K means algorithm

import java.util.ArrayList;
import java.util.Arrays;
import java.util.Scanner;
import java.util.Vector;

public class KMeans {

    static Scanner sc = new Scanner(System.in);

    static double dataSet[][];
    static int clusters;
    static double centroids[][];
    static double clusterDistanceArrays[][];
    static Vector al[];

    public static void getElements() {
        System.out.println("Enter the number of data elements: ");
        int n = sc.nextInt();
        dataSet = new double[n][];
        System.out.println("Enter the elements : ");
        for (int i = 0; i < dataSet.length; i++) {
            dataSet[i] = new double[2];
            dataSet[i][0] = sc.nextDouble();
            dataSet[i][1] = sc.nextDouble();
        }
    }

    public static void setCentroids() {

        for (int i = 0; i < clusters; i++) {

            centroids[i] = dataSet[i];
        }
    }

    public static void printCentroids() {
        int i = 0;
        for (double x[] : centroids) {

            System.out.print("centroids " + i + ": ");
            i++;

            for (double y : x) {
                System.out.print(y + " ");
            }
            System.out.println();
        }

    }

    public static void clusterDistanceArrays() {
        clusterDistanceArrays = new double[clusters][dataSet.length];
    }

    public static double findDistance(double a, double b, double x, double y) {
        //return Math.sqrt(Math.pow((2 - 1),2)+Math.pow((3 - 2), 2));
        return Math.sqrt(Math.pow((a - x), 2) + Math.pow((b - y), 2));
    }

    public static void calculateDistances() {

        for (int i = 0; i < centroids.length; i++) {
            for (int j = 0; j < dataSet.length; j++) {

                clusterDistanceArrays[i][j] = findDistance(dataSet[j][0], dataSet[j][1], centroids[i][0], centroids[i][1]);
            }
        }
    }

    public static void printDistances() {
        for (double x[] : clusterDistanceArrays) {
            for (double y : x) {
                System.out.print(y + " ");
            }
            System.out.println();
        }

    }

    public static void addIntoClusters() {
        // al = new Vector[clusters];
        initClusters();
        for (int i = 0; i < dataSet.length; i++) {
            int index = 0;
            double smallest = clusterDistanceArrays[0][i];

            for (int j = 1; j < centroids.length; j++) {
                if (smallest >= clusterDistanceArrays[j][i]) {
                    index = j;
                    smallest = clusterDistanceArrays[j][i];
                }
            }

            al[index].addElement(dataSet[i]);
        }
    }

    public static void updateCentroids() {
        for (int i = 0; i < al.length; i++) {
            double x[][] = new double[1][2];
            for (int j = 0; j < al[i].size(); j++) {
                double c[][] = new double[1][2];
                c[0] = (double[]) al[i].get(j);
                x[0][0] += c[0][0];
                x[0][1] += c[0][1];

                //System.out.print(Arrays.toString(c[0]));
            }
            x[0][0] = (double) (x[0][0] / al[i].size());
            x[0][1] = (double) (x[0][1] / al[i].size());
            centroids[i] = x[0];
            //System.out.println(x[0][0]+" "+x[0][1]);
        }

    }

    public static void printClusters() {
        for (int i = 0; i < al.length; i++) {
            System.out.print("cluster " + i + ": ");
            for (int j = 0; j < al[i].size(); j++) {
                double c[][] = new double[1][2];
                c[0] = (double[]) al[i].get(j);
                System.out.print(Arrays.toString(c[0]));
            }
            System.out.println();
        }
    }

    public static void initClusters() {
        al = new Vector[clusters];
        for (int i = 0; i < al.length; i++) {
            al[i] = new Vector(0);
        }
    }

    public static void main(String args[]) {
        getElements();
        System.out.println("Enter number of clusters: ");
        clusters = sc.nextInt();
        centroids = new double[clusters][2];
        initClusters();
        setCentroids();
        clusterDistanceArrays();

        for (int i = 0; i < 50; i++) {
            calculateDistances();
            addIntoClusters();
            updateCentroids();
        }

        System.out.println("The clusters: ");
        printClusters();
        System.out.println("Final Centroids: ");
        printCentroids();

    }
}

/*
run:
Enter the number of data elements:
8
Enter the elements :
2 10
2 5
8 4
5 8
7 5
6 4
1 2
4 9
Enter number of clusters:
3
The clusters:
cluster 0: [2.0, 10.0][5.0, 8.0][4.0, 9.0]
cluster 1: [2.0, 5.0][1.0, 2.0]
cluster 2: [8.0, 4.0][7.0, 5.0][6.0, 4.0]
Final Centroids:
centroids 0: 3.6666666666666665 9.0
centroids 1: 1.5 3.5
centroids 2: 7.0 4.333333333333333
BUILD SUCCESSFUL (total time: 1 minute 3 seconds)
*/