首先画出k_means算法的流程图:

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" alt="" width="709" height="752" />

具体代码,C++实现:

#include <iostream>
#include <fstream>
#include <cmath>
#include <ctime>
using namespace std; const int N = ; typedef struct {
double x1,x2;
int flag;
}oneNode; oneNode node[N];
int k,n; void DataInput();
void SetInitial(oneNode *p,oneNode *q);
void SetQzero(oneNode &qi);
bool GetExist(int xm,int *sb,int i);
void DataIteration(oneNode *p,oneNode *q,double &E1,double &E3);
double GetRange(oneNode &node1,oneNode &node2);
bool JudgeCondition(oneNode *p,oneNode *q);
void DataOutput(oneNode *p,double E1,double E3); int main(){
int E2=;
double E1 = ,E3;
n = ; DataInput(); //将txt文件中记录读入,存储到oneNode列表中 cout<<"请输入新的聚类数目:";
cin>>k;
oneNode *p = new oneNode[k];
oneNode *q = new oneNode[k]; SetInitial(p,q); //初始化开始的k个聚类中心 DataIteration(p,q,E1,E3); //循环处理每条记录,获得新的聚类中心 DataOutput(p,E1,E3); //输出程序运行结果 delete[] q;
delete[] p;
return ;
} void DataInput(){
ifstream ifs; //通过txt文件,读入所有记录,将记录存储到oneNode列表中
ifs.open("D:\k_means\\aa11.txt");
if (!ifs.is_open())
{
cout<<"文件读取失败"<<endl;
exit();
} while (!ifs.eof()) //将txt文件中记录,存储到node[]中
{
ifs>>node[n].x1;
ifs>>node[n].x2;
n++;
}
} void SetInitial(oneNode *p,oneNode *q){
int xm;
int *sb = new int[k];
srand((unsigned)time());
for (int i = ;i < k;i++)
{
do
{
xm = rand()%n;
} while (GetExist(xm,sb,i));
sb[i] = xm; p[i] = node[xm]; SetQzero(q[i]);
}
delete[] sb;
} void SetQzero(oneNode &qi){
qi.x1 = ;
qi.x2 = ;
qi.flag = ;
} bool GetExist(int xm,int *sb,int i){
bool b = false;
for (int j = ;j < i;j++)
{
if (sb[j] == xm)
{
b = true;
break;
}
}
return b;
} void DataIteration(oneNode *p,oneNode *q,double &E1,double &E3){
int i,j,E2 = ;
bool b;
do
{
E3 = ;
for(i = ;i < n;i++){
double mm = GetRange(node[i],p[]);
int m = ;
for (j = ;j < k;j++)
{
double nn = GetRange(node[i],p[j]);
if (mm > nn)
{
mm = nn;
m = j;
}
}
node[i].flag = m;
E3 += GetRange(node[i],p[m]);
q[m].x1 += node[i].x1;
q[m].x2 += node[i].x2;
q[m].flag += ;
}
if (E2 == )
{
E1 = E3;
}else if(E2 > ){
if (E1 <= E3)
{
break;
}else{
E1 = E3;
}
}
E2++;
for (i = ;i < k;i++)
{
if (q[i].flag != )
{
q[i].x1 = q[i].x1/q[i].flag;
q[i].x2 = q[i].x2/q[i].flag;
}
}
b = JudgeCondition(p,q);
for (i = ;i < k;i++)
{
p[i] = q[i]; SetQzero(q[i]);
}
} while (b); } double GetRange(oneNode &node1,oneNode &node2){
return pow(node1.x1-node2.x1,)+pow(node1.x2-node2.x2,);
} bool JudgeCondition(oneNode *p,oneNode *q){
bool bo = false;
for (int i = ;i < k;i++)
{
if (pow(p[i].x1-q[i].x1,)+pow(p[i].x2-q[i].x2,) >= 5.0)
{
bo = true;
break;
} }
return bo;
} void DataOutput(oneNode *p,double E1,double E3){
if (E1 != E3){
cout<<"操作失败"<<endl;
}else{
cout<<"本次总体误差是:"<<E1<<endl;
ofstream ofile("D://k_means/aa11result.txt");
for (int i = ;i < k;i++)
{
ofile<<"第"<<i+<<"个聚类中心是:"<<"("<<p[i].x1<<","<<p[i].x2<<")"<<endl<<"属于第"<<i+<<"个聚类的所有点是:"<<endl;
for (int j = ;j < n;j++)
{
if (node[j].flag == i)
{
ofile<<node[j].x1<<" "<<node[j].x2<<endl;
}
}
} }
}

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