Path: news.mathworks.com!newsfeed-00.mathworks.com!newsfeed2.dallas1.level3.net!news.level3.com!postnews.google.com!22g2000hsm.googlegroups.com!not-for-mail
From:  Greg Heath <heath@alumni.brown.edu>
Newsgroups: comp.soft-sys.matlab
Subject: Re: HELP!!!
Date: Mon, 12 Nov 2007 12:34:32 -0800
Organization: http://groups.google.com
Lines: 32
Message-ID: <1194899672.262350.297810@22g2000hsm.googlegroups.com>
References: <fha8l9$f1l$1@fred.mathworks.com>
NNTP-Posting-Host: 68.39.14.248
Mime-Version: 1.0
Content-Type: text/plain; charset="us-ascii"
X-Trace: posting.google.com 1194899672 3999 127.0.0.1 (12 Nov 2007 20:34:32 GMT)
X-Complaints-To: groups-abuse@google.com
NNTP-Posting-Date: Mon, 12 Nov 2007 20:34:32 +0000 (UTC)
In-Reply-To: <fha8l9$f1l$1@fred.mathworks.com>
User-Agent: G2/1.0
X-HTTP-UserAgent: Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1; AT&T CSM6.0; AT&T CSM 6; (R1 1.3); .NET CLR 1.0.3705),gzip(gfe),gzip(gfe)
Complaints-To: groups-abuse@google.com
Injection-Info: 22g2000hsm.googlegroups.com; posting-host=68.39.14.248;
Xref: news.mathworks.com comp.soft-sys.matlab:437162


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.

Hope this helps.

Greg