计算数据集中的元素与各个簇的中心的距离，将它赋给最近的簇，然后重新计算每个簇的平均值，再将元素按离平均值点最近的原则重新分配直到没有出现重新分配

该算法要事先给出k的值，即划分为几个簇。

vector<int> datoclu(data.size(), -1);用这个来标记每个数据在哪个簇中。

#include <fstream>
#include <sstream>
#include <vector>
#include <iostream>

using namespace std;

struct Point
{
    double x;
    double y;
};


double distance(const Point& a, const Point& b)
{
    return sqrt(pow(a.x - b.x, 2) + pow(a.y - b.y, 2));
}

vector<int> KMeans(vector<Point>& data, int k, int maxIterations)
{
    vector<Point> centroids(k);
    for (int i = 0; i < k; i++)          
    {
        centroids[i] = data[rand() % data.size()];      //随机选择k个类聚中心。0到(data.size()-1)
    }

    vector<int> datoclu(data.size(), -1);           //每个数据属于哪个簇
    bool flag = 0;
    while (!flag && maxIterations)
    {
        flag = 1;
        for (int i = 0; i < data.size(); i++)
        {
            double minDis = numeric_limits<double>::max();
            int index = -1;
            for (int j = 0; j < centroids.size(); j++)
            {
                double dis = distance(data[i], centroids[j]);
                if (dis < minDis)
                {
                    minDis = dis;
                    index = j;
                }
            }
            if (datoclu[i] != index)                //记录每个数据属于的聚类中心
            {
                datoclu[i] = index;
                flag = 0;
            }
        }

        vector<Point> newClu(k);
        vector<int> num(k, 0);

        //计算每个簇平均值点
        for (int i = 0; i < data.size(); i++)
        {
            newClu[datoclu[i]].x += data[i].x;
            newClu[datoclu[i]].y += data[i].y;
            num[datoclu[i]]++;
            
        }
        for (int i = 0; i < k; i++)
        {
            newClu[i].x /= num[i];
            newClu[i].y /= num[i];
        }
        centroids = newClu;
        maxIterations--;
    }
    return datoclu;
}
vector<Point> ReadData(string filename)
{
    vector<Point> data;
    ifstream file(filename);
    if (file.is_open())
    {
        string line;
        while (getline(file, line))
        {
            istringstream iss(line);
            double x, y;
            string token;
            Point point;
            if (getline(iss, token, ',') && istringstream(token) >> point.x &&
                getline(iss, token, ',') && istringstream(token) >> point.y) {
                data.push_back(point);
            }
        }
    }
    else
    {
        cout << "open fail";
    }
    file.close();
    return data;
}

int main()
{
    vector<Point> dataset = ReadData("data.txt");
    vector<int> clusters;
    int k, maxIterations;
    cout << "输入簇的个数和最大迭代次数"<<endl;
    cin >> k >> maxIterations;
    clusters= KMeans(dataset, k, maxIterations);
    vector <vector<int>> index(k);
   
    for (int j = 0; j < k; j++)
    {
        for (int i = 0; i < clusters.size(); i++)
        {
            if (clusters[i] == j)
            {
                 index[j].push_back(i);
            }
        }
    }
    for (int i = 0; i < index.size(); i++)
    {
        cout << "{";
        for (int j = 0; j < index[i].size(); j++)
        {
            
            cout << index[i][j]+1;
            if (j != index[i].size() - 1)
            {
                cout << ",";
            }
        }
        cout << "}";
    }
}    

数据集

1.0, 1.0 
2.0, 1.0 
1.0, 2.0  
2.0, 2.0  
4.0, 3.0  
5.0, 3.0  
4.0, 4.0  
5.0,4.0

运行结果