k-means聚类算法c语言

以下是K-means聚类算法的C语言实现步骤和核心代码（部分）：

1. 首先需要定义一个结构体来表示数据点的坐标：

typedef struct {
    double x;
    double y;
} Point;

2. 接着需要定义一个函数来计算两个数据点之间的距离：

double distance(Point p1, Point p2) {
    double dx = p1.x - p2.x;
    double dy = p1.y - p2.y;
    return sqrt(dx * dx + dy * dy);
}

3. 然后需要定义一个函数来随机生成初始聚类中心点：

void initCentroids(int k, Point* centroids, int numPoints) {
    int i, j;
    for (i = 0; i < k; i++) {
        int index = rand() % numPoints;        centroids[i] = points[index];
        for (j = 0; j < i; j++) {
            if (centroids[i].x == centroids[j].x && centroids[i].y == centroids[j].y) {
                i--;
                break;
            }
        }
    }
}

4. 接着需要定义一个函数来将数据点分配到最近的聚类中心点：

void assignPointsToCentroids(int k, Point* centroids, int numPoints, int* assignments) {
    int i, j;
    for (i = 0; i < numPoints; i++) {
        double minDistance = DBL_MAX;
        int minIndex = -1;
        for (j = 0; j < k; j++) {
            double d = distance(points[i], centroids[j]);
            if (d < minDistance) {
                minDistance = d;
                minIndex = j;
            }
        }
        assignments[i] = minIndex;
    }
}

5. 然后需要定义一个函数来重新计算聚类中心点的位置：

void updateCentroids(int k, Point* centroids, int numPoints, int* assignments) {
    int i, j;
    for (i = 0; i < k; i++) {
        double sumX = 0.0;
        double sumY = 0.0;
        int count = 0;
        for (j = 0; j < numPoints; j++) {
            if (assignments[j] == i) {
                sumX += points[j].x;
                sumY += points[j].y;
                count++;
            }
        }
        if (count > 0) {
            centroids[i].x = sumX / count;
            centroids[i].y = sumY / count;
        }
    }
}

6. 最后需要定义一个函数来实现K-means聚类算法的主要逻辑：

void kMeansClustering(int k, Point* points, int numPoints, Point* centroids, int* assignments) {
    initCentroids(k, centroids, numPoints);
    int i;
    for (i = 0; i < MAX_ITERATIONS; i++) {
        assignPointsToCentroids(k, centroids, numPoints, assignments);
        updateCentroids(k, centroids, numPoints, assignments);
    }
}