simple kmeans.m

function [means,Nmeans] = simple_kmeans(X,K,maxerr)

% function [medias,Nmedias] = simple_kmedias(X,K,maxerr)

% Finds K prototypes
representing the samples in data matrix X,

% where each row of X
represents a sample.

% Iterates until maximum norm
difference between

% prototypes found in
successive iterations is < maxerr

%

% This script uses square
Euclidean distance,

% but can be easily modified to
use other metrics

%

% Output arguments

% means: matrix with each row a
cluster prototype

% Nmeans: Number of samples in
each cluster

%

% Example:

% X = [randn(100,1)
; 2+randn(100,1)];

% K = 2;

% [means Nmeans] =
simple_kmeans(X,K,0)

%

% Mauricio Martinez-Garcia,
2003,2007

[Ndata, dims] = size(X);

dist = zeros(1,K);

% Initial prototype assignment (arbitrary)

for i=1:K-1

means(i,:) = X(i,:);

end

means(K,:) = mean(X(K:Ndata,:));

cmp = 1 + maxerr;

while (cmp > maxerr)

% Sums (class) and data
counters (Nclass) initialization

class = zeros(K,dims);

Nclass = zeros(K,1);

% Groups each elements to
the nearest prototype

for i=1:Ndata

for j=1:K

% Euclidean distance from data to each prototype

dist(j) = norm(X(i,:)-means(j,:))^2;

end

% Find indices of minimum distance

index_min = find(~(dist-min(dist)));

% If there are multiple min distances, decide randomly

index_min = index_min(ceil(length(index_min)*rand));

class(index_min,:) = class(index_min,:) + X(i,:);

Nclass(index_min) = Nclass(index_min) + 1;

end

for i=1:K

class(i,:) = class(i,:) / Nclass(i);

end

% Compare results with
previous iteration

cmp = 0;

for i=1:K

cmp = norm(class(i,:)-means(i,:));

end

% Prototype update

means = class;

end

Nmeans = Nclass;