Serialized Form

Package weka.clusterers
- Class weka.clusterers.AbstractClusterer extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -6099962589663877632L
  - Serialized Fields
    - m_Debug
```
boolean m_Debug
```
    - m_DoNotCheckCapabilities
```
boolean m_DoNotCheckCapabilities
```
- Class weka.clusterers.AbstractDensityBasedClusterer extends weka.clusterers.AbstractClusterer implements Serializable
  
  serialVersionUID:
  
  -5950728041704213845L
- Class weka.clusterers.Canopy extends weka.clusterers.RandomizableClusterer implements Serializable
  
  serialVersionUID:
  
  2067574593448223334L
  - Serialized Fields
    - m_canopies
```
weka.core.Instances m_canopies
```
    - m_canopyT2Density
```
java.util.List<E> m_canopyT2Density
```
    - m_canopyCenters
```
java.util.List<E> m_canopyCenters
```
    - m_canopyNumMissingForNumerics
```
java.util.List<E> m_canopyNumMissingForNumerics
```
    - m_clusterCanopies
```
java.util.List<E> m_clusterCanopies
```
    - m_userT2
```
double m_userT2
```
    - m_userT1
```
double m_userT1
```
    - m_t1
```
double m_t1
```
    - m_t2
```
double m_t2
```
    - m_periodicPruningRate
```
int m_periodicPruningRate
```
    - m_minClusterDensity
```
double m_minClusterDensity
```
    - m_maxCanopyCandidates
```
int m_maxCanopyCandidates
```
    - m_didPruneLastTime
```
boolean m_didPruneLastTime
```
    - m_instanceCount
```
int m_instanceCount
```
    - m_numClustersRequested
```
int m_numClustersRequested
```
    - m_missingValuesReplacer
```
weka.filters.Filter m_missingValuesReplacer
```
    - m_dontReplaceMissing
```
boolean m_dontReplaceMissing
```
    - m_distanceFunction
```
weka.core.NormalizableDistance m_distanceFunction
```
    - m_trainingData
```
weka.core.Instances m_trainingData
```
- Class weka.clusterers.ClusterEvaluation extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -830188327319128005L
  - Serialized Fields
    - m_Clusterer
```
weka.clusterers.Clusterer m_Clusterer
```
    - m_clusteringResults
```
java.lang.StringBuffer m_clusteringResults
```
    - m_numClusters
```
int m_numClusters
```
    - m_clusterAssignments
```
double[] m_clusterAssignments
```
    - m_logL
```
double m_logL
```
    - m_classToCluster
```
int[] m_classToCluster
```
- Class weka.clusterers.Cobweb extends weka.clusterers.RandomizableClusterer implements Serializable
  
  serialVersionUID:
  
  928406656495092318L
  - Serialized Fields
    - m_acuity
```
double m_acuity
```
    - m_cutoff
```
double m_cutoff
```
    - m_cobwebTree
```
weka.clusterers.Cobweb.CNode m_cobwebTree
```
    - m_numberOfClusters
```
int m_numberOfClusters
```
    - m_numberOfClustersDetermined
```
boolean m_numberOfClustersDetermined
```
    - m_numberSplits
```
int m_numberSplits
```
    - m_numberMerges
```
int m_numberMerges
```
    - m_saveInstances
```
boolean m_saveInstances
```
- Class weka.clusterers.Cobweb.CNode extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  3452097436933325631L
  - Serialized Fields
    - m_attStats
```
weka.core.AttributeStats[] m_attStats
```
    - m_numAttributes
```
int m_numAttributes
```
    - m_clusterInstances
```
weka.core.Instances m_clusterInstances
```
    - m_children
```
java.util.ArrayList<E> m_children
```
    - m_totalInstances
```
double m_totalInstances
```
    - m_clusterNum
```
int m_clusterNum
```
- Class weka.clusterers.EM extends weka.clusterers.RandomizableDensityBasedClusterer implements Serializable
  
  serialVersionUID:
  
  8348181483812829475L
  - Serialized Fields
    - m_modelPrev
```
weka.estimators.Estimator[][] m_modelPrev
```
    - m_modelNormalPrev
```
double[][][] m_modelNormalPrev
```
    - m_priorsPrev
```
double[] m_priorsPrev
```
    - m_model
```
weka.estimators.Estimator[][] m_model
```
    - m_modelNormal
```
double[][][] m_modelNormal
```
    - m_minStdDev
```
double m_minStdDev
```
    - m_minStdDevPerAtt
```
double[] m_minStdDevPerAtt
```
    - m_weights
```
double[][] m_weights
```
    - m_priors
```
double[] m_priors
```
    - m_theInstances
```
weka.core.Instances m_theInstances
```
    - m_num_clusters
```
int m_num_clusters
```
    - m_initialNumClusters
```
int m_initialNumClusters
```
    - m_upperBoundNumClustersCV
```
int m_upperBoundNumClustersCV
```
    - m_num_attribs
```
int m_num_attribs
```
    - m_num_instances
```
int m_num_instances
```
    - m_max_iterations
```
int m_max_iterations
```
    - m_minValues
```
double[] m_minValues
```
    - m_maxValues
```
double[] m_maxValues
```
    - m_rr
```
java.util.Random m_rr
```
    - m_verbose
```
boolean m_verbose
```
    - m_replaceMissing
```
weka.filters.unsupervised.attribute.ReplaceMissingValues m_replaceMissing
```
    - m_displayModelInOldFormat
```
boolean m_displayModelInOldFormat
```
    - m_executionSlots
```
int m_executionSlots
```
    - m_training
```
boolean m_training
```
    - m_iterationsPerformed
```
int m_iterationsPerformed
```
    - m_minLogLikelihoodImprovementIterating
```
double m_minLogLikelihoodImprovementIterating
```
    - m_minLogLikelihoodImprovementCV
```
double m_minLogLikelihoodImprovementCV
```
    - m_cvFolds
```
int m_cvFolds
```
    - m_NumKMeansRuns
```
int m_NumKMeansRuns
```
- Class weka.clusterers.FarthestFirst extends weka.clusterers.RandomizableClusterer implements Serializable
  
  serialVersionUID:
  
  7499838100631329509L
  - Serialized Fields
    - m_instances
```
weka.core.Instances m_instances
```
    - m_ReplaceMissingFilter
```
weka.filters.unsupervised.attribute.ReplaceMissingValues m_ReplaceMissingFilter
```
    - m_NumClusters
```
int m_NumClusters
```
    - m_ClusterCentroids
```
weka.core.Instances m_ClusterCentroids
```
    - m_Min
```
double[] m_Min
```
    - m_Max
```
double[] m_Max
```
- Class weka.clusterers.FilteredClusterer extends weka.clusterers.SingleClustererEnhancer implements Serializable
  
  serialVersionUID:
  
  1420005943163412943L
  - Serialized Fields
    - m_Filter
```
weka.filters.Filter m_Filter
```
    - m_FilteredInstances
```
weka.core.Instances m_FilteredInstances
```
- Class weka.clusterers.HierarchicalClusterer extends weka.clusterers.AbstractClusterer implements Serializable
  
  serialVersionUID:
  
  1L
  - Serialized Fields
    - m_bDistanceIsBranchLength
```
boolean m_bDistanceIsBranchLength
```
    - m_instances
```
weka.core.Instances m_instances
```
    - m_nNumClusters
```
int m_nNumClusters
```
    - m_DistanceFunction
```
weka.core.DistanceFunction m_DistanceFunction
```
    - m_nLinkType
```
int m_nLinkType
```
    - m_bPrintNewick
```
boolean m_bPrintNewick
```
    - m_clusters
```
weka.clusterers.HierarchicalClusterer.Node[] m_clusters
```
    - m_nClusterNr
```
int[] m_nClusterNr
```
- Class weka.clusterers.MakeDensityBasedClusterer extends weka.clusterers.AbstractDensityBasedClusterer implements Serializable
  
  serialVersionUID:
  
  -5643302427972186631L
  - Serialized Fields
    - m_theInstances
```
weka.core.Instances m_theInstances
```
    - m_priors
```
double[] m_priors
```
    - m_modelNormal
```
double[][][] m_modelNormal
```
    - m_model
```
weka.estimators.DiscreteEstimator[][] m_model
```
    - m_minStdDev
```
double m_minStdDev
```
    - m_wrappedClusterer
```
weka.clusterers.Clusterer m_wrappedClusterer
```
    - m_replaceMissing
```
weka.filters.unsupervised.attribute.ReplaceMissingValues m_replaceMissing
```
- Class weka.clusterers.RandomizableClusterer extends weka.clusterers.AbstractClusterer implements Serializable
  
  serialVersionUID:
  
  -4819590778152242745L
  - Serialized Fields
    - m_SeedDefault
```
int m_SeedDefault
```
    - m_Seed
```
int m_Seed
```
- Class weka.clusterers.RandomizableDensityBasedClusterer extends weka.clusterers.AbstractDensityBasedClusterer implements Serializable
  
  serialVersionUID:
  
  -5325270357918932849L
  - Serialized Fields
    - m_SeedDefault
```
int m_SeedDefault
```
    - m_Seed
```
int m_Seed
```
- Class weka.clusterers.RandomizableSingleClustererEnhancer extends weka.clusterers.AbstractClusterer implements Serializable
  
  serialVersionUID:
  
  -644847037106316249L
  - Serialized Fields
    - m_SeedDefault
```
int m_SeedDefault
```
    - m_Seed
```
int m_Seed
```
- Class weka.clusterers.SimpleKMeans extends weka.clusterers.RandomizableClusterer implements Serializable
  
  serialVersionUID:
  
  -3235809600124455376L
  - Serialized Fields
    - m_ReplaceMissingFilter
```
weka.filters.unsupervised.attribute.ReplaceMissingValues m_ReplaceMissingFilter
```
    - m_NumClusters
```
int m_NumClusters
```
    - m_initialStartPoints
```
weka.core.Instances m_initialStartPoints
```
    - m_ClusterCentroids
```
weka.core.Instances m_ClusterCentroids
```
    - m_ClusterStdDevs
```
weka.core.Instances m_ClusterStdDevs
```
    - m_ClusterNominalCounts
```
double[][][] m_ClusterNominalCounts
```
    - m_ClusterMissingCounts
```
double[][] m_ClusterMissingCounts
```
    - m_FullMeansOrMediansOrModes
```
double[] m_FullMeansOrMediansOrModes
```
    - m_FullStdDevs
```
double[] m_FullStdDevs
```
    - m_FullNominalCounts
```
double[][] m_FullNominalCounts
```
    - m_FullMissingCounts
```
double[] m_FullMissingCounts
```
    - m_displayStdDevs
```
boolean m_displayStdDevs
```
    - m_dontReplaceMissing
```
boolean m_dontReplaceMissing
```
    - m_ClusterSizes
```
double[] m_ClusterSizes
```
    - m_MaxIterations
```
int m_MaxIterations
```
    - m_Iterations
```
int m_Iterations
```
    - m_squaredErrors
```
double[] m_squaredErrors
```
    - m_DistanceFunction
```
weka.core.DistanceFunction m_DistanceFunction
```
    - m_PreserveOrder
```
boolean m_PreserveOrder
```
    - m_Assignments
```
int[] m_Assignments
```
    - m_FastDistanceCalc
```
boolean m_FastDistanceCalc
```
    - m_initializationMethod
```
int m_initializationMethod
```
    - m_speedUpDistanceCompWithCanopies
```
boolean m_speedUpDistanceCompWithCanopies
```
    - m_centroidCanopyAssignments
```
java.util.List<E> m_centroidCanopyAssignments
```
    - m_dataPointCanopyAssignments
```
java.util.List<E> m_dataPointCanopyAssignments
```
    - m_canopyClusters
```
weka.clusterers.Canopy m_canopyClusters
```
    - m_maxCanopyCandidates
```
int m_maxCanopyCandidates
```
    - m_periodicPruningRate
```
int m_periodicPruningRate
```
    - m_minClusterDensity
```
double m_minClusterDensity
```
    - m_t2
```
double m_t2
```
    - m_t1
```
double m_t1
```
    - m_executionSlots
```
int m_executionSlots
```
    - m_completed
```
int m_completed
```
    - m_failed
```
int m_failed
```
- Class weka.clusterers.SingleClustererEnhancer extends weka.clusterers.AbstractClusterer implements Serializable
  
  serialVersionUID:
  
  4893928362926428671L
  - Serialized Fields
    - m_Clusterer
```
weka.clusterers.Clusterer m_Clusterer
```
- Class weka.clusterers.XMeans extends weka.clusterers.RandomizableClusterer implements Serializable
  
  serialVersionUID:
  
  -7941793078404132616L
  - Serialized Fields
    - m_Instances
```
weka.core.Instances m_Instances
```
      training instances.
    - m_Model
```
weka.core.Instances m_Model
```
      model information, should increase readability.
    - m_ReplaceMissingFilter
```
weka.filters.unsupervised.attribute.ReplaceMissingValues m_ReplaceMissingFilter
```
      replace missing values in training instances.
    - m_BinValue
```
double m_BinValue
```
      Distance value between true and false of binary attributes and "same" and "different" of nominal attributes (default = 1.0).
    - m_Bic
```
double m_Bic
```
      BIC-Score of the current model.
    - m_Mle
```
double[] m_Mle
```
      Distortion.
    - m_MaxIterations
```
int m_MaxIterations
```
      maximum overall iterations.
    - m_MaxKMeans
```
int m_MaxKMeans
```
      maximum iterations to perform Kmeans part if negative, iterations are not checked.
    - m_MaxKMeansForChildren
```
int m_MaxKMeansForChildren
```
      see above, but for kMeans of splitted clusters.
    - m_NumClusters
```
int m_NumClusters
```
      The actual number of clusters.
    - m_MinNumClusters
```
int m_MinNumClusters
```
      min number of clusters to generate.
    - m_MaxNumClusters
```
int m_MaxNumClusters
```
      max number of clusters to generate.
    - m_DistanceF
```
weka.core.DistanceFunction m_DistanceF
```
      the distance function used.
    - m_ClusterCenters
```
weka.core.Instances m_ClusterCenters
```
      cluster centers.
    - m_InputCenterFile
```
java.io.File m_InputCenterFile
```
      file name of the output file for the cluster centers.
    - m_DebugVectorsInput
```
java.io.Reader m_DebugVectorsInput
```
      input file for the random vectors --> USED FOR DEBUGGING.
    - m_DebugVectorsIndex
```
int m_DebugVectorsIndex
```
      the index for the current debug vector.
    - m_DebugVectors
```
weka.core.Instances m_DebugVectors
```
      all the debug vectors.
    - m_DebugVectorsFile
```
java.io.File m_DebugVectorsFile
```
      file name of the input file for the random vectors.
    - m_OutputCenterFile
```
java.io.File m_OutputCenterFile
```
      file name of the output file for the cluster centers.
    - m_ClusterAssignments
```
int[] m_ClusterAssignments
```
      temporary variable holding cluster assignments while iterating.
    - m_CutOffFactor
```
double m_CutOffFactor
```
      cutoff factor - percentage of splits done in Improve-Structure part only relevant, if all children lost.
    - m_KDTree
```
weka.core.neighboursearch.KDTree m_KDTree
```
      KDTrees class if KDTrees are used.
    - m_UseKDTree
```
boolean m_UseKDTree
```
      whether to use the KDTree (the KDTree is only initialized to be configurable from the GUI).
    - m_IterationCount
```
int m_IterationCount
```
      counts iterations done in main loop.
    - m_KMeansStopped
```
int m_KMeansStopped
```
      counter to say how often kMeans was stopped by loop counter.
    - m_NumSplits
```
int m_NumSplits
```
      Number of splits prepared.
    - m_NumSplitsDone
```
int m_NumSplitsDone
```
      Number of splits accepted (including cutoff factor decisions).
    - m_NumSplitsStillDone
```
int m_NumSplitsStillDone
```
      Number of splits accepted just because of cutoff factor.
    - m_DebugLevel
```
int m_DebugLevel
```
      level of debug output, 0 is no output.
    - m_CurrDebugFlag
```
boolean m_CurrDebugFlag
```
      Flag: I'm debugging.

Serialized Form

Package weka.clusterers

Class weka.clusterers.AbstractClusterer extends java.lang.Object implements Serializable

Serialized Fields

m_Debug

m_DoNotCheckCapabilities

Class weka.clusterers.AbstractDensityBasedClusterer extends weka.clusterers.AbstractClusterer implements Serializable

Class weka.clusterers.Canopy extends weka.clusterers.RandomizableClusterer implements Serializable

Serialized Fields

m_canopies

m_canopyT2Density

m_canopyCenters

m_canopyNumMissingForNumerics

m_clusterCanopies

m_userT2

m_userT1

m_t1

m_t2

m_periodicPruningRate

m_minClusterDensity

m_maxCanopyCandidates

m_didPruneLastTime

m_instanceCount

m_numClustersRequested

m_missingValuesReplacer

m_dontReplaceMissing

m_distanceFunction

m_trainingData

Class weka.clusterers.ClusterEvaluation extends java.lang.Object implements Serializable

Serialized Fields

m_Clusterer

m_clusteringResults

m_numClusters

m_clusterAssignments

m_logL

m_classToCluster

Class weka.clusterers.Cobweb extends weka.clusterers.RandomizableClusterer implements Serializable

Serialized Fields

m_acuity

m_cutoff

m_cobwebTree

m_numberOfClusters

m_numberOfClustersDetermined

m_numberSplits

m_numberMerges

m_saveInstances

Class weka.clusterers.Cobweb.CNode extends java.lang.Object implements Serializable

Serialized Fields

m_attStats

m_numAttributes

m_clusterInstances

m_children

m_totalInstances

m_clusterNum

Class weka.clusterers.EM extends weka.clusterers.RandomizableDensityBasedClusterer implements Serializable

Serialized Fields

m_modelPrev

m_modelNormalPrev

m_priorsPrev

m_model

m_modelNormal

m_minStdDev

m_minStdDevPerAtt

m_weights

m_priors

m_theInstances

m_num_clusters

m_initialNumClusters

m_upperBoundNumClustersCV

m_num_attribs

m_num_instances

m_max_iterations

m_minValues

m_maxValues

m_rr

m_verbose

m_replaceMissing

m_displayModelInOldFormat

m_executionSlots

m_training