mulan.classifier.meta
Class ConstrainedKMeans

java.lang.Object
  weka.clusterers.AbstractClusterer
      weka.clusterers.RandomizableClusterer
          mulan.classifier.meta.ConstrainedKMeans

All Implemented Interfaces:

Serializable, Cloneable, Clusterer, NumberOfClustersRequestable, CapabilitiesHandler, OptionHandler, Randomizable, RevisionHandler, WeightedInstancesHandler

public class ConstrainedKMeans
extends RandomizableClusterer
implements NumberOfClustersRequestable, WeightedInstancesHandler

Cluster data using the constrained k means algorithm

Version:

2012.07.16

Author:

Mark Hall, Eibe Frank, Grigorios Tsoumakas

See Also:

RandomizableClusterer, Serialized Form

Nested Class Summary

static class

ConstrainedKMeans.bucketInstance Class for representing an instance inside a bucket

Field Summary

Fields inherited from class weka.clusterers.RandomizableClusterer

m_Seed, m_SeedDefault

Constructor Summary

ConstrainedKMeans()
the default constructor

Method Summary

void	buildClusterer(Instances data) Generates a clusterer.
int	clusterInstance(Instance instance) Classifies a given instance.
Capabilities	getCapabilities() Returns default capabilities of the clusterer.
Instances	getClusterCentroids() Gets the the cluster centroids
int[][][]	getClusterNominalCounts() Returns for each cluster the frequency counts for the values of each nominal attribute
int[]	getClusterSizes() Gets the number of instances in each cluster
Instances	getClusterStandardDevs() Gets the standard deviations of the numeric attributes in each cluster
int	getNumClusters() gets the number of clusters to generate
double	getSquaredError() Gets the squared error for all clusters
String	globalInfo() Returns a string describing this clusterer
Enumeration	listOptions() Returns an enumeration describing the available options.
int	numberOfClusters() Returns the number of clusters.
String	numClustersTipText() Returns the tip text for this property
void	setMaxIterations(int x)
void	setNumClusters(int n) set the number of clusters to generate
String	toString() return a string describing this clusterer

Methods inherited from class weka.clusterers.RandomizableClusterer

getOptions, getSeed, seedTipText, setOptions, setSeed

Methods inherited from class weka.clusterers.AbstractClusterer

distributionForInstance, forName, getRevision, makeCopies, makeCopy, runClusterer

Methods inherited from class java.lang.Object

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

Constructor Detail

ConstrainedKMeans

public ConstrainedKMeans()

the default constructor

Method Detail

globalInfo

public String globalInfo()

Returns a string describing this clusterer

Returns:

a description of the evaluator suitable for displaying in the explorer/experimenter gui

getCapabilities

public Capabilities getCapabilities()

Returns default capabilities of the clusterer.

Specified by:

getCapabilities in interface Clusterer

Specified by:

getCapabilities in interface CapabilitiesHandler

Overrides:

getCapabilities in class AbstractClusterer

Returns:

the capabilities of this clusterer

setMaxIterations

public void setMaxIterations(int x)

Parameters:

x -

buildClusterer

public void buildClusterer(Instances data)
                    throws Exception

Generates a clusterer. Has to initialize all fields of the clusterer that are not being set via options.

Specified by:

buildClusterer in interface Clusterer

Specified by:

buildClusterer in class AbstractClusterer

Parameters:

data - set of instances serving as training data

Throws:

Exception - if the clusterer has not been generated successfully

clusterInstance

public int clusterInstance(Instance instance)
                    throws Exception

Classifies a given instance.

Specified by:

clusterInstance in interface Clusterer

Overrides:

clusterInstance in class AbstractClusterer

Parameters:

instance - the instance to be assigned to a cluster

Returns:

the number of the assigned cluster as an interger if the class is enumerated, otherwise the predicted value

Throws:

Exception - if instance could not be classified successfully

numberOfClusters

public int numberOfClusters()
                     throws Exception

Returns the number of clusters.

Specified by:

numberOfClusters in interface Clusterer

Specified by:

numberOfClusters in class AbstractClusterer

Returns:

the number of clusters generated for a training dataset.

Throws:

Exception - if number of clusters could not be returned successfully

listOptions

public Enumeration listOptions()

Returns an enumeration describing the available options.

Specified by:

listOptions in interface OptionHandler

Overrides:

listOptions in class RandomizableClusterer

Returns:

an enumeration of all the available options.

numClustersTipText

public String numClustersTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

setNumClusters

public void setNumClusters(int n)
                    throws Exception

set the number of clusters to generate

Specified by:

setNumClusters in interface NumberOfClustersRequestable

Parameters:

n - the number of clusters to generate

Throws:

Exception - if number of clusters is negative

getNumClusters

public int getNumClusters()

gets the number of clusters to generate

Returns:

the number of clusters to generate

toString

public String toString()

return a string describing this clusterer

Overrides:

toString in class Object

Returns:

a description of the clusterer as a string

getClusterCentroids

public Instances getClusterCentroids()

Gets the the cluster centroids

Returns:

the cluster centroids

getClusterStandardDevs

public Instances getClusterStandardDevs()

Gets the standard deviations of the numeric attributes in each cluster

Returns:

the standard deviations of the numeric attributes in each cluster

getClusterNominalCounts

public int[][][] getClusterNominalCounts()

Returns for each cluster the frequency counts for the values of each nominal attribute

Returns:

the counts

getSquaredError

public double getSquaredError()

Gets the squared error for all clusters

Returns:

the squared error

getClusterSizes

public int[] getClusterSizes()

Gets the number of instances in each cluster

Returns:

The number of instances in each cluster