Contents

kmeansexample.m

From A First Course in Machine Learning, Chapter 6. Simon Rogers, 01/11/11 [simon.rogers@glasgow.ac.uk] Example of K-means clustering

clear all;close all;

Load the data

load ../data/kmeansdata

Plot the data

figure(1);hold off
plot(X(:,1),X(:,2),'ko');

Randomly initialise the means

K = 3; % The number of clusters
cluster_means = rand(K,2)*10-5;

Iteratively update the means and assignments

converged = 0;
N = size(X,1);
cluster_assignments = zeros(N,K);
di = zeros(N,K);
cols = {'r','g','b'};
while ~converged

Update assignments

    for k = 1:K
        di(:,k) = sum((X - repmat(cluster_means(k,:),N,1)).^2,2);
    end
    old_assignments = cluster_assignments;
    cluster_assignments = (di == repmat(min(di,[],2),1,K));
    if sum(sum(old_assignments~=cluster_assignments))==0
        converged = 1;
    end
    % Plot the assigned data
    figure(1); hold off
    for k = 1:K
        plot(X(cluster_assignments(:,k),1),X(cluster_assignments(:,k),2),...
            'ko','markerfacecolor',cols{k});
        hold on
    end
    title('Updated assignments');
    % Update means
    for k = 1:K
        if sum(cluster_assignments(:,k))==0
            % This cluster is empty, randomise it
            cluster_means(k,:) = rand(1,2)*10-5;
        else
            cluster_means(k,:) = mean(X(cluster_assignments(:,k),:),1);
        end
    end
    % Plot the means
    figure(1)
    for k = 1:K
        plot(cluster_means(k,1),cluster_means(k,2),'ks','markersize',15,...
            'markerfacecolor',cols{k});
    end
    title('Updated means');

end

Published with MATLAB® 7.13