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From:  Greg Heath <heath@alumni.brown.edu>
Newsgroups: comp.soft-sys.matlab
Subject: Re: HELP!!!
Date: Mon, 12 Nov 2007 13:06:31 -0800
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On Nov 12, 3:34 pm, Greg Heath <he...@alumni.brown.edu> wrote:
> On Nov 12, 2:13 pm, "jenya polyakova" <jeny...@yahoo.com> wrote:
>
> >  I have three matrices a b and c of size 10x195
> > I need to perform some classification on the first raw of
> > these matrices. For example, k-means clustering would work
> > just fine. That is I would map really the first raw of
> > each matrix into 3-D space, and perform the classification.
>
> > For each cluster I need then to calculate the mean by
> > averaging over the values of the 11'th raw of matrix
> > a,band c.
> > Note, I do not have the kmeans in my matlab. Any
> > suggestions. THANKS
>
> In general, clustering a mixture of multiple class data
> via unsupervised clustering yields a suboptimal cluster
> based classifier.  However, cluster based classification
> can be improved, significantly, if supervised clustering
> using class labels, is used.
>
> Effective versions of classifiers designed via supervised
> clustering  can be  found by searching the acronyms
> of ART, LVQ and RCE.  However, I'm not sure if the
> corresponding MATLAB code is readily available.


> A simple alternative is just to cluster each class
> separately and compare classification results with
> classifiers created from clustering the multiclass mixture.

If you wish to search for MATLAB codes, the following
information may help:

Artificial Resonance Theory, Grossburg
Learning Vector Quantization, Kohonen
Reduced Coulomb Energy, Cooper
AKA
Restricted Coulomb Energy, Cooper

Hope this helps.

Greg