基于MATLAB的KMEANS聚类程序Word文档格式.docx

1、 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 0.2014-0.155

2、6 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 0.18

3、80 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 0.8

4、222 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.

5、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

6、.0800 1.0999 1.8852-0.0475-0.0585-2.7395 0.2585-0.8441 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 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-

7、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 0.1173-0.1092-2.9322-0.0062-0.0511-2.7919 0.0438-0.1935

8、-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 1.236

9、0-0.3342 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 1.05

10、87 2.2227-0.1250 0.0957 3.0755-1.7365-0.0511 0.1500-1.3076 0.8791-0.3720 0.0331-2.8252-0.0366-0.6790-2.6551-0.1875 0.3222 0.0483-0.1585 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 1.9

11、594-0.3221 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.

12、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

13、.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

14、（i）=1,plot（x（i,1）,x（i,2）,r*） % 显示第一类hold on elseif cid（i）=2,b*） %显示第二类elseif cid（i）=3, plot（x（i,1）,x（i,2）,g*） %显示第三类 hold onif cid（i）=4,k*） %显示第四类end endstrt=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）;函数如下:%BasicKMeans.m主类 function cid,nr,centers = kmeans（x,k,nc）% 设置cid为分类结果显示矩阵cid = zeros（

15、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:ndist = 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

16、;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）;iter = iter + 1;% Now check each observation to see if the error can be minimized some more. % Loop through all points.maxiter = 2;move = 1; 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

17、r = cid（i）;% 将当前数据属于的类给rdadj = 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%将新的聚类结果送给cidic = find（cid = ind）;%重新计算调整当前类别的聚类中心nc（ind,:） = mean（x（ic,:iter = iter+1;centers = nc;if move = 0disp（No points were moved after the initial clustering procedure.）Some points were moved after the initial clustering procedure.end 分类效果图14_7774_ecd62647e667583.jpg