Another web site for mixture model-based clustering (via the EMMIX program) is: http://www.maths.uq.edu.au/~gjm Geoff McLachlan Department of Mathematics, Phone: +61 7 3365-2150 The University of Queensland, FAX: +61 7 3365-1477 St. Lucia, Brisbane, Email: [log in to unmask] Queensland 4072 Australia Web: http://www.maths.uq.edu.au/~gjm >From: "Noordam Ir J.C." <[log in to unmask]> >Subject: Re: Clustering question >To: [log in to unmask] >Precedence: list >X-Scanned-By: MIMEDefang 2.1 (www dot roaringpenguin dot com slash mimedefang) >X-Scanned-By: MIMEDefang 2.11 >Status: RO >Content-Length: 3173 > >hi, > >I agree, mixture modelling can handle your specific data. >for mixure modelling, try MCLUST >http://www.stat.washington.edu/fraley/software.html/ > >There are also some good papers and reports on the site. > >regards, >jacco >------------------------------------------- >J.C. Noordam >Agrotechnological Research Institute (ATO) >Department Production & Control Systems >P.O.Box 17,6700 AA Wageningen, the Netherlands >http://www.ato.wageningen-ur.nl >email : [log in to unmask] >tel: +31.317.475139 >fax: +31.317.475347 > > >> -----Original Message----- >> From: shannon [mailto:[log in to unmask]] >> Sent: woensdag 23 oktober 2002 13:32 >> To: [log in to unmask] >> Subject: Re: Clustering question >> >> >> Hi >> >> I would think this could occur only in a special case where a mixture >> model approach can be used. The data would need to be from >> three different >> multivariate normal distributions, each with the same >> covariance matrix. >> If you do a web search on 'mixture models' you will come up with the >> information you need. >> >> I don't know of and can't imagine any type of hierarchical or scaling >> approach that could be used. >> >> >> Bill >> --- >> >> William D. Shannon, Ph.D. >> >> Assistant Professor of Biostatistics in Medicine >> Division of General Medical Sciences and Biostatistics >> >> Washington University School of Medicine >> Campus Box 8005, 660 S. Euclid >> St. Louis, MO 63110 >> >> Phone: 314-454-8356 >> Fax: 314-454-5113 >> e-mail: [log in to unmask] >> web page: http://ilya.wustl.edu/~shannon >> >> >> On Wed, 23 Oct 2002, Marinucci, Max (MB Ergo) wrote: >> >> > Dear all >> > >> > >> > I would like to know if there is some clustering provedure >> which does the >> > following.Given a data set with n observations on k variables with >> > correlations matrix R (k x k) I would like to obtain 3 cluster of >> > approximatively equal size n1=n2=n3 that satisfy the >> following condition. >> > >> > >> > The correlations matrix of each of the three subgroups >> should be as close as >> > possible each other and with respect to the pooled >> correlation matrix, That >> > is R1=R2=R3=R >> > >> > >> > Do you have any suggestions or ideas on how to proceed to >> obtain such >> > partitions? >> > >> > >> > Thanx a lot >> > >> > >> > Massimiliano Marinucci >> > >> > >> > Phd candidate >> > >> > >> > Universidad Complutense Madrid >> > >> > >> > >> > >> > >> > >> > ==================================================== >> > This email is confidential and intended solely for the use of the >> > individual or organisation to whom it is addressed. Any opinions or >> > advice presented are solely those of the author and do not >> necessarily >> > represent those of the Millward Brown Group of Companies. >> If you are >> > not the intended recipient of this email, you should not >> copy, modify, >> > distribute or take any action in reliance on it. If you >> have received >> > this email in error please notify the sender and delete this email >> > from your system. Although this email has been checked for viruses >> > and other defects, no responsibility can be accepted for >> any loss or >> > damage arising from its receipt or use. >> > ==================================================== >> > >> >