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; 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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