k均值聚类坐标c语言,K-Means聚类算法的实现（C语言）
最近做了聚类实验，就写了下K-Means算法，C语言实现.
实验给出的数据集比较小,总共有11个：(2, 10), (2, 5), (8, 4), (5, 8), (7, 5), (6, 4), (1, 2), (4, 9), (7, 3), (1, 3), (3, 9)
代码运行的聚类结果：
Cluster-1： (2, 10), (5, 8), (4, 9), (3, 9)
Cluster-2： (8, 4), (7, 5), (6, 4), (7, 3)
Cluster-3： (2, 5), (1, 2), (1, 3)
Clementine软件运行结果：
可以看出与软件运行结果相比代码是正确的.
Code：
#include
#include
#include
#include
#include
#define N 11
#define K 3
typedef struct
{
float x;
float y;
}Point;
int center[N]; /// 判断每个点属于哪个簇
Point point[N] = {
{2.0, 10.0},
{2.0, 5.0},
{8.0, 4.0},
{5.0, 8.0},
{7.0, 5.0},
{6.0, 4.0},
{1.0, 2.0},
{4.0, 9.0},
{7.0, 3.0},
{1.0, 3.0},
{3.0, 9.0}
};
Point mean[K]; /// 保存每个簇的中心点
float getDistance(Point point1, Point point2)
{
float d;
d = sqrt((point1.x - point2.x) * (point1.x - point2.x) + (point1.y - point2.y) * (point1.y - point2.y));
return d;
}
/// 计算每个簇的中心点
void getMean(int center[N])
{
Point tep;
int i, j, count = 0;
for(i = 0; i < K; ++i)
{
count = 0;
tep.x = 0.0; /// 每算出一个簇的中心点值后清0
tep.y = 0.0;
for(j = 0; j < N; ++j)
{
if(i == center[j])
{
count++;
tep.x += point[j].x;
tep.y += point[j].y;
}
}
tep.x /= count;
tep.y /= count;
mean[i] = tep;
}
for(i = 0; i < K; ++i)
{
printf("The new center point of %d is : \t( %f, %f )\n", i+1, mean[i].x, mean[i].y);
}
}
/// 计算平方误差函数
float getE()
{
int i, j;
float cnt = 0.0, sum = 0.0;
for(i = 0; i < K; ++i)
{
for(j = 0; j < N; ++j)
{
if(i == center[j])
{
cnt = (point[j].x - mean[i].x) * (point[j].x - mean[i].x) + (point[j].y - mean[i].y) * (point[j].y - mean[i].y);
sum += cnt;
}
}
}
return sum;
}
/// 把N个点聚类
void cluster()
{
int i, j, q;
float min;
float distance[N][K];
for(i = 0; i < N; ++i)
{
min = 999999.0;
for(j = 0; j < K; ++j)
{
distance[i][j] = getDistance(point[i], mean[j]);
/// printf("%f\n", distance[i][j]); /// 可以用来测试对于每个点与3个中心点之间的距离
}
for(q = 0; q < K; ++q)
{
if(distance[i][q] < min)
{
min = distance[i][q];
center[i] = q;
}
}
printf("( %.0f, %.0f )\t in cluster-%d\n", point[i].x, point[i].y, center[i] + 1);
}
printf("-----------------------------\n");
}
int main()
{
int i, j, n = 0;
float temp1;
float temp2, t;
printf("----------Data sets----------\n");
for(i = 0; i < N; ++i)
{
printf("\t( %.0f, %.0f )\n", point[i].x, point[i].y);
}
printf("-----------------------------\n");
/*
可以选择当前时间为随机数
srand((unsigned int)time(NULL));
for(i = 0; i < K; ++i)
{
j = rand() % K;
mean[i].x = point[j].x;
mean[i].y = point[j].y;
}
*/
mean[0].x = point[0].x; /// 初始化k个中心点
mean[0].y = point[0].y;
mean[1].x = point[3].x;
mean[1].y = point[3].y;
mean[2].x = point[6].x;
mean[2].y = point[6].y;
cluster(); /// 第一次根据预设的k个点进行聚类
temp1 = getE(); /// 第一次平方误差
n++; /// n计算形成最终的簇用了多少次
printf("The E1 is: %f\n\n", temp1);
getMean(center);
cluster();
temp2 = getE(); /// 根据簇形成新的中心点，并计算出平方误差
n++;
printf("The E2 is: %f\n\n", temp2);
while(fabs(temp2 - temp1) != 0) /// 比较两次平方误差 判断是否相等，不相等继续迭代
{
temp1 = temp2;
getMean(center);
cluster();
temp2 = getE();
n++;
printf("The E%d is: %f\n", n, temp2);
}
printf("The total number of cluster is: %d\n\n", n); /// 统计出迭代次数
system("pause");
return 0;
}