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C语言中K-means算法实现代码

K-means算法是很典型的基于距离的聚类算法，采用距离作为相似性的评价指标，即认为两个对象的距离越近，其相似度就越大。该算法认为簇是由距离靠近的对象组成的，因此把得到紧凑且独立的簇作为最终目标。

算法过程如下：

1）从N个样本随机选取K个样本作为质心
2）对剩余的每个样本测量其到每个质心的距离，并把它归到最近的质心的类
3）重新计算已经得到的各个类的质心
4）迭代2～3步直至新的质心与原质心相等或小于指定阈值，算法结束

#include<stdio.h> 
#include<stdlib.h> 
#include<string.h> 
#include<time.h> 
#include<math.h> 
 
#define DIMENSIOM  2    //目前只是处理2维的数据 
#define MAX_ROUND_TIME 100   //最大的聚类次数 
 
typedef struct Item{ 
  int dimension_1;    //用于存放第一维的数据 
  int dimension_2;    //用于存放第二维的数据 
  int clusterID;     //用于存放该item的cluster center是谁 
}Item; 
Item* data; 
 
typedef struct ClusterCenter{ 
  double dimension_1; 
  double dimension_2; 
  int clusterID; 
}ClusterCenter; 
ClusterCenter* cluster_center_new; 
 
int isContinue; 
 
int* cluster_center;    //记录center 
double* distanceFromCenter; //记录一个“点”到所有center的距离 
int data_size; 
char filename[200]; 
int cluster_count; 
 
void initial(); 
void readDataFromFile(); 
void initial_cluster(); 
void calculateDistance_ToOneCenter(int itemID, int centerID, int count); 
void calculateDistance_ToAllCenter(int itemID); 
void partition_forOneItem(int itemID); 
void partition_forAllItem_OneCluster(int round); 
void calculate_clusterCenter(int round); 
void K_means(); 
void writeClusterDataToFile(int round); 
void writeClusterCenterToFile(int round); 
void compareNew_OldClusterCenter(double* new_X_Y); 
void test_1(); 
 
int main(int argc, char* argv[]){ 
  if( argc != 4 ) 
  { 
    printf("This application need other parameter to run:" 
        "\n\t\tthe first is the size of data set," 
        "\n\t\tthe second is the file name that contain data" 
        "\n\t\tthe third indicate the cluster_count" 
        "\n"); 
    exit(0); 
  } 
  srand((unsigned)time(NULL)); 
  data_size = atoi(argv[1]); 
  strcat(filename, argv[2]); 
  cluster_count = atoi(argv[3]); 
 
  initial(); 
  readDataFromFile(); 
  initial_cluster(); 
  //test_1(); 
  //partition_forAllItem_OneCluster(); 
  //calculate_clusterCenter(); 
  K_means(); 
  return 0; 
} 
 
/* 
 * 对涉及到的二维动态数组根据main函数中传入的参数分配空间 
 * */ 
void initial(){ 
  data = (Item*)malloc(sizeof(struct Item) * (data_size + 1)); 
  if( !data ) 
  { 
    printf("malloc error:data!"); 
    exit(0); 
  } 
  cluster_center = (int*)malloc(sizeof(int) * (cluster_count + 1)); 
  if( !cluster_center ) 
  { 
    printf("malloc error:cluster_center!\n"); 
    exit(0); 
  } 
  distanceFromCenter = (double*)malloc(sizeof(double) * (cluster_count + 1)); 
  if( !distanceFromCenter ) 
  { 
    printf("malloc error: distanceFromCenter!\n"); 
    exit(0); 
  } 
  cluster_center_new = (ClusterCenter*)malloc(sizeof(struct ClusterCenter) * (cluster_count + 1)); 
  if( !cluster_center_new ) 
  { 
    printf("malloc cluster center new error!\n"); 
    exit(0); 
  } 
} 
 
/* 
 * 从文件中读入x和y数据 
 * */ 
void readDataFromFile(){ 
  FILE* fread; 
  if( NULL == (fread = fopen(filename, "r"))) 
  { 
    printf("open file(%s) error!\n", filename); 
    exit(0); 
  } 
  int row; 
  for( row = 1; row <= data_size; row++ ) 
  { 
    if( 2 != fscanf(fread, "%d %d ", &data[row].dimension_1, &data[row].dimension_2)) 
    { 
      printf("fscanf error: %d\n", row); 
    } 
    data[row].clusterID = 0; 
  } 
} 
 
/* 
 * 根据从主函数中传入的@cluster_count(聚类的个数)来随机的选择@cluster_count个 
 * 初始的聚类的起点 
 * */ 
 
void initial_cluster(){ 
  //辅助产生不重复的数 
  int* auxiliary; 
  int i; 
  auxiliary = (int*)malloc(sizeof(int) * (data_size + 1)); 
  if( !auxiliary ) 
  { 
    printf("malloc error: auxiliary"); 
    exit(0); 
  } 
  for( i = 1; i <= data_size; i++ ) 
  { 
    auxiliary[i] = i; 
  } 
   
  //产生初始化的cluster_count个聚类 
  int length = data_size; 
  int random; 
  for( i = 1; i <= cluster_count; i++ ) 
  { 
    random = rand()%length + 1; 
    //printf("%d \n", auxiliary[random]); 
    //data[auxiliary[random]].clusterID = auxiliary[random]; 
    cluster_center[i] = auxiliary[random]; 
    auxiliary[random] = auxiliary[length--]; 
  } 
   
  for( i = 1; i <= cluster_count; i++ ) 
  { 
    cluster_center_new[i].dimension_1 = data[cluster_center[i]].dimension_1; 
    cluster_center_new[i].dimension_2 = data[cluster_center[i]].dimension_2; 
    cluster_center_new[i].clusterID = i; 
    data[cluster_center[i]].clusterID = i; 
  } 
} 
 
/* 
 * 计算一个点(还没有划分到cluster center的点)到一个cluster center的distance 
 *   @itemID:  不属于任何cluster中的点 
 *   @centerID: center的ID 
 *   @count:   表明在计算的是itemID到第几个@center的distance，并且指明了结果放在distanceFromCenter的第几号元素 
 * */ 
void calculateDistance_ToOneCenter(int itemID,int centerID){ 
  distanceFromCenter[centerID] = sqrt( (data[itemID].dimension_1-cluster_center_new[centerID].dimension_1)*(double)(data[itemID].dimension_1-cluster_center_new[centerID].dimension_1) + (double)(data[itemID].dimension_2-cluster_center_new[centerID].dimension_2) * (data[itemID].dimension_2-cluster_center_new[centerID].dimension_2) ); 
} 
 
/* 
 * 计算一个点(还没有划分到cluster center的点)到每个cluster center的distance 
 * */ 
void calculateDistance_ToAllCenter(int itemID){ 
  int i; 
  for( i = 1; i <= cluster_count; i++ ) 
  { 
    calculateDistance_ToOneCenter(itemID, i); 
  } 
} 
 
void test_1() 
{ 
  calculateDistance_ToAllCenter(3); 
  int i; 
  for( i = 1; i <= cluster_count; i++ ) 
  { 
    printf("%f ", distanceFromCenter[i]); 
  } 
} 
 
/* 
 * 在得到任一的点(不属于任一cluster的)到每一个cluster center的distance之后，决定它属于哪一个cluster center,即取距离最小的 
 *   函数功能：得到一个item所属的cluster center 
 * */ 
void partition_forOneItem(int itemID){ 
  //操作对象是 distanceFromCenter和cluster_center 
  int i; 
  int min_index = 1; 
  double min_value = distanceFromCenter[1]; 
  for( i = 2; i <= cluster_count; i++ ) 
  { 
    if( distanceFromCenter[i] < min_value ) 
    { 
      min_value = distanceFromCenter[i]; 
      min_index = i; 
    } 
  } 
 
  data[itemID].clusterID = cluster_center_new[min_index].clusterID; 
} 
 
/* 
 * 得到所有的item所属于的cluster center , 在一轮的聚类中 
 * */ 
void partition_forAllItem_OneCluster(int round){        //changed!!!!!!!!!!!!!!!!!!!!!!!! 
  int i; 
  for( i = 1; i <= data_size; i++ ) 
  { 
    if( data[i].clusterID != 0 ) 
      continue; 
    else 
    { 
      calculateDistance_ToAllCenter(i);  //计算i到所有center的distance 
      partition_forOneItem(i);    //根据distance对i进行partition 
    } 
  } 
 
  //把聚类得到的数据写入到文件中 
  writeClusterDataToFile(round); 
} 
 
/* 
 * 将聚类得到的数据写入到文件中，每一个类写入一个文件中 
 *   @round: 表明在进行第几轮的cluster，该参数的另一个作用是指定了文件名字中的第一个项. 
 * */ 
void writeClusterDataToFile(int round){ 
  int i; 
  char filename[200]; 
  FILE** file; 
  file = (FILE**)malloc(sizeof(FILE*) * (cluster_count + 1)); 
  if( !file ) 
  { 
    printf("malloc file error!\n"); 
    exit(0); 
  } 
  for( i = 1; i <= cluster_count; i++ ) 
  { 
    sprintf(filename, ".//ClusterProcess//round%d_cluster%d.data", round, i); 
    if( NULL == (file[i] = fopen(filename, "w"))) 
    { 
      printf("file open(%s) error!", filename); 
      exit(0); 
    } 
  } 
   
  for( i = 1; i <= data_size; i++ ) 
  { 
    //sprintf(filename, ".//ClusterProcess//round%d_cluster%d.data", round, data[i].clusterID); 
    fprintf(file[data[i].clusterID], "%d\t%d\n", data[i].dimension_1, data[i].dimension_2); 
  } 
  for( i = 1; i <= cluster_count; i++ ) 
  { 
    //sprintf(filename, ".//ClusterProcess//round%d_cluster%d.data", round, i); 
    fclose(file[i]); 
  } 
} 
 
/* 
 * 重新计算新的cluster center 
 * */ 
void calculate_clusterCenter(int round){          //changed!!!!!!!!!!!!!!!!!!!!!! 
  int i; 
  double* new_X_Y;  /* 
          用来计算和保存新的cluster center的值，同样的，0号元素不用。1,2号元素分别用来 
          存放第一个聚类的所有的项的x和y的累加和。3,4号元素分别用来存放第二个聚类的所有 
          的项的x和y的累加和...... 
        */ 
  new_X_Y = (double*)malloc(sizeof(double) * (2 * cluster_count + 1)); 
  if( !new_X_Y ) 
  { 
    printf("malloc error: new_X_Y!\n"); 
    exit(0); 
  } 
  //初始化为0 
  for( i = 1; i <= 2*cluster_count; i++ ) 
    new_X_Y[i] = 0.0; 
 
  //用来统计属于各个cluster的item的个数 
  int* counter; 
  counter = (int*)malloc(sizeof(int) * (cluster_count + 1)); 
  if( !counter ) 
  { 
    printf("malloc error: counter\n"); 
    exit(0); 
  } 
  //初始化为0 
  for( i = 1; i <= cluster_count; i++ ) 
    counter[i] = 0; 
 
  for( i = 1; i <= data_size; i++ ) 
  { 
    new_X_Y[data[i].clusterID * 2 - 1] += data[i].dimension_1; 
    new_X_Y[data[i].clusterID * 2] += data[i].dimension_2; 
    counter[data[i].clusterID]++; 
  } 
 
  for( i = 1; i <= cluster_count; i++ ) 
  { 
    new_X_Y[2 * i - 1] = new_X_Y[2 * i - 1] / (double)(counter[i]); 
    new_X_Y[2 * i] = new_X_Y[2 * i] / (double)(counter[i]); 
  } 
   
  //要将cluster center的值保存在文件中，后续作图 
  writeClusterCenterToFile(round); 
   
  /* 
   * 在这里比较一下新的和旧的cluster center值的差别。如果是相等的，则停止K-means算法。 
   * */ 
  compareNew_OldClusterCenter(new_X_Y); 
 
  //将新的cluster center的值放入cluster_center_new 
  for( i = 1; i <= cluster_count; i++ ) 
  { 
    cluster_center_new[i].dimension_1 = new_X_Y[2 * i - 1]; 
    cluster_center_new[i].dimension_2 = new_X_Y[2 * i]; 
    cluster_center_new[i].clusterID = i; 
  } 
  free(new_X_Y); 
  free(counter); 
 
  //在重新计算了新的cluster center之后，意味着我们要重新来为每一个Item进行聚类，所以data中用于表示聚类ID的clusterID 
  //要都重新置为0。 
  for( i = 1; i <= data_size; i++ ) 
  { 
    data[i].clusterID = 0; 
  } 
} 
 
/* 
 * 将得到的新的cluster_count个cluster center的值保存在文件中。以便于观察聚类的过程。 
 * */ 
void writeClusterCenterToFile(int round){ 
  FILE* file; 
  int i; 
  char filename[200]; 
  sprintf(filename, ".//ClusterProcess//round%d_clusterCenter.data", round); 
  if( NULL == (file = fopen(filename, "w"))) 
  { 
    printf("open file(%s) error!\n", filename); 
    exit(0); 
  } 
 
  for( i = 1; i <= cluster_count; i++ ) 
  { 
    fprintf(file, "%f\t%f\n", cluster_center_new[i].dimension_1, cluster_center_new[i].dimension_2); 
  } 
 
  for( i = 1; i <= cluster_count; i++ ) 
  { 
    fclose(file); 
  } 
} 
 
/* 
 * 比较新旧的cluster center的差异 
 * */ 
void compareNew_OldClusterCenter(double* new_X_Y){ 
  int i; 
  isContinue = 0;       //等于0表示的是不要继续 
  for( i = 1; i <= cluster_count; i++ ) 
  { 
    if( new_X_Y[2 * i - 1] != cluster_center_new[i].dimension_1 || new_X_Y[2 * i] != cluster_center_new[i].dimension_2) 
    { 
      isContinue = 1;   //要继续 
      break; 
    } 
  } 
} 
 
/************************************************************************************************ 
 *         K-means算法            *    
 ***********************************************************************************************/ 
void K_means(){ 
  int times_cluster; 
  for( times_cluster = 1; times_cluster <= MAX_ROUND_TIME; times_cluster++ ) 
  { 
    printf("\n            times : %d             \n", times_cluster); 
    partition_forAllItem_OneCluster(times_cluster); 
    calculate_clusterCenter(times_cluster); 
    if( 0 == isContinue ) 
    { 
      break; 
      //printf("\n\nthe application can stop!\n\n"); 
    } 
  } 
}

 

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