Class AlgorithmKMeans

java.lang.Object
  Algorithm
      AlgorithmKMeans

All Implemented Interfaces:

java.lang.Runnable

public class AlgorithmKMeans
extends Algorithm

implements the K nearest neighbor algorithms

Field Summary

static long

RAND_SEED The random number generator

Constructor Summary

AlgorithmKMeans()

Method Summary

void	classify(DecisionRegion region) Classifies the data sets based on the k-means iterative algorithm
void	computeMeans(DecisionRegion region) Computes the means of the data sets after each iteraion
int	displayClusterError(int closest, java.util.Vector cluster, int id) determines the number of points in error, i.e, not classified by finding the distance of the datapoints from the closest of the vector set
void	generateMeans(int numMeans) Generates random initial guesses (means) for the data set
void	generatePool() Collects all the data points of all the data sets
int	getClosestSet(MyPoint mean) Determines the closest data sets to the cluster
java.util.Vector<MyPoint>	getDecisionRegion(java.util.Vector<MyPoint> vec) Computes the k-mean decision region - nearest neighbor algorithm
boolean	initialize() Overrides the initialize() method in the base class.
void	initializeKmeans() Initializes the kmean array with the original data sets
void	outputDecisionRegion() displays the decision region on the output panel
void	run() Implementation of the run function from the Runnable interface.

Methods inherited from class Algorithm

computeMeans, disableControl, enableControl, nextStep, prevStep, scaleToFitData, setDataPoints, setOutputPanel, setProcessBox

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

RAND_SEED

public static final long RAND_SEED

The random number generator

See Also:

Constant Field Values

Constructor Detail

AlgorithmKMeans

public AlgorithmKMeans()

Method Detail

initialize

public boolean initialize()

Overrides the initialize() method in the base class. Initializes member data and prepares for execution of first step. This method "resets" the algorithm.

Specified by:

initialize in class Algorithm

Returns:

Returns false

run

public void run()

Implementation of the run function from the Runnable interface. Determines what the current step is and calls the appropriate method.

Specified by:

run in interface java.lang.Runnable

Specified by:

run in class Algorithm

generatePool

public void generatePool()

Collects all the data points of all the data sets

generateMeans

public void generateMeans(int numMeans)

Generates random initial guesses (means) for the data set

Parameters:

numMeans - number of mean points

initializeKmeans

public void initializeKmeans()

Initializes the kmean array with the original data sets

classify

public void classify(DecisionRegion region)

Classifies the data sets based on the k-means iterative algorithm

Parameters:

region - - stored data sets from the classification

See Also:

DecisionRegion

computeMeans

public void computeMeans(DecisionRegion region)

Computes the means of the data sets after each iteraion

Parameters:

region - - classified data sets

See Also:

DecisionRegion

getClosestSet

public int getClosestSet(MyPoint mean)

Determines the closest data sets to the cluster

Parameters:

mean - mean point of the cluster

Returns:

closest data set to the cluster

See Also:

MyPoint

displayClusterError

public int displayClusterError(int closest,
                               java.util.Vector cluster,
                               int id)

determines the number of points in error, i.e, not classified by finding the distance of the datapoints from the closest of the vector set

Parameters:

closest - the data specifying which of the dataset is the closest.

cluster - the data points which form a closest cluster

id - unused in current implementation

Returns:

error number of data points which lie out of the classification

getDecisionRegion

public java.util.Vector<MyPoint> getDecisionRegion(java.util.Vector<MyPoint> vec)

Computes the k-mean decision region - nearest neighbor algorithm

Parameters:

vec - vector of initial guesses

Returns:

vector of desision region points

outputDecisionRegion

public void outputDecisionRegion()

displays the decision region on the output panel