Kmean matlab code

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function testkMeans

clc

clear all

close all

K = 4;

dataSet = load('testSet.txt');

figure

scatter(dataSet(:,1),dataSet(:,2))

[row,col] = size(dataSet);

% 存储质心矩阵

centSet = zeros(K,col);

% 随机初始化质心

for i= 1:col

   minV = min(dataSet(:,i));

   rangV = max(dataSet(:,i)) - minV;

   centSet(:,i) = repmat(minV,[K,1]) + rangV*rand(K,1);

end

 

% 用于存储每个点被分配的cluster以及到质心的距离

clusterAssment = zeros(row,2);

clusterChange = true;

while clusterChange

   clusterChange = false;

    %计算每个点应该被分配的cluster

   for i = 1:row

       % 这部分可能可以优化

        minDist = 10000;

       minIndex = 0;

       for j = 1:K

           distCal = distEclud(dataSet(i,:) , centSet(j,:));

           if (distCal < minDist)

                minDist = distCal;

                minIndex = j;

           end

       end

        if minIndex ~= clusterAssment(i,1)           

           clusterChange = true;

       end

       clusterAssment(i,1) = minIndex;

       clusterAssment(i,2) = minDist;

   end

   

    %更新每个cluster 的质心

   for j = 1:K

       simpleCluster = find(clusterAssment(:,1) == j);

       centSet(j,:) = mean(dataSet(simpleCluster',:));

   end

    centSet

end

figure

%scatter(dataSet(:,1),dataSet(:,2),5)

for i = 1:K

   pointCluster = find(clusterAssment(:,1) == i);

   scatter(dataSet(pointCluster,1),dataSet(pointCluster,2),5)

   hold on

end

%hold on

scatter(centSet(:,1),centSet(:,2),300,'+')

hold off

 

end

 

% 计算欧式距离

function dist = distEclud(vecA,vecB)

   dist  = sum(power((vecA-vecB),2));

end

testSet=

1.658985 4.285136
-3.453687 3.424321
4.838138 -1.151539
-5.379713 -3.362104
0.972564 2.924086
-3.567919 1.531611
0.450614 -3.302219
-3.487105 -1.724432
2.668759 1.594842
-3.156485 3.191137
3.165506 -3.999838
-2.786837 -3.099354
4.208187 2.984927
-2.123337 2.943366
0.704199 -0.479481
-0.392370 -3.963704
2.831667 1.574018
-0.790153 3.343144
2.943496 -3.357075
-3.195883 -2.283926
2.336445 2.875106
-1.786345 2.554248
2.190101 -1.906020
-3.403367 -2.778288
1.778124 3.880832
-1.688346 2.230267
2.592976 -2.054368
-4.007257 -3.207066
2.257734 3.387564
-2.679011 0.785119
0.939512 -4.023563
-3.674424 -2.261084
2.046259 2.735279
-3.189470 1.780269
4.372646 -0.822248
-2.579316 -3.497576
1.889034 5.190400
-0.798747 2.185588
2.836520 -2.658556
-3.837877 -3.253815
2.096701 3.886007
-2.709034 2.923887
3.367037 -3.184789
-2.121479 -4.232586
2.329546 3.179764
-3.284816 3.273099
3.091414 -3.815232
-3.762093 -2.432191
3.542056 2.778832
-1.736822 4.241041
2.127073 -2.983680
-4.323818 -3.938116
3.792121 5.135768
-4.786473 3.358547
2.624081 -3.260715
-4.009299 -2.978115
2.493525 1.963710
-2.513661 2.642162
1.864375 -3.176309
-3.171184 -3.572452
2.894220 2.489128
-2.562539 2.884438
3.491078 -3.947487
-2.565729 -2.012114
3.332948 3.983102
-1.616805 3.573188
2.280615 -2.559444
-2.651229 -3.103198
2.321395 3.154987
-1.685703 2.939697
3.031012 -3.620252
-4.599622 -2.185829
4.196223 1.126677
-2.133863 3.093686
4.668892 -2.562705
-2.793241 -2.149706
2.884105 3.043438
-2.967647 2.848696
4.479332 -1.764772
-4.905566 -2.911070

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