XMeans (ecological-engine 1.10.1-SNAPSHOT API)

java.lang.Object
- weka.clusterers.AbstractClusterer
- - weka.clusterers.RandomizableClusterer
  - - org.gcube.dataanalysis.ecoengine.clustering.XMeans

All Implemented Interfaces:: Serializable, Cloneable, weka.clusterers.Clusterer, weka.core.CapabilitiesHandler, 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: 8109 $
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.
`protected double`	`m_Bic` BIC-Score of the current model.
`protected double`	`m_BinValue` Distance value between true and false of binary attributes and "same" and "different" of nominal attributes (default = 1.0).
`protected Reader`	`m_CenterInput` input file for the cluster centers.
`protected PrintWriter`	`m_CenterOutput` output file for the cluster centers.
`protected int[]`	`m_ClusterAssignments` temporary variable holding cluster assignments while iterating.
`protected weka.core.Instances`	`m_ClusterCenters` cluster centers.
`boolean`	`m_CurrDebugFlag` Flag: I'm debugging.
`protected double`	`m_CutOffFactor` cutoff factor - percentage of splits done in Improve-Structure part only relevant, if all children lost.
`protected int`	`m_DebugLevel` level of debug output, 0 is no output.
`protected weka.core.Instances`	`m_DebugVectors` all the debug vectors.
`protected File`	`m_DebugVectorsFile` file name of the input file for the random vectors.
`protected int`	`m_DebugVectorsIndex` the index for the current debug vector.
`protected Reader`	`m_DebugVectorsInput` input file for the random vectors --> USED FOR DEBUGGING.
`protected weka.core.DistanceFunction`	`m_DistanceF` the distance function used.
`protected File`	`m_InputCenterFile` file name of the output file for the cluster centers.
`protected weka.core.Instances`	`m_Instances` training instances.
`protected int`	`m_IterationCount` counts iterations done in main loop.
`protected weka.core.neighboursearch.KDTree`	`m_KDTree` KDTrees class if KDTrees are used.
`protected int`	`m_KMeansStopped` counter to say how often kMeans was stopped by loop counter.
`protected int`	`m_MaxIterations` maximum overall iterations.
`protected int`	`m_MaxKMeans` maximum iterations to perform Kmeans part if negative, iterations are not checked.
`protected int`	`m_MaxKMeansForChildren` see above, but for kMeans of splitted clusters.
`protected int`	`m_MaxNumClusters` max number of clusters to generate.
`protected int`	`m_MinNumClusters` min number of clusters to generate.
`protected double[]`	`m_Mle` Distortion.
`protected weka.core.Instances`	`m_Model` model information, should increase readability.
`protected int`	`m_NumClusters` The actual number of clusters.
`protected int`	`m_NumSplits` Number of splits prepared.
`protected int`	`m_NumSplitsDone` Number of splits accepted (including cutoff factor decisions).
`protected int`	`m_NumSplitsStillDone` Number of splits accepted just because of cutoff factor.
`protected File`	`m_OutputCenterFile` file name of the output file for the cluster centers.
`protected weka.filters.unsupervised.attribute.ReplaceMissingValues`	`m_ReplaceMissingFilter` replace missing values in training instances.
`protected boolean`	`m_UseKDTree` whether to use the KDTree (the KDTree is only initialized to be configurable from the GUI).
`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.

Fields inherited from class weka.clusterers.RandomizableClusterer
m_Seed, m_SeedDefault

Constructor Summary

Constructors
Constructor and Description

XMeans()
the default constructor.

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

Method Summary

Methods
Modifier and Type	Method and Description
`protected boolean`	`assignToCenters(weka.core.Instances centers, int[][] instOfCent, int[] allInstList, int[] assignments)` Assign instances to centers.
`protected boolean`	`assignToCenters(weka.core.neighboursearch.KDTree kdtree, weka.core.Instances centers, int[][] instOfCent, int[] assignments, int iterationCount)` Assign instances to centers using KDtree.
`protected boolean`	`assignToCenters(weka.core.neighboursearch.KDTree tree, weka.core.Instances centers, int[][] instOfCent, int[] allInstList, int[] assignments, int iterationCount)` Assigns instances to centers.
`String`	`binValueTipText()` Returns the tip text for this property.
`void`	`buildClusterer(weka.core.Instances data)` Generates the X-Means clusterer.
`protected double`	`calculateBIC(int[][] instOfCent, weka.core.Instances centers, double[] mle)` Calculates the BIC for the given set of centers and instances.
`protected double`	`calculateBIC(int[] instList, weka.core.Instance center, double mle, weka.core.Instances model)` Returns the BIC-value for the given center and instances.
`boolean`	`checkForNominalAttributes(weka.core.Instances data)` Checks for nominal attributes in the dataset.
`protected void`	`checkInstances()` Checks the instances.
`int`	`clusterInstance(weka.core.Instance instance)` Classifies a given instance.
`protected int`	`clusterProcessedInstance(weka.core.Instance instance)` Clusters an instance that has been through the filters.
`protected int`	`clusterProcessedInstance(weka.core.Instance instance, weka.core.Instances centers)` Clusters an instance.
`String`	`cutOffFactorTipText()` Returns the tip text for this property.
`String`	`debugLevelTipText()` Returns the tip text for this property.
`String`	`debugVectorsFileTipText()` Returns the tip text for this property.
`String`	`distanceFTipText()` Returns the tip text for this property.
`protected double[]`	`distortion(int[][] instOfCent, weka.core.Instances centers)` Calculates the maximum likelihood estimate for the variance.
`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.
`File`	`getDebugVectorsFile()` Gets the file name for a file that has the random vectors stored.
`weka.core.DistanceFunction`	`getDistanceF()` Gets the distance function.
`protected String`	`getDistanceFSpec()` Gets the distance function specification string, which contains the class name of the distance function class and any options to it.
`File`	`getInputCenterFile()` Gets the file to read the list of centers from.
`weka.core.neighboursearch.KDTree`	`getKDTree()` Gets the KDTree class.
`protected String`	`getKDTreeSpec()` Gets the KDTree specification string, which contains the class name of the KDTree class and any options to the KDTree.
`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.
`String[]`	`getOptions()` Gets the current settings of SimpleKMeans.
`File`	`getOutputCenterFile()` Gets the file to write the list of centers to.
`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.
`String`	`globalInfo()` Returns a string describing this clusterer.
`protected int[]`	`initAssignments(int numInstances)` Creates and initializes integer array, used to store assignments.
`protected int[]`	`initAssignments(int[] ass)` Set array of int, used to store assignments, to -1.
`void`	`initDebugVectorsInput()` Initialises the debug vector input.
`String`	`inputCenterFileTipText()` Returns the tip text for this property.
`String`	`KDTreeTipText()` Returns the tip text for this property.
`Enumeration`	`listOptions()` Returns an enumeration describing the available options.
`protected double`	`logLikelihoodEstimate(int numInst, weka.core.Instance center, double distortion, int numCent)` Calculates the log-likelihood of the data for the given model, taken at the maximum likelihood point.
`static void`	`main(String[] argv)` Main method for testing this class.
`protected weka.core.Instances`	`makeCentersRandomly(Random random0, weka.core.Instances model, int numClusters)` Generates new centers randomly.
`String`	`maxIterationsTipText()` Returns the tip text for this property.
`String`	`maxKMeansForChildrenTipText()` Returns the tip text for this property.
`String`	`maxKMeansTipText()` Returns the tip text for this property.
`String`	`maxNumClustersTipText()` Returns the tip text for this property.
`protected double`	`meanOrMode(weka.core.Instances instances, int[] instList, int attIndex)` Computes Mean Or Mode of one attribute on a subset of m_Instances.
`String`	`minNumClustersTipText()` Returns the tip text for this property.
`protected weka.core.Instances`	`newCentersAfterSplit(boolean[] splitWon, weka.core.Instances splitCenters)` Returns new centers.
`protected weka.core.Instances`	`newCentersAfterSplit(double[] pbic, double[] cbic, double cutoffFactor, weka.core.Instances splitCenters)` Returns new center list.
`protected int`	`nextAssignedOne(int cent, int lastIndex, int[] assignments)` Searches along the assignment array for the next entry of the center in question.
`int`	`numberOfClusters()` Returns the number of clusters.
`String`	`outputCenterFileTipText()` Returns the tip text for this property.
`protected void`	`PFD_CURR(String output)` Does debug printouts.
`protected void`	`PFD(int debugLevel, String output)` Does debug printouts.
`protected void`	`PrCentersFD(int debugLevel)` Print centers for debug.
`protected boolean`	`recomputeCenters(weka.core.Instances centers, int[][] instOfCent, weka.core.Instances model)` Recompute the new centers.
`protected void`	`recomputeCentersFast(weka.core.Instances centers, int[][] instOfCentIndexes, weka.core.Instances model)` Recompute the new centers - 2nd version Same as recomputeCenters, but does not check if center stays the same.
`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(File value)` Sets the file that has the random vectors stored.
`void`	`setDistanceF(weka.core.DistanceFunction distanceF)` gets the "binary" distance value.
`void`	`setInputCenterFile(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(String[] options)` Parses a given list of options.
`void`	`setOutputCenterFile(File value)` Sets file to write the list of centers to.
`void`	`setUseKDTree(boolean value)` Sets whether to use the KDTree or not.
`protected weka.core.Instances`	`splitCenter(Random random, weka.core.Instance center, double variance, weka.core.Instances model)` Split centers in their region.
`protected weka.core.Instances`	`splitCenters(Random random, weka.core.Instances instances, weka.core.Instances model)` Split centers in their region.
`protected boolean`	`stopIteration(int iterationCount, int max)` Checks if iterationCount has to be checked and if yes (this means max is > 0) compares it with max.
`protected boolean`	`stopKMeansIteration(int iterationCount, int max)` Controls that counter does not exceed max iteration value.
`protected boolean`	`TFD(int debugLevel)` Tests on debug status.
`String`	`toString()` Return a string describing this clusterer.
`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
distributionForInstance, forName, makeCopies, makeCopy, runClusterer

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

- Field Detail
  - m_Instances
```
protected weka.core.Instances m_Instances
```
    training instances.
  - m_Model
```
protected weka.core.Instances m_Model
```
    model information, should increase readability.
  - m_ReplaceMissingFilter
```
protected weka.filters.unsupervised.attribute.ReplaceMissingValues m_ReplaceMissingFilter
```
    replace missing values in training instances.
  - m_BinValue
```
protected double m_BinValue
```
    Distance value between true and false of binary attributes and "same" and "different" of nominal attributes (default = 1.0).
  - m_Bic
```
protected double m_Bic
```
    BIC-Score of the current model.
  - m_Mle
```
protected double[] m_Mle
```
    Distortion.
  - m_MaxIterations
```
protected int m_MaxIterations
```
    maximum overall iterations.
  - m_MaxKMeans
```
protected int m_MaxKMeans
```
    maximum iterations to perform Kmeans part if negative, iterations are not checked.
  - m_MaxKMeansForChildren
```
protected int m_MaxKMeansForChildren
```
    see above, but for kMeans of splitted clusters.
  - m_NumClusters
```
protected int m_NumClusters
```
    The actual number of clusters.
  - m_MinNumClusters
```
protected int m_MinNumClusters
```
    min number of clusters to generate.
  - m_MaxNumClusters
```
protected int m_MaxNumClusters
```
    max number of clusters to generate.
  - m_DistanceF
```
protected weka.core.DistanceFunction m_DistanceF
```
    the distance function used.
  - m_ClusterCenters
```
protected weka.core.Instances m_ClusterCenters
```
    cluster centers.
  - m_InputCenterFile
```
protected File m_InputCenterFile
```
    file name of the output file for the cluster centers.
  - m_DebugVectorsInput
```
protected Reader m_DebugVectorsInput
```
    input file for the random vectors --> USED FOR DEBUGGING.
  - m_DebugVectorsIndex
```
protected int m_DebugVectorsIndex
```
    the index for the current debug vector.
  - m_DebugVectors
```
protected weka.core.Instances m_DebugVectors
```
    all the debug vectors.
  - m_DebugVectorsFile
```
protected File m_DebugVectorsFile
```
    file name of the input file for the random vectors.
  - m_CenterInput
```
protected Reader m_CenterInput
```
    input file for the cluster centers.
  - m_OutputCenterFile
```
protected File m_OutputCenterFile
```
    file name of the output file for the cluster centers.
  - m_CenterOutput
```
protected PrintWriter m_CenterOutput
```
    output file for the cluster centers.
  - m_ClusterAssignments
```
protected int[] m_ClusterAssignments
```
    temporary variable holding cluster assignments while iterating.
  - m_CutOffFactor
```
protected double m_CutOffFactor
```
    cutoff factor - percentage of splits done in Improve-Structure part only relevant, if all children lost.
  - 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.
  - m_KDTree
```
protected weka.core.neighboursearch.KDTree m_KDTree
```
    KDTrees class if KDTrees are used.
  - m_UseKDTree
```
protected boolean m_UseKDTree
```
    whether to use the KDTree (the KDTree is only initialized to be configurable from the GUI).
  - m_IterationCount
```
protected int m_IterationCount
```
    counts iterations done in main loop.
  - m_KMeansStopped
```
protected int m_KMeansStopped
```
    counter to say how often kMeans was stopped by loop counter.
  - m_NumSplits
```
protected int m_NumSplits
```
    Number of splits prepared.
  - m_NumSplitsDone
```
protected int m_NumSplitsDone
```
    Number of splits accepted (including cutoff factor decisions).
  - m_NumSplitsStillDone
```
protected int m_NumSplitsStillDone
```
    Number of splits accepted just because of cutoff factor.
  - m_DebugLevel
```
protected int m_DebugLevel
```
    level of debug output, 0 is no output.
  - 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 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 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:
    
    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
  - initAssignments
```
protected int[] initAssignments(int[] ass)
```
    Set array of int, used to store assignments, to -1.
    
    Parameters:
    ass - integer array used for storing assignments
    
    Returns:
    integer array used for storing assignments
  - initAssignments
```
protected int[] initAssignments(int numInstances)
```
    Creates and initializes integer array, used to store assignments.
    
    Parameters:
    numInstances - length of array used for assignments
    
    Returns:
    integer array used for storing assignments
  - newCentersAfterSplit
```
protected weka.core.Instances newCentersAfterSplit(double[] pbic,
                                       double[] cbic,
                                       double cutoffFactor,
                                       weka.core.Instances splitCenters)
```
    Returns new center list. The following steps 1. and 2. both take care that the number of centers does not exceed maxCenters. 1. Compare BIC values of parent and children and takes the one as new centers which do win (= BIC-value is smaller). 2. If in 1. none of the children are chosen && and cutoff factor is > 0 cutoff factor is taken as the percentage of "best" centers that are still taken.
    
    Parameters:
    pbic - array of parents BIC-values
    cbic - array of childrens BIC-values
    cutoffFactor - cutoff factor
    splitCenters - all children
    
    Returns:
    the new centers
  - newCentersAfterSplit
```
protected weka.core.Instances newCentersAfterSplit(boolean[] splitWon,
                                       weka.core.Instances splitCenters)
```
    Returns new centers. Depending on splitWon: if true takes children, if false takes parent = current center.
    
    Parameters:
    splitWon - array of boolean to indicate to take split or not
    splitCenters - list of splitted centers
    
    Returns:
    the new centers
  - stopKMeansIteration
```
protected boolean stopKMeansIteration(int iterationCount,
                          int max)
```
    Controls that counter does not exceed max iteration value. Special function for kmeans iterations.
    
    Parameters:
    iterationCount - current value of counter
    max - maximum value for counter
    
    Returns:
    true if iteration should be stopped
  - stopIteration
```
protected boolean stopIteration(int iterationCount,
                    int max)
```
    Checks if iterationCount has to be checked and if yes (this means max is > 0) compares it with max.
    
    Parameters:
    iterationCount - the current iteration count
    max - the maximum number of iterations
    
    Returns:
    true if maximum has been reached
  - recomputeCenters
```
protected boolean recomputeCenters(weka.core.Instances centers,
                       int[][] instOfCent,
                       weka.core.Instances model)
```
    Recompute the new centers. New cluster center is center of mass of its instances. Returns true if cluster stays the same.
    
    Parameters:
    centers - the input and output centers
    instOfCent - the instances to the centers
    model - data model information
    
    Returns:
    true if converged.
  - recomputeCentersFast
```
protected void recomputeCentersFast(weka.core.Instances centers,
                        int[][] instOfCentIndexes,
                        weka.core.Instances model)
```
    Recompute the new centers - 2nd version Same as recomputeCenters, but does not check if center stays the same.
    
    Parameters:
    centers - the input center and output centers
    instOfCentIndexes - the indexes of the instances to the centers
    model - data model information
  - meanOrMode
```
protected double meanOrMode(weka.core.Instances instances,
                int[] instList,
                int attIndex)
```
    Computes Mean Or Mode of one attribute on a subset of m_Instances. The subset is defined by an index list.
    
    Parameters:
    instances - all instances
    instList - the indexes of the instances the mean is computed from
    attIndex - the index of the attribute
    
    Returns:
    mean value
  - assignToCenters
```
protected boolean assignToCenters(weka.core.neighboursearch.KDTree tree,
                      weka.core.Instances centers,
                      int[][] instOfCent,
                      int[] allInstList,
                      int[] assignments,
                      int iterationCount)
                           throws Exception
```
    Assigns instances to centers.
    
    Parameters:
    tree - KDTree on all instances
    centers - all the input centers
    instOfCent - the instances to each center
    allInstList - list of all instances
    assignments - assignments of instances to centers
    iterationCount - the number of iteration
    
    Returns:
    true if converged
    
    Throws:
    
    Exception - is something goes wrong
  - assignToCenters
```
protected boolean assignToCenters(weka.core.neighboursearch.KDTree kdtree,
                      weka.core.Instances centers,
                      int[][] instOfCent,
                      int[] assignments,
                      int iterationCount)
                           throws Exception
```
    Assign instances to centers using KDtree. First part of conventionell K-Means, returns true if new assignment is the same as the last one.
    
    Parameters:
    kdtree - KDTree on all instances
    centers - all the input centers
    instOfCent - the instances to each center
    assignments - assignments of instances to centers
    iterationCount - the number of iteration
    
    Returns:
    true if converged
    
    Throws:
    
    Exception - in case instances are not assigned to cluster
  - assignToCenters
```
protected boolean assignToCenters(weka.core.Instances centers,
                      int[][] instOfCent,
                      int[] allInstList,
                      int[] assignments)
                           throws Exception
```
    Assign instances to centers. Part of conventionell K-Means, returns true if new assignment is the same as the last one.
    
    Parameters:
    centers - all the input centers
    instOfCent - the instances to each center
    allInstList - list of all indexes
    assignments - assignments of instances to centers
    
    Returns:
    true if converged
    
    Throws:
    
    Exception - if something goes wrong
  - nextAssignedOne
```
protected int nextAssignedOne(int cent,
                  int lastIndex,
                  int[] assignments)
```
    Searches along the assignment array for the next entry of the center in question.
    
    Parameters:
    cent - index of the center
    lastIndex - index to start searching
    assignments - assignments
    
    Returns:
    index of the instance the center cent is assigned to
  - splitCenter
```
protected weka.core.Instances splitCenter(Random random,
                              weka.core.Instance center,
                              double variance,
                              weka.core.Instances model)
                                   throws Exception
```
    Split centers in their region. Generates random vector of length = variance and adds and substractsx to cluster vector to get two new clusters.
    
    Parameters:
    random - random function
    center - the center that is split here
    variance - variance of the cluster
    model - data model valid
    
    Returns:
    a pair of new centers
    
    Throws:
    
    Exception - something in AlgVector goes wrong
  - splitCenters
```
protected weka.core.Instances splitCenters(Random random,
                               weka.core.Instances instances,
                               weka.core.Instances model)
```
    Split centers in their region. (*Alternative version of splitCenter()*)
    
    Parameters:
    random - the random number generator
    instances - of the region
    model - the model for the centers (should be the same as that of instances)
    
    Returns:
    a pair of new centers
  - makeCentersRandomly
```
protected weka.core.Instances makeCentersRandomly(Random random0,
                                      weka.core.Instances model,
                                      int numClusters)
```
    Generates new centers randomly. Used for starting centers.
    
    Parameters:
    random0 - random number generator
    model - data model of the instances
    numClusters - number of clusters
    
    Returns:
    new centers
  - calculateBIC
```
protected double calculateBIC(int[] instList,
                  weka.core.Instance center,
                  double mle,
                  weka.core.Instances model)
```
    Returns the BIC-value for the given center and instances.
    
    Parameters:
    instList - The indices of the instances that belong to the center
    center - the center.
    mle - maximum likelihood
    model - the data model
    
    Returns:
    the BIC value
  - calculateBIC
```
protected double calculateBIC(int[][] instOfCent,
                  weka.core.Instances centers,
                  double[] mle)
```
    Calculates the BIC for the given set of centers and instances.
    
    Parameters:
    instOfCent - The instances that belong to their respective centers
    centers - the centers
    mle - maximum likelihood
    
    Returns:
    The BIC for the input.
  - logLikelihoodEstimate
```
protected double logLikelihoodEstimate(int numInst,
                           weka.core.Instance center,
                           double distortion,
                           int numCent)
```
    Calculates the log-likelihood of the data for the given model, taken at the maximum likelihood point.
    
    Parameters:
    numInst - number of instances that belong to the center
    center - the center
    distortion - distortion
    numCent - number of centers
    
    Returns:
    the likelihood estimate
  - distortion
```
protected double[] distortion(int[][] instOfCent,
                  weka.core.Instances centers)
```
    Calculates the maximum likelihood estimate for the variance.
    
    Parameters:
    instOfCent - indices of instances to each center
    centers - the centers
    
    Returns:
    the list of distortions distortion.
  - clusterProcessedInstance
```
protected int clusterProcessedInstance(weka.core.Instance instance,
                           weka.core.Instances centers)
```
    Clusters an instance.
    
    Parameters:
    instance - the instance to assign a cluster to.
    centers - the centers to cluster the instance to.
    
    Returns:
    a cluster index.
  - clusterProcessedInstance
```
protected int clusterProcessedInstance(weka.core.Instance instance)
```
    Clusters an instance that has been through the filters.
    
    Parameters:
    instance - the instance to assign a cluster to
    
    Returns:
    a cluster number
  - clusterInstance
```
public int clusterInstance(weka.core.Instance instance)
                    throws 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:
    
    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 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
  - minNumClustersTipText
```
public 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 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 String maxIterationsTipText()
```
    Returns the tip text for this property.
    
    Returns:
    tip text for this property
  - setMaxIterations
```
public void setMaxIterations(int i)
                      throws Exception
```
    Sets the maximum number of iterations to perform.
    
    Parameters:
    i - the number of iterations
    
    Throws:
    
    Exception - if i is less than 1
  - getMaxIterations
```
public int getMaxIterations()
```
    Gets the maximum number of iterations.
    
    Returns:
    the number of iterations
  - maxKMeansTipText
```
public 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 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 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 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 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
  - getDistanceFSpec
```
protected String getDistanceFSpec()
```
    Gets the distance function specification string, which contains the class name of the distance function class and any options to it.
    
    Returns:
    the distance function specification string
  - debugVectorsFileTipText
```
public 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(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 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 Exception
```
    Initialises the debug vector input.
    
    Throws:
    
    Exception - if there is error opening the debug input file.
  - getNextDebugVectorsInstance
```
public weka.core.Instance getNextDebugVectorsInstance(weka.core.Instances model)
                                               throws Exception
```
    Read an instance from debug vectors file.
    
    Parameters:
    model - the data model for the instance.
    
    Returns:
    the next debug vector.
    
    Throws:
    
    Exception - if there are no debug vector in m_DebugVectors.
  - inputCenterFileTipText
```
public 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(File value)
```
    Sets the file to read the list of centers from.
    
    Parameters:
    value - the file to read centers from
  - getInputCenterFile
```
public File getInputCenterFile()
```
    Gets the file to read the list of centers from.
    
    Returns:
    the file to read the centers from
  - outputCenterFileTipText
```
public 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(File value)
```
    Sets file to write the list of centers to.
    
    Parameters:
    value - file to write centers to
  - getOutputCenterFile
```
public File getOutputCenterFile()
```
    Gets the file to write the list of centers to.
    
    Returns:
    filename of the file to write centers to
  - KDTreeTipText
```
public 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 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
  - getKDTreeSpec
```
protected String getKDTreeSpec()
```
    Gets the KDTree specification string, which contains the class name of the KDTree class and any options to the KDTree.
    
    Returns:
    the KDTree string.
  - debugLevelTipText
```
public 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
  - checkInstances
```
protected void checkInstances()
```
    Checks the instances. No checks in this KDTree but it calls the check of the distance function.
  - setOptions
```
public void setOptions(String[] options)
                throws 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:
    
    Exception - if an option is not supported
  - getOptions
```
public 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 String toString()
```
    Return a string describing this clusterer.
    
    Overrides:
    
    toString in class 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.
  - PrCentersFD
```
protected void PrCentersFD(int debugLevel)
```
    Print centers for debug.
    
    Parameters:
    debugLevel - level that gives according messages
  - TFD
```
protected boolean TFD(int debugLevel)
```
    Tests on debug status.
    
    Parameters:
    debugLevel - level that gives according messages
    
    Returns:
    true if debug level is set
  - PFD
```
protected void PFD(int debugLevel,
       String output)
```
    Does debug printouts.
    
    Parameters:
    debugLevel - level that gives according messages
    output - string that is printed
  - PFD_CURR
```
protected void PFD_CURR(String output)
```
    Does debug printouts.
    
    Parameters:
    output - string that is printed
  - getRevision
```
public 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(String[] argv)
```
    Main method for testing this class.
    
    Parameters:
    argv - should contain options

Class XMeans

Field Summary

Fields inherited from class weka.clusterers.RandomizableClusterer

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

Field Detail

m_Instances

m_Model

m_ReplaceMissingFilter

m_BinValue

m_Bic

m_Mle

m_MaxIterations

m_MaxKMeans

m_MaxKMeansForChildren

m_NumClusters

m_MinNumClusters

m_MaxNumClusters

m_DistanceF

m_ClusterCenters

m_InputCenterFile

m_DebugVectorsInput

m_DebugVectorsIndex

m_DebugVectors

m_DebugVectorsFile

m_CenterInput

m_OutputCenterFile

m_CenterOutput

m_ClusterAssignments

m_CutOffFactor

R_LOW

R_HIGH

R_WIDTH

m_KDTree

m_UseKDTree

m_IterationCount

m_KMeansStopped

m_NumSplits

m_NumSplitsDone

m_NumSplitsStillDone

m_DebugLevel

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

initAssignments

initAssignments

newCentersAfterSplit

newCentersAfterSplit

stopKMeansIteration

stopIteration

recomputeCenters

recomputeCentersFast

meanOrMode

assignToCenters

assignToCenters

assignToCenters

nextAssignedOne

splitCenter

splitCenters

makeCentersRandomly

calculateBIC

calculateBIC