KMEANS 聚类算法实现程序
%K-means算法主程序
k=4;
x =[ 1.2126 2.1338 0.5115 0.2044
-0.9316 0.7634 0.0125 -0.2752
-2.9593 0.1813 -0.8833 0.8505
3.1104 -2.5393 -0.0588 0.1808
-3.1141 -0.1244 -0.6811 0.9891
-3.2008 0.0024 -1.2901 0.9748
-1.0777 1.1438 0.1996 0.0139
-2.7213 -0.1909 0.1184 0.1013
-1.1467 1.3820 0.1427 -0.2239
1.1497 1.9414 -0.3035 0.3464
2.6993 -2.2556 0.1637 -0.0139
-3.0311 0.1417 0.0888 0.1791
-2.8403 -0.1809 -0.0965 0.0817
1.0118 2.0372 0.1638 -0.0349
-0.8968 1.0260 -0.1013 0.2369
1.1112 1.8802 -0.0291 -0.1506
1.1907 2.2041 -0.1060 0.2167
-1.0114 0.8029 -0.1317 0.0153
-3.1715 0.1041 -0.3338 0.0321
0.9718 1.9634 0.0305 -0.3259
-1.0377 0.8889 -0.2834 0.2301
-0.8989 1.0185 -0.0289 0.0213
-2.9815 -0.4798 0.2245 0.3085
-0.8576 0.9231 -0.2752 -0.0091
-3.1356 0.0026 -1.2138 0.7733
3.4470 -2.2418 0.2014 -0.1556
2.9143 -1.7951 0.1992 -0.2146
3.4961 -2.4969 -0.0121 0.1315
-2.9341 -0.1071 -0.7712 0.8911
-2.8105 -0.0884 -0.0287 -0.1279
3.1006 -2.0677 -0.2002 -0.1303
0.8209 2.1724 0.1548 0.3516
-2.8500 0.3196 0.1359 -0.1179
-2.8679 0.1365 -0.5702 0.7626
-2.8245 -0.1312 0.0881 -0.1305
-0.8322 1.3014 -0.3837 0.2400
-2.6063 0.1431 0.1880 0.0487
-3.1341 -0.0854 -0.0359 -0.2080
0.6893 2.0854 -0.3250 -0.1007
1.0894 1.7271 -0.0176 0.6553
-2.9851 -0.0113 0.0666 -0.0802
1.0371 2.2724 0.1044 0.3982
-2.8032 -0.2737 -0.7391 1.0277
-2.6856 0.0619 -1.1066 1.0485
-2.9445 -0.1602 -0.0019 0.0093
1.2004 2.1302 -0.1650 0.3413
3.2505 -1.9279 0.4462 -0.2405
-1.2080 0.8222 0.1671 0.1576
-2.8274 0.1515 -0.9636 1.0675
2.8190 -1.8626 0.2702 0.0026
1.0507 1.7776 -0.1421 0.0999
-2.8946 0.1446 -0.1645 0.3071
-1.0105 1.0973 0.0241 0.1628
-2.9138 -0.3404 0.0627 0.1286
-3.0646 -0.0008 0.3819 -0.1541
1.2531 1.9830 -0.0774 0.2413
1.1486 2.0440 -0.0582 -0.0650
-3.1401 -0.1447 -0.6580 0.9562
-2.9591 0.1598 -0.6581 1.1937
-2.9219 -0.3637 -0.1538 -0.2085
2.8948 -2.2745 0.2332 -0.0312
-3.2972 -0.0219 -0.0288 -0.1436
-1.2737 0.7648 0.0643 0.0858
-1.0690 0.8108 -0.2723 0.3231
-0.5908 0.7508 -0.5456 0.0190
0.5808 2.0573 -0.1658 0.1709
2.8227 -2.2461 0.2255 -0.3684
0.6174 1.7654 -0.3999 0.4125
3.2587 -1.9310 0.2021 0.0800
1.0999 1.8852 -0.0475 -0.0585
-2.7395 0.2585 -0.84
41 0.9987
-1.2223 1.0542 -0.2480 -0.2795
-2.9212 -0.0605 -0.0259 0.2591
3.1598 -2.2631 0.1746 0.1485
0.8476 1.8760 -0.2894 -0.0354
2.9205 -2.2418 0.4137 -0.2499
2.7656 -2.1768 0.0719 -0.1848
-0.8698 1.0249 -0.2084 -0.0008
-1.1444 0.7787 -0.4958 0.3676
-1.0711 1.0450 -0.0477 -0.4030
0.5350 1.8110 -0.0377 0.1622
0.9076 1.8845 -0.1121 0.5700
-2.7887 -0.2119 0.0566 0.0120
-1.2567 0.9274 0.1104 0.1581
-2.9946 -0.2086 -0.8169 0.6662
1.0536 1.9818 -0.0631 0.2581
-2.8465 -0.2222 0.2745 0.1997
-2.8516 0.1649 -0.7566 0.8616
-3.2470 0.0770 0.1173 -0.1092
-2.9322 -0.0631 -0.0062 -0.0511
-2.7919 0.0438 -0.1935 -0.5023
0.9894 1.9475 -0.0146 -0.0390
-2.9659 -0.1300 0.1144 0.3410
-2.7322 -0.0427 -1.0758 0.9718
-1.4852 0.8592 -0.0503 -0.1373
2.8845 -2.1465 -0.0533 -0.1044
-3.1470 0.0536 0.1073 0.3323
2.9423 -2.1572 0.0505 0.1180
-3.0683 0.3434 -0.6563 0.8960
1.3215 2.0951 -0.1557 0.3994
-0.7681 1.2075 -0.2781 0.2372
-0.6964 1.2360 -0.3342 0.1662
-0.6382 0.8204 -0.2587 0.3344
-3.0233 -0.1496 -0.2607 -0.0400
-0.8952 0.9872 0.0019 0.3138
-0.8172 0.6814 -0.0691 0.1009
-3.3032 0.0571 -0.0243 -0.1405
0.7810 1.9013 -0.3996 0.7374
-0.9030 0.8646 -0.1498 0.1112
-0.8461 0.9261 -0.1295 -0.0727
2.8182 -2.0818 -0.1430 -0.0547
2.9295 -2.3846 -0.0244 -0.1400
1.0587 2.2227 -0.1250 0.0957
3.0755 -1.7365 -0.0511 0.1500
-1.3076 0.8791 -0.3720 0.0331
-2.8252 -0.0366 -0.6790 0.7374
-2.6551 -0.1875 0.3222 0.0483
-2.9659 -0.1585 0.4013 -0.1402
-3.2859 -0.1546 0.0104 -0.1781
-0.6679 1.1999 0.1396 -0.3195
-1.0205 1.2226 0.1850 0.0050
-3.0091 -0.0186 -0.9111 0.9663
-3.0339 0.1377 -0.9662 1.0664
0.8952 1.9594 -0.3221 0.3579
-2.8481 0.1963 -0.1428 0.0382
1.0796 2.1353 -0.0792 0.6491
-0.8732 0.8985 -0.0049 0.0068
1.0620 2.1478 -0.1275 0.3553
3.4509 -1.9975 0.1285 -0.1575
-3.2280 -0.0640 -1.1513 0.8235
-0.6654 0.9402 0.0577 -0.0175
-3.2100 0.2762 -0.1053 0.0626
3.0793 -2.0043 0.2948 0.0411
1.3596 1.9481 -0.0167 0.3958
-3.1267 0.1801 0.2228 0.1179
-0.7979 0.9892 -0.2673 0.4734
2.5580 -1.7623 -0.1049 -0.0521
-0.9172 1.0621 -0.0826 0.1501
-0.7817 1.1658 0.1922 0.0803
3.1747 -2.1442 0.1472 -0.3411
2.8476 -1.8056 -0.0680 0.1536
-0.6175 1.4349 -0.1970 -0.1085
0.7308 1.9656 0.2602 0.2801
-1.0310 1.0553 -0.2928 -0.1647
-2.9251 -0.2095 0.0582 -0.1813
-0.9827 1.2720 -0.2225 0.2563
-1.0830 1.1158 -0.0405 -0.1181
-2.8744 0.0195 -0.3811 0.1455
3.1663 -1.9241 0.0455 0.1684
-1.0734 0.7681 -0.4725 -0.1976];
[n,d] = size(x);
bn=round(n/k*rand);%第一个随机数在前1/K的范围内
nc=[x(bn,:);x(2*bn,:);x(3*bn,:);x(4*bn,:)];%初始聚类中心
[cid,nr,centers] = kmeans(x,k,nc)%调用kmeans函数
for i=1:150,
if cid(i)==1,
plot(x(i,1),x(i,2),'r*') % 显示第一类
hold on
else
if cid(i)==2,
plot(x(i,1),x(i,2),'b*') %显示第二类
hold on
else
if cid(i)==3,
plot(x(i,1),x(i,2),'g*') %显示第三类
hold on
else
if cid(i)==4,
plot(x(i,1),x(i,2),'k*') %显示第四类
hold on
end
end
end
end
end
strt=['红色*为第一类；蓝色*为第二类；绿色*为第三类；黑色*为第四类' ];
text(-4,-3.6,strt);
%BasicKMeans.m主类
function [cid,nr,centers] = kmeans(x,k,nc)
[n,d] = size(x);
% 设置cid为分类结果显示矩阵
cid = zeros(1,n);
% Make this different to get the loop started.
oldcid = ones(1,n);
% The number in each cluster.
nr = zeros(1,k);
% Set up maximum number of iterations.
maxgn= 100;
iter = 1;
while iter < maxgn
%计算每个数据到聚类中心的距离
for i = 1:n
dist = sum((repmat(x(i,:),k,1)-nc).^2,2);
[m,ind] = min(dist); % 将当前聚类结果存入cid中
cid(i) = ind;
end
for i = 1:k
%找到每一类的所有数据，计算他们的平均值，作为下次计算的聚类中心
ind = find(cid==i);
nc(i,:) = mean(x(ind,:));
% 统计每一类的数据个数
nr(i) = length(ind);
end
iter = iter + 1;
end
% Now check each observation to see if the error can be minimized some more.
% Loop through all points.
maxiter = 2;
iter = 1;
move = 1;
while iter < maxiter & move ~= 0
move = 0;
% 对所有的数据进行再次判断，寻求最佳聚类结果
for i = 1:n
dist = sum((repmat(x(i,:),k,1)-nc).^2,2);
r = cid(i); % 将当前数据属于的类给r
dadj = nr./(nr+1).*dist'; % 计算调整后的距离
[m,ind] = min(dadj); % 早到该数据距哪个聚类中心最近
if ind ~= r % 如果不等则聚类中心移动
cid(i) = ind;%将新的聚类结果送给cid
ic = find(cid == ind);%重新计算调整当前类别的聚类中心
nc(ind,:) = mean(x(ic,:));
move = 1;
end
end
iter = iter+1;
end
centers = nc;
if move == 0
disp('No points were moved after the initial clustering procedure.')
else
disp('Some points were moved after the initial clustering procedure.')
end