> > > At 15:36 24/10/02 +0200, Christian Hennig wrote: > > > > > > >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. > > > > > > R1=R2=R3, maybe but =R???
...we look for clusters with unit correlation in data with unit correlation... > > > > > > Surely it is most unlikely that the overall correlation structure > > > would mirror > > > the within-cluster structure? It is also hard to think why that might be > > > desirable. If it were then an obvious way to achieve it would be > > > to randomly > > > allocate the data points to the three clusters. > > > > > > Murray Jorgensen OK, my suggestion will usually not give perfectly R1=R2=R3=R and not a very good clustering, but to a certain amount it tries to do both simultaneously. > I apologise if the matter is not strictly related with cluster analysis. > Maybe it can be considered an allocation-optimization problem. > > Kind regards > max However, clustering does not seem to be in any sense Max' aim (not only "not strictly"), and therefore all this does not really help. I guess that under usual conditions my suggestion will match R1=R2=R3=R worse then random allocation. Christian -- *********************************************************************** 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
