用c语言编写N-Step-SCAN

N-Step-SCAN是一种基于SCAN算法的聚类算法，可以用于将数据分成不同的群组。以下是使用C语言编写N-Step-SCAN算法的示例代码：

#include <stdio.h>
#include <stdlib.h>
#include <math.h>

#define N 10  // 数据点数量
#define Eps 1.2 // 邻域半径
#define MinPts 3 // 最小密度阈值

int visited[N];  // 记录数据点是否被访问过
int cluster[N];  // 记录数据点所属的簇
int unvisited[N];  // 记录未被访问的数据点
int num_unvisited;  // 未被访问的数据点数量

// 计算两个数据点之间的距离
float distance(float x1[], float x2[]) {
    float d = 0.0;
    for (int i = 0; i < N; i++) {
        d += pow(x1[i] - x2[i], 2);
    }
    return sqrt(d);
}

// 找到所有与当前点距离小于邻域半径的数据点
int* neighbors(float data[][N], int p) {
    int* neighbors = (int*)malloc(N * sizeof(int));
    int count = 0;
    for (int i = 0; i < N; i++) {
        if (distance(data[p], data[i]) <= Eps) {
            neighbors[count++] = i;
        }
    }
    return neighbors;
}

// 执行聚类
void cluster_points(float data[][N], int p, int cluster_id) {
    int* seeds = neighbors(data, p);
    if (seeds == NULL) {
        visited[p] = 1;
        return;
    }
    cluster[p] = cluster_id;
    for (int i = 0; i < MinPts; i++) {
        int q = seeds[i];
        if (!visited[q]) {
            int* q_neighbors = neighbors(data, q);
            if (q_neighbors != NULL) {
                int num_q_neighbors = 0;
                for (int j = 0; j < N; j++) {
                    if (q_neighbors[j] != -1) {
                        num_q_neighbors++;
                    }
                }
                if (num_q_neighbors >= MinPts) {
                    for (int j = 0; j < N; j++) {
                        if (q_neighbors[j] != -1) {
                            seeds[MinPts++] = q_neighbors[j];
                        }
                    }
                }
            }
            visited[q] = 1;
            cluster[q] = cluster_id;
        }
    }
    visited[p] = 1;
    free(seeds);
}

// N-Step-SCAN算法
void n_step_scan(float data[][N]) {
    int cluster_id = 0;
    for (int i = 0; i < N; i++) {
        if (!visited[i]) {
            int* unvisited = neighbors(data, i);
            if (unvisited != NULL) {
                num_unvisited = 0;
                for (int j = 0; j < N; j++) {
                    if (unvisited[j] != -1) {
                        visited[unvisited[j]] = 1;
                        unvisited[num_unvisited++] = unvisited[j];
                    }
                }
                free(unvisited);
                if (num_unvisited >= MinPts) {
                    cluster_points(data, i, cluster_id++);
                }
            }
        }
    }
}

int main() {
    float data[N][N] = {
        {1, 2, 3, 4, 5, 6, 7, 8, 9, 10},
        {2, 3, 4, 5, 6, 7, 8, 9, 10, 1},
        {3, 4, 5, 6, 7, 8, 9, 10, 1, 2},
        {4, 5, 6, 7, 8, 9, 10, 1, 2, 3},
        {5, 6, 7, 8, 9, 10, 1, 2, 3, 4},
        {6, 7, 8, 9, 10, 1, 2, 3, 4, 5},
        {7, 8, 9, 10, 1, 2, 3, 4, 5, 6},
        {8, 9, 10, 1, 2, 3, 4, 5, 6, 7},
        {9, 10, 1, 2, 3, 4, 5, 6, 7, 8},
        {10, 1, 2, 3, 4, 5, 6, 7, 8, 9}
    };
    n_step_scan(data);
    for (int i = 0; i < N; i++) {
        printf("Data point %d belongs to cluster %d\n", i, cluster[i]);
    }
    return 0;
}

这个示例代码中，我们使用了一个包含10个数据点的简单数据集。在实际使用中，可以根据需要修改数据集的大小和内容。