k-means algorithm c++ implementation

 Calculate the distance between the elements in the data set and the center of each cluster, assign it to the nearest cluster, then recalculate the average of each cluster, and then redistribute the elements according to the principle of being closest to the average point until no redistribution occurs

This algorithm requires the value of k to be given in advance, that is, it is divided into several clusters.

vector<int> datoclu(data.size(), -1);Use this to mark which cluster each data is in.

#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 << "}";
    }
}    

data set

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

operation result