MATLAB統(tǒng)計(jì)工具箱數(shù)值模擬分析

1、MATLAB 統(tǒng)計(jì)工具箱數(shù)值模擬分析3.1 模擬數(shù)據(jù)用 mntlab 產(chǎn)生一組隨機(jī)數(shù)，共 300 個(gè)，非別是完全可分離數(shù)據(jù)、重疊度較低數(shù)據(jù)和重疊度較高數(shù)據(jù)，并且畫出圖形。程序1rngefault;% ForreproucibilityXI=rann(100,2)+0*ones(100,1),m*ones(X2= rann( 100,2) +0*ones(100J), n*ones(100,l); X3=rann( 100,2) +0*ones(100J), p*ones( 100,1); ataX=Xl;X2;X3J;opts =statset(isplayVfinab);ix,C = kme

2、ans(X3/istancecityblockVReplicates5JOptionsopts); figure ;hol on plot(Xl(:,l),Xl(:,2)； plot(X2(:,l),X2(:,2),bo);plot(X3(:,l),X3(:,2)； r*1);title Ranomly Generateata;31完全可分離數(shù)據(jù)m15,n=&3161416141210*札*214-32-123418圖31完全可離數(shù)據(jù)散 點(diǎn)圖3 2 重疊度較低的數(shù)據(jù)1,m=15,n=10,p=5r 3.2100%100%oo24-10218161412屯弄。號(hào)匚3.2 重疊度較低的數(shù)據(jù)散點(diǎn)圖3

3、.1.3 重疊度較高的數(shù)據(jù)調(diào)用【程序 1】，取 m=15,n=12,p=9散點(diǎn)圖如圖 3.3 所示。Ranomly Gen erate ata18r1614-12OO10-89 -4-3-2-10123圖33重疊度較高的數(shù)據(jù)散點(diǎn)圖3. 2聚類分析完全可分離數(shù)據(jù)程序 2rng efault; % For reproucibilityXI=rann(100,2)+0*ones(l00J),m*ones(X2=rann(100,2)+0*ones(100J),n*ones(100,1); X3=rann(100,2)+0*ones(100,l),p*ones(100,1); ataX=Xl;X2;X

4、3;opts =statset(isplay7finar);ix.C = kmeans(X,3/istance7cityblockReplicates5/Optionsopts); figure ; plot(X(:,l),X(:,2),T);title Ranomly Generate at1 P=ix;a=0;b=0;c=0;fori=l:1:300ifp(i)=la=a+1; elseif P(i)=2;b=b+l; else c=c+l; enen ab cfigure ;plot(X(ix= 1,1 ),X(ix 二二 1,2)/r/MarkerSize 12)holonplot(X

5、(ix=2,l ),X(ix=2,2)/boMarkerSizel holonplot(X(ix=3,l ),X(ix=3,2)/r*7MarkerSizel 2) plot(C(:J ),C(:,2)/kx.A MarkerSizeJ；LineWith3)legen(Ckisterr/Cluster 3Centrois.1614Cluster1614Cluster1Cluster2Cluster3Centrois12 1086 420-23- 10- 2123.4完全可分離titleClusterAssignmentsanCentroishol off取m=15,n=&聚類效果圖如圖34所示

6、:Cluster Assignments an Centrois18數(shù)據(jù)聚類效果圖Cluster1=100Cluster2=100Cluster3=100重疊度較低的數(shù)據(jù)2】，取m=15,210,p=5;聚類效果圖如圖35所示:Cluster A33ignments an Centrois3.5 重疊度較低數(shù)據(jù)聚類效果圖Cluster 1 =993.2.3Cluster2= 100Cluster3= 10118161816Cluster1Cluster2Cluster 3 Centrois半14o12310O8621413Cluster 2Cluster 3 Conuois12米1110987

7、*ooo米6調(diào)用【程序2】，取m=12,n=10,p=8;聚類效果圖如圖36所示:Cluster Assignments an Centrois15Cluster 1-3-2-101233.6 重疊度較髙數(shù)據(jù)聚類效果圖Cluster I =95Ckister2=IOlCluster3=1043. 33. 3.1 錯(cuò)誤率計(jì)算方法若聚類正確，則記代二 0,1, 9,聚類的錯(cuò)誤率驢3.3.2 模擬數(shù)據(jù)錯(cuò)誤率分析3.1 完全可分離數(shù)據(jù)錯(cuò)誤率分析聚類前聚類后錯(cuò)誤率1001000.001001000.001001000.00Cluster 1 Cluster2Cluster3奕數(shù)點(diǎn)數(shù)Cluster Cluster 1 Cluster2Cluster3奕數(shù)點(diǎn)數(shù)Cluster Cluster2Cluster3聚類前聚類后錯(cuò)誤率100990.011001000.001001010.01聚類前聚類后錯(cuò)誤率3.2聚類前聚類后錯(cuò)誤率類數(shù)點(diǎn)數(shù)Cluster 1100950.05Cluster21001010.01Cluster31()()1040.04k-means 關(guān)于重疊度較高的數(shù)據(jù)進(jìn)行聚類的結(jié)果并且不是特別理想，但正如本文前面提到的那個(gè)正態(tài)分布的例子，在聚類的進(jìn)程中，出 現(xiàn)了部分樣本點(diǎn)的重合，