[R] clustering

[Liaw, Andy]

It depends a lot on what you know or don't know about the data, and what
problem you're trying to solve.  

If you know for sure it's a mixture of gaussians, likelihood based
approaches might be better.  MASS (the book) has an example of fitting
univariate mixture of gaussians using various optimizers.  The code is even
in $R_HOME/library/MASS/scripts/ch16.R.

Andy

> From: WeiWei Shi
> 
> Hi,
> thanks for reply. In fact, I tried both of them and I also tried the
> other method and I found all of them gave me different boundaries (to
> my real datasets). I am thinking about k-median but hoping to get more
> suggestions from all of you in this forum.
> 
> Cheers,
> 
> Ed
> 
> 
> On Thu, 27 Jan 2005 15:37:16 -0600, msck9 at mizzou.edu 
> <msck9 at mizzou.edu> wrote:
> > The cluster analysis should be able to handle that. I think if you
> > know how many clusters you have, "kmeans" is ok, or the EM algorithm
> > can also do that.
> > On Thu, Jan 27, 2005 at 03:44:42PM -0500, WeiWei Shi wrote:
> > > Hi,
> > > I just get a question (sorry if it is a dumb one) and I "phase" my
> > > question in the following R codes:
> > >
> > > group1<-rnorm(n=50, mean=0, sd=1)
> > > group2<-rnorm(n=20, mean=1, sd=1.5)
> > > group3<-c(group1,group2)
> > >
> > >
> > > Now, if I am given a dataset from group3, what method 
> (discriminant
> > > analysis, clustering, maybe) is the best to cluster them 
> by using R.
> > > The known info includes: 2 clusters, normal distribution (but the
> > > parameters are unknown).
> > >
> > > Thanks,
> > >
> > > Ed
> > > 
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> 
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