weka.clusterers

Class CascadeSimpleKMeans

java.lang.Object

weka.clusterers.AbstractClusterer

weka.clusterers.RandomizableClusterer

weka.clusterers.CascadeSimpleKMeans

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, weka.clusterers.Clusterer, weka.core.CapabilitiesHandler, weka.core.CapabilitiesIgnorer, weka.core.CommandlineRunnable, weka.core.OptionHandler, weka.core.Randomizable, weka.core.RevisionHandler, weka.core.TechnicalInformationHandler

public class CascadeSimpleKMeans
extends weka.clusterers.RandomizableClusterer
implements weka.clusterers.Clusterer, weka.core.TechnicalInformationHandler

cascade simple k means, selects the best k according to calinski-harabasz criterion analogous to: http://cc.oulu.fi/~jarioksa/softhelp/vegan/html/cascadeKM.html see Calinski, T. and J. Harabasz. 1974. A dendrite method for cluster analysis. Commun. Stat. 3: 1-27. quoted in German: http://books.google.com/books?id=-f9Ox0p1-D4C&lpg=PA394&ots=SV3JfRIkQn&dq=Calinski%20and%20Harabasz&hl=de&pg=PA394#v=onepage&q&f=false

Author:

Martin Gutlein (martin.guetlein@gmail.com)

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
CascadeSimpleKMeans()

Method Summary

Modifier and Type	Method and Description
void	buildClusterer(weka.core.Instances data)
int	clusterInstance(weka.core.Instance instance)
java.lang.String	distanceFunctionTipText()
double[]	distributionForInstance(weka.core.Instance instance)
weka.core.Capabilities	getCapabilities()
weka.core.DistanceFunction	getDistanceFunction()
boolean	getInitializeUsingKMeansPlusPlusMethod() Get whether to initialize using the probabilistic farthest first like method of the k-means++ algorithm (rather than the standard random selection of initial cluster centers).
int	getMaxIterations()
int	getMaxNumClusters()
int	getMinNumClusters()
java.lang.String[]	getOptions() Gets the current settings of SimpleKMeans.
int	getRestarts()
java.lang.String	getRevision() Returns the revision string.
weka.core.TechnicalInformation	getTechnicalInformation()
java.lang.String	globalInfo() Returns a string describing this clusterer.
java.lang.String	initializeUsingKMeansPlusPlusMethodTipText() Returns the tip text for this property.
boolean	isManuallySelectNumClusters()
boolean	isPrintDebug()
java.util.Enumeration	listOptions() Returns an enumeration describing the available options.
static void	main(java.lang.String[] args) Main method for executing this class.
java.lang.String	manuallySelectNumClustersTipText()
java.lang.String	maxIterationsTipText()
java.lang.String	maxNumClustersTipText()
java.lang.String	minNumClustersTipText()
int	numberOfClusters()
java.lang.String	printDebugTipText()
java.lang.String	restartsTipText()
void	setDistanceFunction(weka.core.DistanceFunction distanceFunction)
void	setInitializeUsingKMeansPlusPlusMethod(boolean k) Set whether to initialize using the probabilistic farthest first like method of the k-means++ algorithm (rather than the standard random selection of initial cluster centers).
void	setManuallySelectNumClusters(boolean manuallySelectNumClusters)
void	setMaxIterations(int maxIterations)
void	setMaxNumClusters(int maxNumClusters)
void	setMinNumClusters(int minNumClusters)
void	setOptions(java.lang.String[] options) Parses a given list of options.
void	setPrintDebug(boolean printDebug)
void	setRestarts(int restarts)
java.lang.String	toString()

Methods inherited from class weka.clusterers.RandomizableClusterer

getSeed, seedTipText, setSeed

Methods inherited from class weka.clusterers.AbstractClusterer

debugTipText, doNotCheckCapabilitiesTipText, forName, getDebug, getDoNotCheckCapabilities, makeCopies, makeCopy, postExecution, preExecution, run, runClusterer, setDebug, setDoNotCheckCapabilities

Methods inherited from class java.lang.Object

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

Methods inherited from interface weka.core.OptionHandler

makeCopy

Constructor Detail

CascadeSimpleKMeans

public CascadeSimpleKMeans()

Method Detail

getTechnicalInformation

public weka.core.TechnicalInformation getTechnicalInformation()

Specified by:

getTechnicalInformation in interface weka.core.TechnicalInformationHandler

toString

public java.lang.String toString()

Overrides:

toString in class java.lang.Object

globalInfo

public java.lang.String globalInfo()

Returns a string describing this clusterer.

Returns:

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

buildClusterer

public void buildClusterer(weka.core.Instances data)
                    throws java.lang.Exception

Specified by:

buildClusterer in interface weka.clusterers.Clusterer

Specified by:

buildClusterer in class weka.clusterers.AbstractClusterer

Throws:

java.lang.Exception

clusterInstance

public int clusterInstance(weka.core.Instance instance)
                    throws java.lang.Exception

Specified by:

clusterInstance in interface weka.clusterers.Clusterer

Overrides:

clusterInstance in class weka.clusterers.AbstractClusterer

Throws:

java.lang.Exception

distributionForInstance

public double[] distributionForInstance(weka.core.Instance instance)
                                 throws java.lang.Exception

Specified by:

distributionForInstance in interface weka.clusterers.Clusterer

Overrides:

distributionForInstance in class weka.clusterers.AbstractClusterer

Throws:

java.lang.Exception

numberOfClusters

public int numberOfClusters()
                     throws java.lang.Exception

Specified by:

numberOfClusters in interface weka.clusterers.Clusterer

Specified by:

numberOfClusters in class weka.clusterers.AbstractClusterer

Throws:

java.lang.Exception

getCapabilities

public weka.core.Capabilities getCapabilities()

Specified by:

getCapabilities in interface weka.clusterers.Clusterer

Specified by:

getCapabilities in interface weka.core.CapabilitiesHandler

Overrides:

getCapabilities in class weka.clusterers.AbstractClusterer

minNumClustersTipText

public java.lang.String minNumClustersTipText()

getMinNumClusters

public int getMinNumClusters()

setMinNumClusters

public void setMinNumClusters(int minNumClusters)

maxNumClustersTipText

public java.lang.String maxNumClustersTipText()

getMaxNumClusters

public int getMaxNumClusters()

setMaxNumClusters

public void setMaxNumClusters(int maxNumClusters)

restartsTipText

public java.lang.String restartsTipText()

getRestarts

public int getRestarts()

setRestarts

public void setRestarts(int restarts)

printDebugTipText

public java.lang.String printDebugTipText()

isPrintDebug

public boolean isPrintDebug()

setPrintDebug

public void setPrintDebug(boolean printDebug)

distanceFunctionTipText

public java.lang.String distanceFunctionTipText()

getDistanceFunction

public weka.core.DistanceFunction getDistanceFunction()

setDistanceFunction

public void setDistanceFunction(weka.core.DistanceFunction distanceFunction)

maxIterationsTipText

public java.lang.String maxIterationsTipText()

getMaxIterations

public int getMaxIterations()

setMaxIterations

public void setMaxIterations(int maxIterations)

manuallySelectNumClustersTipText

public java.lang.String manuallySelectNumClustersTipText()

isManuallySelectNumClusters

public boolean isManuallySelectNumClusters()

setManuallySelectNumClusters

public void setManuallySelectNumClusters(boolean manuallySelectNumClusters)

initializeUsingKMeansPlusPlusMethodTipText

public java.lang.String initializeUsingKMeansPlusPlusMethodTipText()

Returns the tip text for this property.

Returns:

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

setInitializeUsingKMeansPlusPlusMethod

public void setInitializeUsingKMeansPlusPlusMethod(boolean k)

Set whether to initialize using the probabilistic farthest first like method of the k-means++ algorithm (rather than the standard random selection of initial cluster centers).

Parameters:

k - true if the k-means++ method is to be used to select initial cluster centers.

getInitializeUsingKMeansPlusPlusMethod

public boolean getInitializeUsingKMeansPlusPlusMethod()

Get whether to initialize using the probabilistic farthest first like method of the k-means++ algorithm (rather than the standard random selection of initial cluster centers).

Returns:

true if the k-means++ method is to be used to select initial cluster centers.

listOptions

public java.util.Enumeration listOptions()

Returns an enumeration describing the available options.

Specified by:

listOptions in interface weka.core.OptionHandler

Overrides:

listOptions in class weka.clusterers.RandomizableClusterer

Returns:

an enumeration of all the available options.

setOptions

public void setOptions(java.lang.String[] options)
                throws java.lang.Exception

Parses a given list of options.

Valid options are:

 -N <num>
  number of clusters.
  (default 2).

 -P
  Initialize using the k-means++ method.
 

 -V
  Display std. deviations for centroids.
 

 -M
  Replace missing values with mean/mode.
 

 -A <classname and options>
  Distance function to use.
  (default: weka.core.EuclideanDistance)

 -I <num>
  Maximum number of iterations.
 

 -O
  Preserve order of instances.
 

 -fast
  Enables faster distance calculations, using cut-off values.
  Disables the calculation/output of squared errors/distances.
 

 -S <num>
  Random number seed.
  (default 10)

Specified by:

setOptions in interface weka.core.OptionHandler

Overrides:

setOptions in class weka.clusterers.RandomizableClusterer

Parameters:

options - the list of options as an array of strings

Throws:

java.lang.Exception - if an option is not supported

getOptions

public java.lang.String[] getOptions()

Gets the current settings of SimpleKMeans.

Specified by:

getOptions in interface weka.core.OptionHandler

Overrides:

getOptions in class weka.clusterers.RandomizableClusterer

Returns:

an array of strings suitable for passing to setOptions()

getRevision

public java.lang.String getRevision()

Returns the revision string.

Specified by:

getRevision in interface weka.core.RevisionHandler

Overrides:

getRevision in class weka.clusterers.AbstractClusterer

Returns:

the revision

main

public static void main(java.lang.String[] args)

Main method for executing this class.

Parameters:

args - use -h to list all parameters