So transforming the data to unit covariance and then applying 3-means will give clusters with approximately R1=R2=R3=R. May be even better with a Gausiian mixture model where covariance matrices of the clusters are restricted to cI, where I is unit matrix and c may depend on the cluster. This again has to be applied to data which is sphered, i.e. transformed to unit covariance first. I hope this "covariance model" can be found in mclust, mentioned previously in this discussion. Christian Hennig -- *********************************************************************** Christian Hennig Seminar fuer Statistik, ETH-Zentrum (LEO), CH-8092 Zuerich (current) and Fachbereich Mathematik-SPST/ZMS, Universitaet Hamburg [EMAIL PROTECTED], http://stat.ethz.ch/~hennig/ [EMAIL PROTECTED], http://www.math.uni-hamburg.de/home/hennig/ ####################################################################### ich empfehle www.boag.de
