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.
>> > ====================================================
>> >
>>
>
|