Realization of two clusterization algorithms

Clusterizer.java

package clusterizer;

import java.util.ArrayList;

public abstract class Clusterizer {

	protected class Pair {
		public int x, y; // ID's of clusters
		public Pair(int x, int y) {
			this.x = x;
			this.y = y;
		}
	}

	protected Point_double getCenterOfMass(ArrayList<Point_double> cluster) {
		Point_double result = new Point_double(0.0, 0.0);
		for (Point_double aCluster : cluster) {
			result.x += aCluster.x;
			result.y += aCluster.y;
		}
		result.x /= cluster.size();
		result.y /= cluster.size();
		return result;
	}

	protected double getDistance(Point_double A, Point_double B) {
		return Math.sqrt(Math.pow(A.x - B.x, 2) + Math.pow(A.y - B.y, 2));
	}

	protected abstract Pair getMinimum(ArrayList<ArrayList<Point_double>> clusters);

	protected abstract  ArrayList<ArrayList<Point_double>> split(ArrayList<ArrayList<Point_double>> clusters);

	protected boolean containMinimalClusters(ArrayList<ArrayList<Point_double>> clusters) {
		for (ArrayList<Point_double> cluster : clusters)
			if (cluster.size() == 1)
				return true;
		return false;
	}

	protected void printClusters(ArrayList<ArrayList<Point_double>> clusters) {
		for (int i = 0, clustersSize = clusters.size(); i < clustersSize; i++) {
			ArrayList<Point_double> cluster = clusters.get(i);
			System.out.print(i + ":[");
			for (Point_double point : cluster) {
				System.out.print(point.x + "," + point.y + "; ");
			}
			System.out.print("]; ");
		}
		System.out.println("\n");
	}

	public abstract ArrayList<ArrayList<Point_double>> process(ArrayList<ArrayList<Point_double>> clusters);

}

DependentClusterizer.java

package clusterizer;

import java.util.ArrayList;

public class DependentClusterizer extends Clusterizer {

	private Pair checkMinimum(Pair min, ArrayList<ArrayList<Point_double>> clusters) {
		double distance = getDistance(
				getCenterOfMass(clusters.get(min.x)),
				getCenterOfMass(clusters.get(min.y))
		);
		if (distance > Config.MAX_DISTANCE)
			return null;
		return min;
	}

	protected Pair getMinimum(ArrayList<ArrayList<Point_double>> clusters) {
		Pair min = new Pair(0, 1);
		double minDistance = getDistance(getCenterOfMass(clusters.get(0)), getCenterOfMass(clusters.get(1)));
		for (int i = 0; i < clusters.size() - 1; i++) {
			for (int j = i + 1; j < clusters.size(); j++) {
					double distance = getDistance(
							getCenterOfMass(clusters.get(i)),
							getCenterOfMass(clusters.get(j))
					);
					if (distance < minDistance) {
						minDistance = distance;
						min = new Pair(i, j);
					}
			}
		}

		return checkMinimum(min, clusters);
	}

	protected ArrayList<ArrayList<Point_double>> split(ArrayList<ArrayList<Point_double>> clusters) {
		ArrayList<ArrayList<Point_double>> result = new ArrayList<ArrayList<Point_double>>();
		for (int i = 0; i < Config.COLOR.length; i++) {
			if (i == clusters.size())
				break;
			result.add(clusters.get(i));
		}
		
		for (int i = Config.COLOR.length; i < clusters.size(); i++) {
			ArrayList<Point_double> cluster = clusters.get(i);
			for (Point_double point : cluster) {
				ArrayList<Point_double> tmp = new ArrayList<Point_double>();
				tmp.add(point);
				result.add(tmp);
			}

		}
		return result;
	}

	public ArrayList<ArrayList<Point_double>> process(ArrayList<ArrayList<Point_double>> clusters) {
		System.out.println("<-------------" + clusters.size() + "------------->");
		ArrayList<ArrayList<Point_double>> result = clusters;
		printClusters(result);
		if (clusters.size() > 1) {
			Pair min = getMinimum(result);
			System.out.print("Min: ");
			if (min == null)
				System.out.println("null");
			else {
				System.out.print( "[" + min.x + "," + min.y + "]; ");
				if (!result.get(min.x).isEmpty() && !result.get(min.y).isEmpty()) {
					for (Point_double point : result.get(min.y))
						result.get(min.x).add(point);
				}
				System.out.println();
				result.remove(min.y);
				split(result);
				printClusters(result);
			}
		}
		System.out.println("<--------------------------->\n\n");
		return result;
	}

}

IndependentClusterizer.java

package clusterizer;

import java.util.ArrayList;

public class IndependentClusterizer extends Clusterizer {

	protected Pair getMinimum(ArrayList<ArrayList<Point_double>> clusters) {
		Pair min =null;
		double minDistance = 0;
		for (int i = 0; i < clusters.size() - 1; i++) {
			for (int j = i + 1; j < clusters.size(); j++) {
				if ((clusters.get(i).size() == 1) || (clusters.get(j).size() == 1)) {
					if (min == null) {
						min = new Pair(i, j);
						minDistance = getDistance(
								getCenterOfMass(clusters.get(i)),
								getCenterOfMass(clusters.get(j))
						);
					} else {
						double distance = getDistance(
								getCenterOfMass(clusters.get(i)),
								getCenterOfMass(clusters.get(j))
						);
						if (distance < minDistance) {
							min = new Pair(i, j);
							minDistance = distance;
						}
					}
				}
			}
		}
		return min;
	}

	protected ArrayList<ArrayList<Point_double>> split(ArrayList<ArrayList<Point_double>> clusters) {
		ArrayList<ArrayList<Point_double>> result = new ArrayList<ArrayList<Point_double>>();
		for (ArrayList<Point_double> cluster : clusters) {
			for (Point_double point : cluster) {
				ArrayList<Point_double> tmp = new ArrayList<Point_double>();
				tmp.add(point);
				result.add(tmp);
			}
		}
		return result;
	}

	public ArrayList<ArrayList<Point_double>> process(ArrayList<ArrayList<Point_double>> clusters) {
		ArrayList<ArrayList<Point_double>> result = split(clusters);
		if (clusters.size() > 1) {
			System.out.println("<--------" + result.size() + "-------->");
			printClusters(result);
			while (containMinimalClusters(result)) {
				Pair min = getMinimum(result);
				for (Point_double point : result.get(min.y))
					result.get(min.x).add(point);
				result.remove(min.y);
				System.out.println("Merge: " + min.x + " & " + min.y + ";");
				printClusters(result);
			}
			System.out.println("<------------------->\n\n");
		}
		return result;
	}

}