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Highlights from
BioMedical EDU Webinar - Statistics and Curve Fitting

BioMedical EDU Webinar - Statistics and Curve Fitting

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09 Oct 2003 (Updated )

Code and data used during the BioMedical EDU Webinar given on 10/8/03.

plotClusts.m
% Copyright 2009 The MathWorks, Inc.


% Standardize the measurements from each observation so that we're looking
% at relative sizes.
meas0 = meas ./ repmat(sqrt(sum(meas.^2, 2)),1,4);

% Plot each observation as a single line consisting of four points.  That's
% known as a parallel coordinates plot.
h = plot(meas0');

% Color the data from each cluster differently.
set(h(clustIdx==1),'Color',[1 .7 .7]);
set(h(clustIdx==2),'Color',[.7 1 .7]);
set(h(clustIdx==3),'Color',[.7 .7 1]);

% Label the plot.
[dum,i] = unique(clustIdx);
legend(h(i),'Cluster 1', 'Cluster 2', 'Cluster 3');
varnames = {'Sepal Length','Sepal Width','Petal Length','Petal Width'};
set(gca,'Xlim',[.9 4.1],'XTick',1:4,'XTickLabel',varnames)
title('K-Means Clustering of Fisher''s Iris Data');

% Label the misclassified specimens.
misses = [67 71 73 84 85];
hold on; plot(meas0(misses,:)', 'k.-'); hold off

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