jminhep.cluster

Class Partition

java.lang.Object

jminhep.cluster.Partition

public class Partition
extends java.lang.Object

Interface class to all clustering algorithms. Use it for JMInHEP embeded applications

Constructor Summary

Constructors

Constructor and Description
Partition(DataHolder data)

Method Summary

Modifier and Type	Method and Description
DataHolder	getCenters() Get cluster centers
double	getCompactness() Get compactness of the cluster configurations
double[][]	getMembership() Get membeship of each pint inside the cluster.
java.lang.String	getName() Get name for this clustering
int	getNclusters() get number of reconstructed clusters
int[]	getPoints() Get number of points accosiated with clusters
DataHolder	getSeeds() Get seeds
void	run(int Imode) Main method to run cluster algorithm
void	set(int Nclusters, double Eps, double Fuzzines, int Niterations) Set parameters for clustering
void	setProbab(double probability) Set probability for cluster accosiation

Methods inherited from class java.lang.Object

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

Constructor Detail

Partition

public Partition(DataHolder data)

Method Detail

set

public void set(int Nclusters,
                double Eps,
                double Fuzzines,
                int Niterations)

Set parameters for clustering

Parameters:

Nclusters - - number of clusters

Eps - - precision of clustering

Fuzzines - - fuzziness (only for cmeans algorithms, for others just dummy constant)

Niterations - - Max number of iterations

run

public void run(int Imode)

Main method to run cluster algorithm

Parameters:

Imode - - cluster mode (int). Find the correct mode from the example or by running JMinHEP in GUI mode. After each clustering, Imode value is shown in the status bar.

getName

public java.lang.String getName()

Get name for this clustering

Returns:

Description of the cluster mode

getCompactness

public double getCompactness()

Get compactness of the cluster configurations

Returns:

compactness

getCenters

public DataHolder getCenters()

Get cluster centers

Returns:

DataHolder with cluster centers

getSeeds

public DataHolder getSeeds()

Get seeds

Returns:

DataHolder with seed values (only for k-means algorithms)

getPoints

public int[] getPoints()

Get number of points accosiated with clusters

Returns:

number of points in each cluster (int[])

getMembership

public double[][] getMembership()

Get membeship of each pint inside the cluster. Applies for fuzzy algorithm.

Returns:

membership of each point.

setProbab

public void setProbab(double probability)

Set probability for cluster accosiation

Parameters:

probability - Active only for cmeans (and only for representative purpose)

getNclusters

public int getNclusters()

get number of reconstructed clusters

Returns:

number of reconstructed clusters