Create k_means_clustering.cpp

cpp/k_means_clustering.cpp

@@ -0,0 +1,78 @@
+#include <iostream>
+#include <vector>
+#include <cmath>
+#include <limits>
+using namespace std;
+
+double euclideanDistance(pair<double, double> point1, pair<double, double> point2) {
+    return sqrt(pow(point1.first - point2.first, 2) + pow(point1.second - point2.second, 2));
+}
+void kMeansClustering(vector<pair<double, double>>& data, int k, int iterations) {
+    int n = data.size();
+    vector<pair<double, double>> centroids(k);
+    vector<int> labels(n);  
+
+    for (int i = 0; i < k; i++) 
+        centroids[i] = data[i];
+
+    for (int iter = 0; iter < iterations; iter++) {
+        for (int i = 0; i < n; i++) {
+            double minDistance = numeric_limits<double>::max();
+            int closestCentroid = 0;
+
+            for (int j = 0; j < k; j++) {
+                double distance = euclideanDistance(data[i], centroids[j]);
+                if (distance < minDistance) {
+                    minDistance = distance;
+                    closestCentroid = j;
+                }
+            }
+            labels[i] = closestCentroid;
+        }
+
+        vector<pair<double, double>> newCentroids(k, {0, 0});
+        vector<int> count(k, 0);
+
+        for (int i = 0; i < n; i++) {
+            newCentroids[labels[i]].first += data[i].first;
+            newCentroids[labels[i]].second += data[i].second;
+            count[labels[i]]++;
+        }
+
+        for (int j = 0; j < k; j++) {
+            if (count[j] != 0) {
+                newCentroids[j].first /= count[j];
+                newCentroids[j].second /= count[j];
+            }
+        }
+
+        centroids = newCentroids; 
+    }
+
+    cout << "Final Centroids:\n";
+    for (int i = 0; i < k; i++) {
+        cout << "Centroid " << i + 1 << ": (" << centroids[i].first << ", " << centroids[i].second << ")\n";
+    }
+
+    cout << "\nCluster Assignments:\n";
+    for (int i = 0; i < n; i++) {
+        cout << "Point (" << data[i].first << ", " << data[i].second << ") is in cluster " << labels[i] + 1 << "\n";
+    }
+}
+
+int main() {
+    int n, k, iterations;
+
+    cout << "Enter the number of data points: ";
+    cin >> n;
+    vector<pair<double, double>> data(n);
+    cout << "Enter the data points (x y):\n";
+    for (int i = 0; i < n; i++) 
+        cin >> data[i].first >> data[i].second;
+    cout << "Enter the number of clusters (k): ";
+    cin >> k;
+    cout << "Enter the number of iterations: ";
+    cin >> iterations;
+    kMeansClustering(data, k, iterations);
+    return 0;
+}