K-均值聚类算法实验报告-模式识别-C.doc

040930520 吴非 模式识别实验报告K-均值聚类算法实验报告试验目的通过对K-均值算法的编程实现，加强对该算法的理解和认识。提高自身的知识水平和编程能力，认识模式识别在生活中的应用。算法思想K-均值算法的主要思想是先在需要分类的数据中寻找K组数据作为初始聚类中心，然后计算其他数据距离这三个聚类中心的距离，将数据归入与其距离最近的聚类中心，之后再对这K个聚类的数据计算均值，作为新的聚类中心，继续以上步骤，直到新的聚类中心与上一次的聚类中心值相等时结束算法。子函数int judge(float i,float j,float k)judge用来判断元素属于哪个聚类，返回元素所在的聚类值（1,2，3）.算法流程图开 始读入要分类的数据设置初始聚类中心计算数据到K个聚类中心的距离将数据分入与其距离最小的聚类计算新的聚类中心聚类中心是否收敛？否输出K个分类好的聚类结 束是实验代码#include#include#include#define COUNT 150 /数据个数struct data float dx;float dy;float dz;float dm;int symbol;/标号data150,Zdata3,Cz3;int time=0;/记录迭代次数int judge(float i,float j,float k)/判断数据属于哪个分类if(i=j)&(i=k)return 1; else if(ji)&(j=k)return 2; elsereturn 3;void main()int z1=0,z2=0,z3=0;/ 存放每个聚类的元素个数 float a=0,b=0,c=0,d=0,e3,t12; float sum=1; int count; fstream outputFile; outputFile.open(Iris.txt,ios:in); /cout文件中的数据如下endl; if(!outputFile) coutCant open the File!endl; exit(0); for(count=0;countdatacount.dx; outputFiledatacount.dy; outputFiledatacount.dz; outputFiledatacount.dm; datacount.symbol=0; /* for(count=0;countCOUNT;count+)/输出文件中的数据 cout.setf(ios:showpoint); coutdatacount.dx ; coutdatacount.dy ; coutdatacount.dz ; coutdatacount.dm ; coutendl; */ outputFile.close(); for(count=0;count3;count+)/初始化聚类 Zdatacount.dx=datacount.dx; Zdatacount.dy=datacount.dy; Zdatacount.dz=datacount.dz; Zdatacount.dm=datacount.dm; Zdatacount.symbol=datacount.symbol; do Cz0.dx=0;/初始化Cz0.dy=0;Cz0.dz=0;Cz0.dm=0;Cz1.dx=0;Cz1.dy=0;Cz1.dz=0;Cz1.dm=0;Cz2.dx=0;Cz2.dy=0;Cz2.dz=0;Cz2.dm=0; for(count=0;countCOUNT;count+)/判断每个元素属于哪个聚类a=(Zdata0.dx-datacount.dx);b=(Zdata0.dy-datacount.dy);c=(Zdata0.dz-datacount.dz);d=(Zdata0.dm-datacount.dm);e0=a*a+b*b+c*c+d*d;a=(Zdata1.dx-datacount.dx);b=(Zdata1.dy-datacount.dy);c=(Zdata1.dz-datacount.dz);d=(Zdata1.dm-datacount.dm);e1=a*a+b*b+c*c+d*d;a=(Zdata2.dx-datacount.dx);b=(Zdata2.dy-datacount.dy);c=(Zdata2.dz-datacount.dz);d=(Zdata2.dm-datacount.dm);e2=a*a+b*b+c*c+d*d;datacount.symbol=judge(e0,e1,e2);/coutdatacount.symbol;z1=0;z2=0;z3=0; for(count=0;countCOUNT;count+)/计算每个聚类的元素个数 if(datacount.symbol=1) Cz0.dx+=datacount.dx; Cz0.dy+=datacount.dy; Cz0.dz+=datacount.dz; Cz0.dm+=datacount.dm; z1+; else if(datacount.symbol=2) Cz1.dx+=datacount.dx; Cz1.dy+=datacount.dy; Cz1.dz+=datacount.dz; Cz1.dm+=datacount.dm; z2+; else Cz2.dx+=datacount.dx; Cz2.dy+=datacount.dy; Cz2.dz+=datacount.dz; Cz2.dm+=datacount.dm; z3+; /coutz1 z2 z3 endl;/计算新的聚类中心 Cz0.dx=Cz0.dx/z1;Cz0.dy=Cz0.dy/z1;Cz0.dz=Cz0.dz/z1;Cz0.dm=Cz0.dm/z1;Cz1.dx=Cz1.dx/z2;Cz1.dy=Cz1.dy/z2;Cz1.dz=Cz1.dz/z2;Cz1.dm=Cz1.dm/z2;Cz2.dx=Cz2.dx/z3;Cz2.dy=Cz2.dy/z3;Cz2.dz=Cz2.dz/z3;Cz2.dm=Cz2.dm/z3;+time;sum=0;t0=(Zdata0.dx-Cz0.dx)*(Zdata0.dx-Cz0.dx);t1=(Zdata0.dy-Cz0.dy)*(Zdata0.dy-Cz0.dy);t2=(Zdata0.dz-Cz0.dz)*(Zdata0.dz-Cz0.dz);t3=(Zdata0.dm-Cz0.dm)*(Zdata0.dm-Cz0.dm); t4=(Zdata1.dx-Cz1.dx)*(Zdata1.dx-Cz1.dx);t5=(Zdata1.dy-Cz1.dy)*(Zdata1.dy-Cz1.dy);t6=(Zdata1.dz-Cz1.dz)*(Zdata1.dz-Cz1.dz);t7=(Zdata1.dm-Cz1.dm)*(Zdata1.dm-Cz1.dm);t8=(Zdata2.dx-Cz2.dx)*(Zdata2.dx-Cz2.dx);t9=(Zdata2.dy-Cz2.dy)*(Zdata2.dy-Cz2.dy);t10=(Zdata2.dz-Cz2.dz)*(Zdata2.dz-Cz2.dz);t11=(Zdata2.dm-Cz2.dm)*(Zdata2.dm-Cz2.dm);for(count=0;count12;count+)sum+=tcount;/coutendlsumendl;/迭代的判定sumZdata0.dx=Cz0.dx;Zdata0.dy=Cz0.dy;Zdata0.dz=Cz0.dz;Zdata0.dm=Cz0.dm;Zdata1.dx=Cz1.dx;Zdata1.dy=Cz1.dy;Zdata1.dz=Cz1.dz;Zdata1.dm=Cz1.dm;Zdata2.dx=Cz2.dx;Zdata2.dy=Cz2.dy;Zdata2.dz=Cz2.dz;Zdata2.dm=Cz2.dm; while(sum!=0);cout(下标从1到150)endl;cout分类成功！endl;cout*endl;for(count=0;countCOUNT;count+)if(datacount.symbol=1)coutcount+1 ;coutendl共z1个元素属于第一类endl;cout*endl;for(count=0;countCOUNT;count+)if(datacount.symbol=2)coutcount+1 ;coutendl共z2个元素属于第二类endl;cout*endl;for(count=0;countCOUNT;count+)if(datacount.symbol=3)coutcount+1 ;coutendl共z3个元素属于第三类endl;cout*endl;coutendl迭代次数为 time 次endlendl; 实验过程执行程序结果如下令前三个数据作为初始聚类中心以第2,3,4个数据作为初始聚类中心以第100,101,102组数据为初始聚类中心发现聚类的结果基本一样，只是有很少数的数据的聚类会发生变化（如第51个数据