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. kmeans clustering implicitly assumes
clusters to have unit matrix correlation. So transforming the data to
unit covariance and then applying 3means 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

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Christian Hennig
Seminar fuer Statistik, ETHZentrum (LEO), CH8092 Zuerich (current)
and Fachbereich MathematikSPST/ZMS, Universitaet Hamburg
[log in to unmask], http://stat.ethz.ch/~hennig/
[log in to unmask], http://www.math.unihamburg.de/home/hennig/
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