R help - Jan 2005 - clustering

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

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
> 
> ______________________________________________
> R-help at stat.math.ethz.ch mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide!
http://www.R-project.org/posting-guide.html

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
> > 
> > ______________________________________________
> > R-help at stat.math.ethz.ch mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide!
http://www.R-project.org/posting-guide.html
>

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
> > > 
> > > ______________________________________________
> > > R-help at stat.math.ethz.ch mailing list
> > > https://stat.ethz.ch/mailman/listinfo/r-help
> > > PLEASE do read the posting guide! 
> http://www.R-project.org/posting-guide.html
> >
> 
> 
> ______________________________________________
> R-help at stat.math.ethz.ch mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide! 
> http://www.R-project.org/posting-guide.html
> 
>