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kmin.java
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253 lines (221 loc) · 7.72 KB
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import java.util.ArrayList;
public class kmin
{
private static final int NUM_CLUSTERS = 2; // Total clusters.
private static final int TOTAL_DATA = 7; // Total data points.
private static final double SAMPLES[][] = new double[][] {{1.0, 1.0},
{1.5, 2.0},
{3.0, 4.0},
{5.0, 7.0},
{3.5, 5.0},
{4.5, 5.0},
{3.5, 4.5}};
private static ArrayList<Data> dataSet = new ArrayList<Data>();
private static ArrayList<Centroid> centroids = new ArrayList<Centroid>();
private static void initialize()
{
System.out.println("Centroids initialized at:");
centroids.add(new Centroid(1.0, 1.0)); // lowest set.
centroids.add(new Centroid(5.0, 7.0)); // highest set.
System.out.println(" (" + centroids.get(0).X() + ", " + centroids.get(0).Y() + ")");
System.out.println(" (" + centroids.get(1).X() + ", " + centroids.get(1).Y() + ")");
System.out.print("\n");
return;
}
private static void kMeanCluster()
{
final double bigNumber = Math.pow(10, 10); // some big number that's sure to be larger than our data range.
double minimum = bigNumber; // The minimum value to beat.
double distance = 0.0; // The current minimum value.
int sampleNumber = 0;
int cluster = 0;
boolean isStillMoving = true;
Data newData = null;
// Add in new data, one at a time, recalculating centroids with each new one.
while(dataSet.size() < TOTAL_DATA)
{
newData = new Data(SAMPLES[sampleNumber][0], SAMPLES[sampleNumber][1]);
dataSet.add(newData);
minimum = bigNumber;
for(int i = 0; i < NUM_CLUSTERS; i++)
{
distance = dist(newData, centroids.get(i));
if(distance < minimum){
minimum = distance;
cluster = i;
}
}
newData.cluster(cluster);
// calculate new centroids.
for(int i = 0; i < NUM_CLUSTERS; i++)
{
int totalX = 0;
int totalY = 0;
int totalInCluster = 0;
for(int j = 0; j < dataSet.size(); j++)
{
if(dataSet.get(j).cluster() == i){
totalX += dataSet.get(j).X();
totalY += dataSet.get(j).Y();
totalInCluster++;
}
}
if(totalInCluster > 0){
centroids.get(i).X(totalX / totalInCluster);
centroids.get(i).Y(totalY / totalInCluster);
}
}
sampleNumber++;
}
// Now, keep shifting centroids until equilibrium occurs.
while(isStillMoving)
{
// calculate new centroids.
for(int i = 0; i < NUM_CLUSTERS; i++)
{
int totalX = 0;
int totalY = 0;
int totalInCluster = 0;
for(int j = 0; j < dataSet.size(); j++)
{
if(dataSet.get(j).cluster() == i){
totalX += dataSet.get(j).X();
totalY += dataSet.get(j).Y();
totalInCluster++;
}
}
if(totalInCluster > 0){
centroids.get(i).X(totalX / totalInCluster);
centroids.get(i).Y(totalY / totalInCluster);
}
}
// Assign all data to the new centroids
isStillMoving = false;
for(int i = 0; i < dataSet.size(); i++)
{
Data tempData = dataSet.get(i);
minimum = bigNumber;
for(int j = 0; j < NUM_CLUSTERS; j++)
{
distance = dist(tempData, centroids.get(j));
if(distance < minimum){
minimum = distance;
cluster = j;
}
}
tempData.cluster(cluster);
if(tempData.cluster() != cluster){
tempData.cluster(cluster);
isStillMoving = true;
}
}
}
return;
}
// Calculate Euclidean Distance
private static double dist(Data d, Centroid c)
{
return Math.sqrt(Math.pow((c.Y() - d.Y()), 2) + Math.pow((c.X() - d.X()), 2));
}
private static class Data
{
private double mX = 0;
private double mY = 0;
private int mCluster = 0;
public Data()
{
return;
}
public Data(double x, double y)
{
this.X(x);
this.Y(y);
return;
}
public void X(double x)
{
this.mX = x;
return;
}
public double X()
{
return this.mX;
}
public void Y(double y)
{
this.mY = y;
return;
}
public double Y()
{
return this.mY;
}
public void cluster(int clusterNumber)
{
this.mCluster = clusterNumber;
return;
}
public int cluster()
{
return this.mCluster;
}
}
private static class Centroid
{
private double mX = 0.0;
private double mY = 0.0;
public Centroid()
{
return;
}
public Centroid(double newX, double newY)
{
this.mX = newX;
this.mY = newY;
return;
}
public void X(double newX)
{
this.mX = newX;
return;
}
public double X()
{
return this.mX;
}
public void Y(double newY)
{
this.mY = newY;
return;
}
public double Y()
{
return this.mY;
}
}
public static void main(String[] args)
{
initialize();
kMeanCluster();
// Print out clustering results.
for(int i = 0; i < NUM_CLUSTERS; i++)
{
System.out.println("Cluster " + i + " includes:");
for(int j = 0; j < TOTAL_DATA; j++)
{
if(dataSet.get(j).cluster() == i){
System.out.println(" (" + dataSet.get(j).X() + ", " + dataSet.get(j).Y() + ")");
}
} // j
System.out.println();
} // i
// Print out centroid results.
System.out.println("Centroids finalized at:");
for(int i = 0; i < NUM_CLUSTERS; i++)
{
System.out.println(" (" + centroids.get(i).X() + ", " + centroids.get(i).Y());
}
System.out.print("\n");
return;
}
}