k-means Cluster Anlysis Algorithm. - Algorithms Source Code in Java

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Home » Java Home » Algorithms Home » k-means Cluster Anlysis Algorithm.

A D V E R T I S E M E N T

Title	k-means Cluster Anlysis Algorithm.
Author	Shyam Sivaraman
Author Email	shyam_siv [at] yahoo.com
Description	The package aims at providing an implementation of k-means Clustering Algorithm in Java. The package does not provide for any UI and it is up to the user to display the output in the required format.
Category	Java » Algorithms
Hits	467906
Code	Select and Copy the Code
---------------JCA.java------------- package org.c4s.algorithm.cluster; import java.util.Vector; / This class is the entry point for constructing Cluster Analysis objects. Each instance of JCA object is associated with one or more clusters, and a Vector of DataPoint objects. The JCA and DataPoint classes are the only classes available from other packages. @see DataPoint / public class JCA { private Cluster[] clusters; private int miter; private Vector mDataPoints = new Vector(); private double mSWCSS; public JCA(int k, int iter, Vector dataPoints) { clusters = new Cluster[k]; for (int i = 0; i < k; i++) { clusters[i] = new Cluster("Cluster" + i); } this.miter = iter; this.mDataPoints = dataPoints; } private void calcSWCSS() { double temp = 0; for (int i = 0; i < clusters.length; i++) { temp = temp + clusters[i].getSumSqr(); } mSWCSS = temp; } public void startAnalysis() { //set Starting centroid positions - Start of Step 1 setInitialCentroids(); int n = 0; //assign DataPoint to clusters loop1: while (true) { for (int l = 0; l < clusters.length; l++) { clusters[l].addDataPoint((DataPoint)mDataPoints.elementAt(n)); n++; if (n >= mDataPoints.size()) break loop1; } } //calculate E for all the clusters calcSWCSS(); //recalculate Cluster centroids - Start of Step 2 for (int i = 0; i < clusters.length; i++) { clusters[i].getCentroid().calcCentroid(); } //recalculate E for all the clusters calcSWCSS(); for (int i = 0; i < miter; i++) { //enter the loop for cluster 1 for (int j = 0; j < clusters.length; j++) { for (int k = 0; k < clusters[j].getNumDataPoints(); k++) { //pick the first element of the first cluster //get the current Euclidean distance double tempEuDt = clusters[j].getDataPoint(k).getCurrentEuDt(); Cluster tempCluster = null; boolean matchFoundFlag = false; //call testEuclidean distance for all clusters for (int l = 0; l < clusters.length; l++) { //if testEuclidean < currentEuclidean then if (tempEuDt > clusters[j].getDataPoint(k).testEuclideanDistance(clusters[l].getCentroid())) { tempEuDt = clusters[j].getDataPoint(k).testEuclideanDistance(clusters[l].getCentroid()); tempCluster = clusters[l]; matchFoundFlag = true; } //if statement - Check whether the Last EuDt is > Present EuDt } //for variable 'l' - Looping between different Clusters for matching a Data Point. //add DataPoint to the cluster and calcSWCSS if (matchFoundFlag) { tempCluster.addDataPoint(clusters[j].getDataPoint(k)); clusters[j].removeDataPoint(clusters[j].getDataPoint(k)); for (int m = 0; m < clusters.length; m++) { clusters[m].getCentroid().calcCentroid(); } //for variable 'm' - Recalculating centroids for all Clusters calcSWCSS(); } //if statement - A Data Point is eligible for transfer between Clusters. } //for variable 'k' - Looping through all Data Points of the current Cluster. }//for variable 'j' - Looping through all the Clusters. }//for variable 'i' - Number of iterations. } public Vector[] getClusterOutput() { Vector v[] = new Vector[clusters.length]; for (int i = 0; i < clusters.length; i++) { v[i] = clusters[i].getDataPoints(); } return v; } private void setInitialCentroids() { //kn = (round((max-min)/k)n)+min where n is from 0 to (k-1). double cx = 0, cy = 0; for (int n = 1; n <= clusters.length; n++) { cx = (((getMaxXValue() - getMinXValue()) / (clusters.length + 1)) n) + getMinXValue(); cy = (((getMaxYValue() - getMinYValue()) / (clusters.length + 1)) * n) + getMinYValue(); Centroid c1 = new Centroid(cx, cy); clusters[n - 1].setCentroid(c1); c1.setCluster(clusters[n - 1]); } } private double getMaxXValue() { double temp; temp = ((DataPoint) mDataPoints.elementAt(0)).getX(); for (int i = 0; i < mDataPoints.size(); i++) { DataPoint dp = (DataPoint) mDataPoints.elementAt(i); temp = (dp.getX() > temp) ? dp.getX() : temp; } return temp; } private double getMinXValue() { double temp = 0; temp = ((DataPoint) mDataPoints.elementAt(0)).getX(); for (int i = 0; i < mDataPoints.size(); i++) { DataPoint dp = (DataPoint) mDataPoints.elementAt(i); temp = (dp.getX() < temp) ? dp.getX() : temp; } return temp; } private double getMaxYValue() { double temp = 0; temp = ((DataPoint) mDataPoints.elementAt(0)).getY(); for (int i = 0; i < mDataPoints.size(); i++) { DataPoint dp = (DataPoint) mDataPoints.elementAt(i); temp = (dp.getY() > temp) ? dp.getY() : temp; } return temp; } private double getMinYValue() { double temp = 0; temp = ((DataPoint) mDataPoints.elementAt(0)).getY(); for (int i = 0; i < mDataPoints.size(); i++) { DataPoint dp = (DataPoint) mDataPoints.elementAt(i); temp = (dp.getY() < temp) ? dp.getY() : temp; } return temp; } public int getKValue() { return clusters.length; } public int getIterations() { return miter; } public int getTotalDataPoints() { return mDataPoints.size(); } public double getSWCSS() { return mSWCSS; } public Cluster getCluster(int pos) { return clusters[pos]; } } /-----------------Cluster.java----------------/ package org.c4s.algorithm.cluster; import java.util.Vector; /** * This class represents a Cluster in a Cluster Analysis Instance. A Cluster is associated * with one and only one JCA Instance. A Cluster is related to more than one DataPoints and * one centroid. * @author Shyam Sivaraman * @version 1.1 * @see DataPoint * @see Centroid / class Cluster { private String mName; private Centroid mCentroid; private double mSumSqr; private Vector mDataPoints; public Cluster(String name) { this.mName = name; this.mCentroid = null; //will be set by calling setCentroid() mDataPoints = new Vector(); } public void setCentroid(Centroid c) { mCentroid = c; } public Centroid getCentroid() { return mCentroid; } public void addDataPoint(DataPoint dp) { //called from CAInstance dp.setCluster(this); //initiates a inner call to calcEuclideanDistance() in DP. this.mDataPoints.addElement(dp); calcSumOfSquares(); } public void removeDataPoint(DataPoint dp) { this.mDataPoints.removeElement(dp); calcSumOfSquares(); } public int getNumDataPoints() { return this.mDataPoints.size(); } public DataPoint getDataPoint(int pos) { return (DataPoint) this.mDataPoints.elementAt(pos); } public void calcSumOfSquares() { //called from Centroid int size = this.mDataPoints.size(); double temp = 0; for (int i = 0; i < size; i++) { temp = temp + ((DataPoint) this.mDataPoints.elementAt(i)).getCurrentEuDt(); } this.mSumSqr = temp; } public double getSumSqr() { return this.mSumSqr; } public String getName() { return this.mName; } public Vector getDataPoints() { return this.mDataPoints; } } /---------------Centroid.java-----------------/ package org.c4s.algorithm.cluster; /* * This class represents the Centroid for a Cluster. The initial centroid is calculated * using a equation which divides the sample space for each dimension into equal parts * depending upon the value of k. * @author Shyam Sivaraman * @version 1.0 * @see Cluster / class Centroid { private double mCx, mCy; private Cluster mCluster; public Centroid(double cx, double cy) { this.mCx = cx; this.mCy = cy; } public void calcCentroid() { //only called by CAInstance int numDP = mCluster.getNumDataPoints(); double tempX = 0, tempY = 0; int i; //caluclating the new Centroid for (i = 0; i < numDP; i++) { tempX = tempX + mCluster.getDataPoint(i).getX(); //total for x tempY = tempY + mCluster.getDataPoint(i).getY(); //total for y } this.mCx = tempX / numDP; this.mCy = tempY / numDP; //calculating the new Euclidean Distance for each Data Point tempX = 0; tempY = 0; for (i = 0; i < numDP; i++) { mCluster.getDataPoint(i).calcEuclideanDistance(); } //calculate the new Sum of Squares for the Cluster mCluster.calcSumOfSquares(); } public void setCluster(Cluster c) { this.mCluster = c; } public double getCx() { return mCx; } public double getCy() { return mCy; } public Cluster getCluster() { return mCluster; } } /----------------DataPoint.java----------------/ package org.c4s.algorithm.cluster; /* This class represents a candidate for Cluster analysis. A candidate must have a name and two independent variables on the basis of which it is to be clustered. A Data Point must have two variables and a name. A Vector of Data Point object is fed into the constructor of the JCA class. JCA and DataPoint are the only classes which may be available from other packages. @author Shyam Sivaraman @version 1.0 @see JCA @see Cluster / public class DataPoint { private double mX,mY; private String mObjName; private Cluster mCluster; private double mEuDt; public DataPoint(double x, double y, String name) { this.mX = x; this.mY = y; this.mObjName = name; this.mCluster = null; } public void setCluster(Cluster cluster) { this.mCluster = cluster; calcEuclideanDistance(); } public void calcEuclideanDistance() { //called when DP is added to a cluster or when a Centroid is recalculated. mEuDt = Math.sqrt(Math.pow((mX - mCluster.getCentroid().getCx()), 2) + Math.pow((mY - mCluster.getCentroid().getCy()), 2)); } public double testEuclideanDistance(Centroid c) { return Math.sqrt(Math.pow((mX - c.getCx()), 2) + Math.pow((mY - c.getCy()), 2)); } public double getX() { return mX; } public double getY() { return mY; } public Cluster getCluster() { return mCluster; } public double getCurrentEuDt() { return mEuDt; } public String getObjName() { return mObjName; } } /-----------------PrgMain.java---------------/ import org.c4s.algorithm.cluster.DataPoint; import org.c4s.algorithm.cluster.JCA; import java.util.Vector; import java.util.Iterator; /* * Created by IntelliJ IDEA. * User: shyam.s * Date: Apr 18, 2004 * Time: 4:26:06 PM */ public class PrgMain { public static void main (String args[]){ Vector dataPoints = new Vector(); dataPoints.add(new DataPoint(22,21,"p53")); dataPoints.add(new DataPoint(19,20,"bcl2")); dataPoints.add(new DataPoint(18,22,"fas")); dataPoints.add(new DataPoint(1,3,"amylase")); dataPoints.add(new DataPoint(3,2,"maltase")); JCA jca = new JCA(2,1000,dataPoints); jca.startAnalysis(); Vector[] v = jca.getClusterOutput(); for (int i=0; i<v.length; i++){ Vector tempV = v[i]; System.out.println("-----------Cluster"+i+"---------"); Iterator iter = tempV.iterator(); while(iter.hasNext()){ DataPoint dpTemp = (DataPoint)iter.next(); System.out.println(dpTemp.getObjName()+" ["+dpTemp.getX()+","+dpTemp.getY()+"]"); } } } }

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