本人以前主要focus在传统音频的软件开发，接触到的算法主要是音频信号处理相关的，如各种编解码算法和回声消除算法等。最近切到语音识别上，接触到的算法就变成了各种机器学习算法，如GMM等。K-means作为其中比较简单的一种肯定是要好好掌握的。今天就讲讲K-means的基本原理和代码实现。其中基本原理简述（主要是因为:1,K-means比较简单；2,网上有很多讲K-means基本原理的），重点放在代码实现上。
1, K-means基本原理
K均值（K-means）聚类算法是无监督聚类（聚类（clustering）是将数据集中的样本划分为若干个通常是不相交的子集，每个子集称为一个“簇（cluster）”）算法中的一种，也是最常用的聚类算法。K表示类别数，Means表示均值。K-means主要思想是在给定K值和若干样本（点）的情况下，把每个样本（点）分到离其最近的类簇中心点所代表的类簇中，所有点分配完毕之后，根据一个类簇内的所有点重新计算该类簇的中心点(取平均值)，然后再迭代的进行分配点和更新类簇中心点的步骤，直至类簇中心点的变化很小，或者达到指定的迭代次数。
K-means算法流程如下：
（a）随机选取K个初始cluster center
（b）分别计算所有样本到这K个cluster center的距离
（c）如果样本离cluster center Ci最近，那么这个样本属于Ci点簇；如果到多个cluster center的距离相等，则可划分到任意簇中
（d）按距离对所有样本分完簇之后，计算每个簇的均值（最简单的方法就是求样本每个维度的平均值），作为新的cluster center
（e）重复（b）（c）（d）直到新的cluster center和上轮cluster center变化很小或者达到指定的迭代次数，算法结束
2, 算法实现
我主要偏底层开发，最熟悉语言是C，所以代码是用C语言来实现的。在二维平面上有一些点，大意如下图，
用K-means算法对其分类，其中类的个数（即K值）和点的个数人为指定。具体的代码如下：
#include<stdio.h>
#include<stdlib.h>
#include<string.h>
#include<math.h>
#define MAX_ROUNDS 100 //最大允许的聚类次数
//“点”的结构体
typedef struct Point{
int x_value; //用于存放点在X轴上的值
int y_value; //用于存放点在Y轴上的值
int cluster_id; //用于存放该点所属的cluster id
}Point;
Point* data;
//cluster center的结构体
typedef struct ClusterCenter{
double x_value;
double y_value;
int cluster_id;
}ClusterCenter;
ClusterCenter* cluster_center;
//计算cluster center的结构体
typedef struct CenterCalc{
double x_value;
double y_value;
}CenterCalc;
CenterCalc *center_calc;
int is_continue; //kmeans 运算是否继续
int* cluster_center_init_index; //记录每个cluster center最初用的是哪个“点”
double* distance_from_center; //记录一个“点”到所有cluster center的距离
int* data_size_per_cluster; //每个cluster点的个数
int data_size_total; //设定点的个数
char filename[200]; //要读取的点的数据的文件名
int cluster_count; //设定的cluster的个数
void memoryAlloc();
void memoryFree();
void readDataFromFile();
void initialCluster();
void calcDistance2OneCenter(int pointID, int centerID);
void calcDistance2AllCenters(int pointID);
void partition4OnePoint(int pointID);
void partition4AllPointOneCluster();
void calcClusterCenter();
void kmeans();
void compareNewOldClusterCenter(CenterCalc* center_calc);
int main(int argc, char* argv[])
{
if( argc != 4 )
{
printf("This application needs 3 parameters to run:"
"\n the 1st is the size of data set,"
"\n the 2nd is the file name that contains data"
"\n the 3rd indicates the cluster_count"
"\n");
exit(1);
}
data_size_total = atoi(argv[1]);
strcat(filename, argv[2]);
cluster_count = atoi(argv[3]);
//1, memory alloc
memoryAlloc();
//2, read point data from file
readDataFromFile();
//3, initial cluster
initialCluster();
//4, run k-means
kmeans();
//5, memory free & end
memoryFree();
return 0;
}
void memoryAlloc()
{
data = (Point*)malloc(sizeof(struct Point) * (data_size_total));
if( !data )
{
printf("malloc error:data!");
exit(1);
}
cluster_center_init_index = (int*)malloc(sizeof(int) * (cluster_count));
if( !cluster_center_init_index )
{
printf("malloc error:cluster_center!\n");
exit(1);
}
distance_from_center = (double*)malloc(sizeof(double) * (cluster_count));
if( !distance_from_center )
{
printf("malloc error: distance_from_center!\n");
exit(1);
}
cluster_center = (ClusterCenter*)malloc(sizeof(struct ClusterCenter) * (cluster_count));
if( !cluster_center )
{
printf("malloc cluster center new error!\n");
exit(1);
}
center_calc = (CenterCalc*)malloc(sizeof(CenterCalc) * cluster_count);
if( !center_calc )
{
printf("malloc error: center_calc!\n");
exit(1);
}
data_size_per_cluster = (int*)malloc(sizeof(int) * (cluster_count));
if( !data_size_per_cluster )
{
printf("malloc error: data_size_per_cluster\n");
exit(1);
}
}
void memoryFree()
{
free(data);
data = NULL;
free(cluster_center_init_index);
cluster_center_init_index = NULL;
free(distance_from_center);
distance_from_center = NULL;
free(cluster_center);
cluster_center = NULL;
free(center_calc);
center_calc = NULL;
free(data_size_per_cluster);
data_size_per_cluster = NULL;
}
//从文件中读入每个点的x和y值
void readDataFromFile()
{
int i;
FILE* fread;
if( NULL == (fread = fopen(filename, "r")))
{
printf("open file(%s) error!\n", filename);
exit(1);
}
for( i = 0; i < data_size_total; i++ )
{
if( 2 != fscanf(fread, "%d %d ", &data[i].x_value, &data[i].y_value))
{
printf("fscanf error: %d\n", i);
}
data[i].cluster_id = -1; //初始时每个点所属的cluster id均置为-1
printf("After reading, point index:%d, X:%d, Y:%d, cluster_id:%d\n", i, data[i].x_value, data[i].y_value, data[i].cluster_id);
}
}
//根据传入的cluster_count来随机的选择一个点作为 一个cluster的center
void initialCluster()
{
int i,j;
int random;
//产生初始化的cluster_count个聚类
for( i = 0; i < cluster_count; i++ )
{
cluster_center_init_index[i] = -1;
}
//随机选择一个点作为每个cluster的center（不重复）
for( i = 0; i < cluster_count; i++ )
{
Reselect:
random = rand() % (data_size_total - 1);
for(j = 0; j < i; j++) {
if(random == cluster_center_init_index[j])
goto Reselect;
}
cluster_center_init_index[i] = random;
printf("cluster_id: %d, located in point index:%d\n", i, random);
}
//将随机选择的点作为center，同时这个点的cluster id也就确定了
for( i = 0; i < cluster_count; i++ )
{
cluster_center[i].x_value = data[cluster_center_init_index[i]].x_value;
cluster_center[i].y_value = data[cluster_center_init_index[i]].y_value;
cluster_center[i].cluster_id = i;
data[cluster_center_init_index[i]].cluster_id = i;
printf("cluster_id:%d, index:%d, x_value:%f, y_value:%f\n", cluster_center[i].cluster_id, cluster_center_init_index[i], cluster_center[i].x_value, cluster_center[i].y_value);
}
}
//计算一个点到一个cluster center的distance
void calcDistance2OneCenter(int point_id,int center_id)
{
distance_from_center[center_id] = sqrt( (data[point_id].x_value-cluster_center[center_id].x_value)*(double)(data[point_id].x_value-cluster_center[center_id].x_value) + (double)(data[point_id].y_value-cluster_center[center_id].y_value) * (data[point_id].y_value-cluster_center[center_id].y_value) );
}
//计算一个点到每个cluster center的distance
void calcDistance2AllCenters(int point_id)
{
int i;
for( i = 0; i < cluster_count; i++ )
{
calcDistance2OneCenter(point_id, i);
}
}
//确定一个点属于哪一个cluster center(取距离最小的)
void partition4OnePoint(int point_id)
{
int i;
int min_index = 0;
double min_value = distance_from_center[0];
for( i = 0; i < cluster_count; i++ )
{
if( distance_from_center[i] < min_value )
{
min_value = distance_from_center[i];
min_index = i;
}
}
data[point_id].cluster_id = cluster_center[min_index].cluster_id;
}
//在一轮的聚类中得到所有的point所属于的cluster center
void partition4AllPointOneCluster()
{
int i;
for( i = 0; i < data_size_total; i++ )
{
if( data[i].cluster_id != -1 ) //这个点就是center，不需要计算
continue;
else
{
calcDistance2AllCenters(i); //计算第i个点到所有center的distance
partition4OnePoint(i); //根据distance对第i个点进行partition
}
}
}
//重新计算新的cluster center
void calcClusterCenter()
{
int i;
memset(center_calc, 0, sizeof(CenterCalc) * cluster_count);
memset(data_size_per_cluster, 0, sizeof(int) * cluster_count);
//分别对每个cluster内的每个点的X和Y求和，并计每个cluster内点的个数
for( i = 0; i < data_size_total; i++ )
{
center_calc[data[i].cluster_id].x_value += data[i].x_value;
center_calc[data[i].cluster_id].y_value += data[i].y_value;
data_size_per_cluster[data[i].cluster_id]++;
}
//计算每个cluster内点的X和Y的均值作为center
for( i = 0; i < cluster_count; i++ )
{
if(data_size_per_cluster[i] != 0) {
center_calc[i].x_value = center_calc[i].x_value/ (double)(data_size_per_cluster[i]);
center_calc[i].y_value = center_calc[i].y_value/ (double)(data_size_per_cluster[i]); printf(" cluster %d point cnt:%d\n", i, data_size_per_cluster[i]);
printf(" cluster %d center: X:%f, Y:%f\n", i, center_calc[i].x_value, center_calc[i].y_value);
}
else
printf(" cluster %d count is zero\n", i);
}
//比较新的和旧的cluster center值的差别。如果是相等的，则停止K-means算法。
compareNewOldClusterCenter(center_calc);
//将新的cluster center的值放入cluster_center结构体中
for( i = 0; i < cluster_count; i++ )
{
cluster_center[i].x_value = center_calc[i].x_value;
cluster_center[i].y_value = center_calc[i].y_value;
cluster_center[i].cluster_id = i;
}
//在重新计算了新的cluster center之后，要重新来为每一个Point进行聚类，所以data中用于表示聚类ID的cluster_id要都重新置为-1。
for( i = 0; i < data_size_total; i++ )
{
data[i].cluster_id = -1;
}
}
//比较新旧的cluster center的值,完全一样表示聚类完成
void compareNewOldClusterCenter(CenterCalc* center_calc)
{
int i;
is_continue = 0; //等于0表示不要继续，1表示要继续
for( i = 0; i < cluster_count; i++ )
{
if( center_calc[i].x_value != cluster_center[i].x_value || center_calc[i].y_value != cluster_center[i].y_value)
{
is_continue = 1;
break;
}
}
}
//K-means算法
void kmeans()
{
int rounds;
for( rounds = 0; rounds < MAX_ROUNDS; rounds++ )
{
printf("\nRounds : %d \n", rounds+1);
partition4AllPointOneCluster();
calcClusterCenter();
if( 0 == is_continue )
{
printf("\n after %d rounds, the classification is ok and can stop.\n", rounds+1);
break;
}
}
}
编译后生成可执行文件kmeans，输入的文件里共有6个点，分别为（0, 0)， (4, 4)， (4, 5)， (0, 1)， (3, 6) ，(4, 9)，要求分成两类。运行可执行程序后得到结果如下：
$ ./kmeans 6 data 2
After reading, point index:0, X:0, Y:0, cluster_id:-1
After reading, point index:1, X:4, Y:4, cluster_id:-1
After reading, point index:2, X:4, Y:5, cluster_id:-1
After reading, point index:3, X:0, Y:1, cluster_id:-1
After reading, point index:4, X:3, Y:6, cluster_id:-1
After reading, point index:5, X:4, Y:9, cluster_id:-1 cluster_id: 0, located in point index:3
cluster_id: 1, located in point index:1
cluster_id:0, index:3, x_value:0.000000, y_value:1.000000
cluster_id:1, index:1, x_value:4.000000, y_value:4.000000
Rounds : 1
cluster 0 point cnt:2
cluster 0 center: X:0.000000, Y:0.500000
cluster 1 point cnt:4
cluster 1 center: X:3.750000, Y:6.000000
Rounds : 2
cluster 0 point cnt:2
cluster 0 center: X:0.000000, Y:0.500000
cluster 1 point cnt:4
cluster 1 center: X:3.750000, Y:6.000000
after 2 rounds, the classification is ok and can stop.即两轮后聚类就好了，（0, 0)，(0, 1)一类，(4, 4)， (4, 5)， (3, 6) ，(4, 9)一类。
本文章为转载内容，我们尊重原作者对文章享有的著作权。如有内容错误或侵权问题，欢迎原作者联系我们进行内容更正或删除文章。
