XMeans

java.lang.Object
- weka.clusterers.AbstractClusterer
- - weka.clusterers.RandomizableClusterer
  - - weka.clusterers.XMeans

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 XMeans
extends weka.clusterers.RandomizableClusterer
implements weka.core.TechnicalInformationHandler

Cluster data using the X-means algorithm.

X-Means is K-Means extended by an Improve-Structure part In this part of the algorithm the centers are attempted to be split in its region. The decision between the children of each center and itself is done comparing the BIC-values of the two structures.

For more information see:

Dan Pelleg, Andrew W. Moore: X-means: Extending K-means with Efficient Estimation of the Number of Clusters. In: Seventeenth International Conference on Machine Learning, 727-734, 2000.

BibTeX:

 @inproceedings{Pelleg2000,
    author = {Dan Pelleg and Andrew W. Moore},
    booktitle = {Seventeenth International Conference on Machine Learning},
    pages = {727-734},
    publisher = {Morgan Kaufmann},
    title = {X-means: Extending K-means with Efficient Estimation of the Number of Clusters},
    year = {2000}
 }

Valid options are:

 -I <num>
  maximum number of overall iterations
  (default 1).

 -M <num>
  maximum number of iterations in the kMeans loop in
  the Improve-Parameter part 
  (default 1000).

 -J <num>
  maximum number of iterations in the kMeans loop
  for the splitted centroids in the Improve-Structure part 
  (default 1000).

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

 -H <num>
  maximum number of clusters
  (default 4).

 -B <value>
  distance value for binary attributes
  (default 1.0).

 -use-kdtree
  Uses the KDTree internally
  (default no).

 -K <KDTree class specification>
  Full class name of KDTree class to use, followed
  by scheme options.
  eg: "weka.core.neighboursearch.kdtrees.KDTree -P"
  (default no KDTree class used).

 -C <value>
  cutoff factor, takes the given percentage of the splitted 
  centroids if none of the children win
  (default 0.0).

 -D <distance function class specification>
  Full class name of Distance function class to use, followed
  by scheme options.
  (default weka.core.EuclideanDistance).

 -N <file name>
  file to read starting centers from (ARFF format).

 -O <file name>
  file to write centers to (ARFF format).

 -U <int>
  The debug level.
  (default 0)

 -Y <file name>
  The debug vectors file.

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

Version:: $Revision: 10390 $
Author:: Gabi Schmidberger (gabi@cs.waikato.ac.nz), Mark Hall (mhall@cs.waikato.ac.nz), Malcolm Ware (mfw4@cs.waikato.ac.nz)
See Also:: RandomizableClusterer, Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`static int`	`D_CONVCHCLOSER` have a closer look at converge children.
`static int`	`D_CURR` for current debug.
`static int`	`D_FOLLOWSPLIT` follows the splitting of the centers.
`static int`	`D_GENERAL` general debugging.
`static int`	`D_ITERCOUNT` follow iterations.
`static int`	`D_KDTREE` check on kdtree.
`static int`	`D_METH_MISUSE` functions were maybe misused.
`static int`	`D_PRINTCENTERS` print the centers.
`static int`	`D_RANDOMVECTOR` check on random vectors.
`boolean`	`m_CurrDebugFlag` Flag: I'm debugging.
`static int`	`R_HIGH` Index in ranges for HIGH.
`static int`	`R_LOW` Index in ranges for LOW.
`static int`	`R_WIDTH` Index in ranges for WIDTH.

Constructor Summary

Constructors
Constructor and Description

XMeans()
the default constructor.

Constructors
Constructor and Description
`XMeans()` the default constructor.

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`java.lang.String`	`binValueTipText()` Returns the tip text for this property.
`void`	`buildClusterer(weka.core.Instances data)` Generates the X-Means clusterer.
`boolean`	`checkForNominalAttributes(weka.core.Instances data)` Checks for nominal attributes in the dataset.
`int`	`clusterInstance(weka.core.Instance instance)` Classifies a given instance.
`java.lang.String`	`cutOffFactorTipText()` Returns the tip text for this property.
`java.lang.String`	`debugLevelTipText()` Returns the tip text for this property.
`java.lang.String`	`debugVectorsFileTipText()` Returns the tip text for this property.
`java.lang.String`	`distanceFTipText()` Returns the tip text for this property.
`double`	`getBinValue()` Gets value that represents true in a new numeric attribute.
`weka.core.Capabilities`	`getCapabilities()` Returns default capabilities of the clusterer.
`weka.core.Instances`	`getClusterCenters()` Return the centers of the clusters as an Instances object
`double`	`getCutOffFactor()` Gets the cutoff factor.
`int`	`getDebugLevel()` Gets the debug level.
`java.io.File`	`getDebugVectorsFile()` Gets the file name for a file that has the random vectors stored.
`weka.core.DistanceFunction`	`getDistanceF()` Gets the distance function.
`java.io.File`	`getInputCenterFile()` Gets the file to read the list of centers from.
`weka.core.neighboursearch.KDTree`	`getKDTree()` Gets the KDTree class.
`int`	`getMaxIterations()` Gets the maximum number of iterations.
`int`	`getMaxKMeans()` Gets the maximum number of iterations in KMeans.
`int`	`getMaxKMeansForChildren()` Gets the maximum number of iterations in KMeans.
`int`	`getMaxNumClusters()` Gets the maximum number of clusters to generate.
`int`	`getMinNumClusters()` Gets the minimum number of clusters to generate.
`weka.core.Instance`	`getNextDebugVectorsInstance(weka.core.Instances model)` Read an instance from debug vectors file.
`java.lang.String[]`	`getOptions()` Gets the current settings of SimpleKMeans.
`java.io.File`	`getOutputCenterFile()` Gets the file to write the list of centers to.
`java.lang.String`	`getRevision()` Returns the revision string.
`weka.core.TechnicalInformation`	`getTechnicalInformation()` Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
`boolean`	`getUseKDTree()` Gets whether the KDTree is used or not.
`java.lang.String`	`globalInfo()` Returns a string describing this clusterer.
`void`	`initDebugVectorsInput()` Initialises the debug vector input.
`java.lang.String`	`inputCenterFileTipText()` Returns the tip text for this property.
`java.lang.String`	`KDTreeTipText()` Returns the tip text for this property.
`java.util.Enumeration<weka.core.Option>`	`listOptions()` Returns an enumeration describing the available options.
`static void`	`main(java.lang.String[] argv)` Main method for testing this class.
`java.lang.String`	`maxIterationsTipText()` Returns the tip text for this property.
`java.lang.String`	`maxKMeansForChildrenTipText()` Returns the tip text for this property.
`java.lang.String`	`maxKMeansTipText()` Returns the tip text for this property.
`java.lang.String`	`maxNumClustersTipText()` Returns the tip text for this property.
`java.lang.String`	`minNumClustersTipText()` Returns the tip text for this property.
`int`	`numberOfClusters()` Returns the number of clusters.
`java.lang.String`	`outputCenterFileTipText()` Returns the tip text for this property.
`void`	`setBinValue(double value)` Sets the distance value between true and false of binary attributes.
`void`	`setCutOffFactor(double i)` Sets a new cutoff factor.
`void`	`setDebugLevel(int d)` Sets the debug level.
`void`	`setDebugVectorsFile(java.io.File value)` Sets the file that has the random vectors stored.
`void`	`setDistanceF(weka.core.DistanceFunction distanceF)` gets the "binary" distance value.
`void`	`setInputCenterFile(java.io.File value)` Sets the file to read the list of centers from.
`void`	`setKDTree(weka.core.neighboursearch.KDTree k)` Sets the KDTree class.
`void`	`setMaxIterations(int i)` Sets the maximum number of iterations to perform.
`void`	`setMaxKMeans(int i)` Set the maximum number of iterations to perform in KMeans.
`void`	`setMaxKMeansForChildren(int i)` Sets the maximum number of iterations KMeans that is performed on the child centers.
`void`	`setMaxNumClusters(int n)` Sets the maximum number of clusters to generate.
`void`	`setMinNumClusters(int n)` Sets the minimum number of clusters to generate.
`void`	`setOptions(java.lang.String[] options)` Parses a given list of options.
`void`	`setOutputCenterFile(java.io.File value)` Sets file to write the list of centers to.
`void`	`setUseKDTree(boolean value)` Sets whether to use the KDTree or not.
`java.lang.String`	`toString()` Return a string describing this clusterer.
`java.lang.String`	`useKDTreeTipText()` Returns the tip text for this property.

Methods inherited from class weka.clusterers.RandomizableClusterer
getSeed, seedTipText, setSeed

Methods inherited from class weka.clusterers.AbstractClusterer
debugTipText, distributionForInstance, 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

- Field Detail
  - R_LOW
```
public static int R_LOW
```
    Index in ranges for LOW.
  - R_HIGH
```
public static int R_HIGH
```
    Index in ranges for HIGH.
  - R_WIDTH
```
public static int R_WIDTH
```
    Index in ranges for WIDTH.
  - D_PRINTCENTERS
```
public static int D_PRINTCENTERS
```
    print the centers.
  - D_FOLLOWSPLIT
```
public static int D_FOLLOWSPLIT
```
    follows the splitting of the centers.
  - D_CONVCHCLOSER
```
public static int D_CONVCHCLOSER
```
    have a closer look at converge children.
  - D_RANDOMVECTOR
```
public static int D_RANDOMVECTOR
```
    check on random vectors.
  - D_KDTREE
```
public static int D_KDTREE
```
    check on kdtree.
  - D_ITERCOUNT
```
public static int D_ITERCOUNT
```
    follow iterations.
  - D_METH_MISUSE
```
public static int D_METH_MISUSE
```
    functions were maybe misused.
  - D_CURR
```
public static int D_CURR
```
    for current debug.
  - D_GENERAL
```
public static int D_GENERAL
```
    general debugging.
  - m_CurrDebugFlag
```
public boolean m_CurrDebugFlag
```
    Flag: I'm debugging.
- Constructor Detail
  - XMeans
```
public XMeans()
```
    the default constructor.
- Method Detail
  - 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
  - getTechnicalInformation
```
public weka.core.TechnicalInformation getTechnicalInformation()
```
    Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
    
    Specified by:
    
    getTechnicalInformation in interface weka.core.TechnicalInformationHandler
    
    Returns:
    
    the technical information about this class
  - getCapabilities
```
public weka.core.Capabilities getCapabilities()
```
    Returns default capabilities of the clusterer.
    
    Specified by:
    
    getCapabilities in interface weka.clusterers.Clusterer
    
    Specified by:
    
    getCapabilities in interface weka.core.CapabilitiesHandler
    
    Overrides:
    
    getCapabilities in class weka.clusterers.AbstractClusterer
    
    Returns:
    
    the capabilities of this clusterer
  - buildClusterer
```
public void buildClusterer(weka.core.Instances data)
                    throws java.lang.Exception
```
    Generates the X-Means clusterer.
    
    Specified by:
    
    buildClusterer in interface weka.clusterers.Clusterer
    
    Specified by:
    
    buildClusterer in class weka.clusterers.AbstractClusterer
    
    Parameters:
    
    data - set of instances serving as training data
    
    Throws:
    
    java.lang.Exception - if the clusterer has not been generated successfully
  - checkForNominalAttributes
```
public boolean checkForNominalAttributes(weka.core.Instances data)
```
    Checks for nominal attributes in the dataset. Class attribute is ignored.
    
    Parameters:
    
    data - the data to check
    
    Returns:
    
    false if no nominal attributes are present
  - clusterInstance
```
public int clusterInstance(weka.core.Instance instance)
                    throws java.lang.Exception
```
    Classifies a given instance.
    
    Specified by:
    
    clusterInstance in interface weka.clusterers.Clusterer
    
    Overrides:
    
    clusterInstance in class weka.clusterers.AbstractClusterer
    
    Parameters:
    
    instance - the instance to be assigned to a cluster
    
    Returns:
    
    the number of the assigned cluster as an integer if the class is enumerated, otherwise the predicted value
    
    Throws:
    
    java.lang.Exception - if instance could not be classified successfully
  - numberOfClusters
```
public int numberOfClusters()
```
    Returns the number of clusters.
    
    Specified by:
    
    numberOfClusters in interface weka.clusterers.Clusterer
    
    Specified by:
    
    numberOfClusters in class weka.clusterers.AbstractClusterer
    
    Returns:
    
    the number of clusters generated for a training dataset.
  - listOptions
```
public java.util.Enumeration<weka.core.Option> 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
  - minNumClustersTipText
```
public java.lang.String minNumClustersTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property
  - setMinNumClusters
```
public void setMinNumClusters(int n)
```
    Sets the minimum number of clusters to generate.
    
    Parameters:
    
    n - the minimum number of clusters to generate
  - getMinNumClusters
```
public int getMinNumClusters()
```
    Gets the minimum number of clusters to generate.
    
    Returns:
    
    the minimum number of clusters to generate
  - maxNumClustersTipText
```
public java.lang.String maxNumClustersTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property
  - setMaxNumClusters
```
public void setMaxNumClusters(int n)
```
    Sets the maximum number of clusters to generate.
    
    Parameters:
    
    n - the maximum number of clusters to generate
  - getMaxNumClusters
```
public int getMaxNumClusters()
```
    Gets the maximum number of clusters to generate.
    
    Returns:
    
    the maximum number of clusters to generate
  - maxIterationsTipText
```
public java.lang.String maxIterationsTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property
  - setMaxIterations
```
public void setMaxIterations(int i)
                      throws java.lang.Exception
```
    Sets the maximum number of iterations to perform.
    
    Parameters:
    
    i - the number of iterations
    
    Throws:
    
    java.lang.Exception - if i is less than 1
  - getMaxIterations
```
public int getMaxIterations()
```
    Gets the maximum number of iterations.
    
    Returns:
    
    the number of iterations
  - maxKMeansTipText
```
public java.lang.String maxKMeansTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property
  - setMaxKMeans
```
public void setMaxKMeans(int i)
```
    Set the maximum number of iterations to perform in KMeans.
    
    Parameters:
    
    i - the number of iterations
  - getMaxKMeans
```
public int getMaxKMeans()
```
    Gets the maximum number of iterations in KMeans.
    
    Returns:
    
    the number of iterations
  - maxKMeansForChildrenTipText
```
public java.lang.String maxKMeansForChildrenTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property
  - setMaxKMeansForChildren
```
public void setMaxKMeansForChildren(int i)
```
    Sets the maximum number of iterations KMeans that is performed on the child centers.
    
    Parameters:
    
    i - the number of iterations
  - getMaxKMeansForChildren
```
public int getMaxKMeansForChildren()
```
    Gets the maximum number of iterations in KMeans.
    
    Returns:
    
    the number of iterations
  - cutOffFactorTipText
```
public java.lang.String cutOffFactorTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property
  - setCutOffFactor
```
public void setCutOffFactor(double i)
```
    Sets a new cutoff factor.
    
    Parameters:
    
    i - the new cutoff factor
  - getCutOffFactor
```
public double getCutOffFactor()
```
    Gets the cutoff factor.
    
    Returns:
    
    the cutoff factor
  - binValueTipText
```
public java.lang.String binValueTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getBinValue
```
public double getBinValue()
```
    Gets value that represents true in a new numeric attribute. (False is always represented by 0.0.)
    
    Returns:
    
    the value that represents true in a new numeric attribute
  - setBinValue
```
public void setBinValue(double value)
```
    Sets the distance value between true and false of binary attributes. and "same" and "different" of nominal attributes
    
    Parameters:
    
    value - the distance
  - distanceFTipText
```
public java.lang.String distanceFTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setDistanceF
```
public void setDistanceF(weka.core.DistanceFunction distanceF)
```
    gets the "binary" distance value.
    
    Parameters:
    
    distanceF - the distance function with all options set
  - getDistanceF
```
public weka.core.DistanceFunction getDistanceF()
```
    Gets the distance function.
    
    Returns:
    
    the distance function
  - debugVectorsFileTipText
```
public java.lang.String debugVectorsFileTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setDebugVectorsFile
```
public void setDebugVectorsFile(java.io.File value)
```
    Sets the file that has the random vectors stored. Only used for debugging reasons.
    
    Parameters:
    
    value - the file to read the random vectors from
  - getDebugVectorsFile
```
public java.io.File getDebugVectorsFile()
```
    Gets the file name for a file that has the random vectors stored. Only used for debugging purposes.
    
    Returns:
    
    the file to read the vectors from
  - initDebugVectorsInput
```
public void initDebugVectorsInput()
                           throws java.lang.Exception
```
    Initialises the debug vector input.
    
    Throws:
    
    java.lang.Exception - if there is error opening the debug input file.
  - getNextDebugVectorsInstance
```
public weka.core.Instance getNextDebugVectorsInstance(weka.core.Instances model)
                                               throws java.lang.Exception
```
    Read an instance from debug vectors file.
    
    Parameters:
    
    model - the data model for the instance.
    
    Returns:
    
    the next debug vector.
    
    Throws:
    
    java.lang.Exception - if there are no debug vector in m_DebugVectors.
  - inputCenterFileTipText
```
public java.lang.String inputCenterFileTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setInputCenterFile
```
public void setInputCenterFile(java.io.File value)
```
    Sets the file to read the list of centers from.
    
    Parameters:
    
    value - the file to read centers from
  - getInputCenterFile
```
public java.io.File getInputCenterFile()
```
    Gets the file to read the list of centers from.
    
    Returns:
    
    the file to read the centers from
  - outputCenterFileTipText
```
public java.lang.String outputCenterFileTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setOutputCenterFile
```
public void setOutputCenterFile(java.io.File value)
```
    Sets file to write the list of centers to.
    
    Parameters:
    
    value - file to write centers to
  - getOutputCenterFile
```
public java.io.File getOutputCenterFile()
```
    Gets the file to write the list of centers to.
    
    Returns:
    
    filename of the file to write centers to
  - KDTreeTipText
```
public java.lang.String KDTreeTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setKDTree
```
public void setKDTree(weka.core.neighboursearch.KDTree k)
```
    Sets the KDTree class.
    
    Parameters:
    
    k - a KDTree object with all options set
  - getKDTree
```
public weka.core.neighboursearch.KDTree getKDTree()
```
    Gets the KDTree class.
    
    Returns:
    
    the configured KDTree
  - useKDTreeTipText
```
public java.lang.String useKDTreeTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setUseKDTree
```
public void setUseKDTree(boolean value)
```
    Sets whether to use the KDTree or not.
    
    Parameters:
    
    value - if true the KDTree is used
  - getUseKDTree
```
public boolean getUseKDTree()
```
    Gets whether the KDTree is used or not.
    
    Returns:
    
    true if KDTrees are used
  - debugLevelTipText
```
public java.lang.String debugLevelTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setDebugLevel
```
public void setDebugLevel(int d)
```
    Sets the debug level. debug level = 0, means no output
    
    Parameters:
    
    d - debuglevel
  - getDebugLevel
```
public int getDebugLevel()
```
    Gets the debug level.
    
    Returns:
    
    debug level
  - setOptions
```
public void setOptions(java.lang.String[] options)
                throws java.lang.Exception
```
    Parses a given list of options.
    Valid options are:
```
 -I <num>
  maximum number of overall iterations
  (default 1).
 
```
```
 -M <num>
  maximum number of iterations in the kMeans loop in
  the Improve-Parameter part 
  (default 1000).
 
```
```
 -J <num>
  maximum number of iterations in the kMeans loop
  for the splitted centroids in the Improve-Structure part 
  (default 1000).
 
```
```
 -L <num>
  minimum number of clusters
  (default 2).
 
```
```
 -H <num>
  maximum number of clusters
  (default 4).
 
```
```
 -B <value>
  distance value for binary attributes
  (default 1.0).
 
```
```
 -use-kdtree
  Uses the KDTree internally
  (default no).
 
```
```
 -K <KDTree class specification>
  Full class name of KDTree class to use, followed
  by scheme options.
  eg: "weka.core.neighboursearch.kdtrees.KDTree -P"
  (default no KDTree class used).
 
```
```
 -C <value>
  cutoff factor, takes the given percentage of the splitted 
  centroids if none of the children win
  (default 0.0).
 
```
```
 -D <distance function class specification>
  Full class name of Distance function class to use, followed
  by scheme options.
  (default weka.core.EuclideanDistance).
 
```
```
 -N <file name>
  file to read starting centers from (ARFF format).
 
```
```
 -O <file name>
  file to write centers to (ARFF format).
 
```
```
 -U <int>
  The debug level.
  (default 0)
 
```
```
 -Y <file name>
  The debug vectors file.
 
```
```
 -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
  - toString
```
public java.lang.String toString()
```
    Return a string describing this clusterer.
    
    Overrides:
    
    toString in class java.lang.Object
    
    Returns:
    
    a description of the clusterer as a string
  - getClusterCenters
```
public weka.core.Instances getClusterCenters()
```
    Return the centers of the clusters as an Instances object
    
    Returns:
    
    the cluster centers.
  - 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[] argv)
```
    Main method for testing this class.
    
    Parameters:
    
    argv - should contain options

Class XMeans

Field Summary

Constructor Summary

Method Summary

Methods inherited from class weka.clusterers.RandomizableClusterer

Methods inherited from class weka.clusterers.AbstractClusterer

Methods inherited from class java.lang.Object

Methods inherited from interface weka.core.OptionHandler

Field Detail

R_LOW

R_HIGH

R_WIDTH

D_PRINTCENTERS

D_FOLLOWSPLIT

D_CONVCHCLOSER

D_RANDOMVECTOR

D_KDTREE

D_ITERCOUNT

D_METH_MISUSE

D_CURR

D_GENERAL

m_CurrDebugFlag

Constructor Detail

XMeans

Method Detail

globalInfo

getTechnicalInformation

getCapabilities

buildClusterer

checkForNominalAttributes

clusterInstance

numberOfClusters

listOptions

minNumClustersTipText

setMinNumClusters

getMinNumClusters

maxNumClustersTipText

setMaxNumClusters

getMaxNumClusters

maxIterationsTipText

setMaxIterations

getMaxIterations

maxKMeansTipText

setMaxKMeans

getMaxKMeans

maxKMeansForChildrenTipText

setMaxKMeansForChildren

getMaxKMeansForChildren

cutOffFactorTipText

setCutOffFactor

getCutOffFactor

binValueTipText

getBinValue

setBinValue

distanceFTipText

setDistanceF

getDistanceF

debugVectorsFileTipText

setDebugVectorsFile

getDebugVectorsFile

initDebugVectorsInput

getNextDebugVectorsInstance

inputCenterFileTipText

setInputCenterFile

getInputCenterFile

outputCenterFileTipText

setOutputCenterFile

getOutputCenterFile

KDTreeTipText

setKDTree

getKDTree

useKDTreeTipText

setUseKDTree

getUseKDTree

debugLevelTipText

setDebugLevel

getDebugLevel

setOptions

getOptions

toString