K Means Clustering

KMeans.java

import java.util.Random;


public class KMeans {

	public static double distance(double x[], double y[]) {
		int D = x.length;
		double distance = 0;
		for(int i = 0; i < D; i++) {
			distance += Math.pow(x[i] - y[i], 2);
		}
		return Math.sqrt(distance);
	}
	
	public static double[][] means(double[][] data, int k, int iterations) {
		int N = data.length;
		int D = data[0].length;
		double means[][] = new double[k][D];
		Random random = new Random();
		for(int i = 0; i < k; i++) {
			int center = random.nextInt(N);
			means[i] = data[center];
		}
		System.out.println(means[0][0] + " " + means[1][0]);

		int responsability[] = new int[N];
		for(int iteration = 0; iteration < iterations; iteration++) {
			for(int i = 0; i < N; i++) {
				double min = Integer.MAX_VALUE;
				int minVal = 0;
				for(int j = 0; j < k; j++) {
					double dist = distance(data[i], means[j]);
					if(dist < min) {
						min = dist;
						minVal = j;
					}
				}
				responsability[i] = minVal;
			}
			
			for(int i = 0; i < k; i++) {
				means[i] = new double[D];
				int count = 0;
				for(int j = 0; j < N; j++) {
					if(responsability[j] == i) {
						for(int d = 0; d < D; d++) {
							means[i][d] += data[j][d];
						}
						count++;
					}
				}
				for(int d = 0; d < D; d++) {
					means[i][d] /= count;
				}
			}
		}
		return means;
	} 

	public static void main(String[] args) {
		double data[][] = new double[][] {
				{1 + Math.random()},
				{2 + Math.random()},
				{1 + Math.random()},
				{2 + Math.random()},
				{1 + Math.random()},
				{2 + Math.random()},

				{10 + Math.random()},
				{20 + Math.random()},
				{10 + Math.random()},
				{20 + Math.random()},
				{10 + Math.random()},
				{20 + Math.random()},
				{10 + Math.random()},
				{20 + Math.random()},

		};
		double means[][] =  means(data, 2, 10);
		System.out.println(means[0][0] + " " + means[1][0]);
	}
	
}