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.DBSCAN extends weka.clusterers.AbstractClusterer implements Serializable
  
  serialVersionUID:
  
  -1666498248451219728L
  - Serialized Fields
    - epsilon
```
double epsilon
```
      Specifies the radius for a range-query
    - minPoints
```
int minPoints
```
      Specifies the density (the range-query must contain at least minPoints DataObjects)
    - replaceMissingValues_Filter
```
weka.filters.unsupervised.attribute.ReplaceMissingValues replaceMissingValues_Filter
```
      Replace missing values in training instances
    - numberOfGeneratedClusters
```
int numberOfGeneratedClusters
```
      Holds the number of clusters generated
    - m_DistanceFunction
```
weka.core.DistanceFunction m_DistanceFunction
```
      the distance function used.
    - database
```
Database database
```
      The database that is used for DBSCAN
    - clusterID
```
int clusterID
```
      Holds the current clusterID
    - processed_InstanceID
```
int processed_InstanceID
```
      Counter for the processed instances
    - elapsedTime
```
double elapsedTime
```
      Holds the time-value (seconds) for the duration of the clustering-process
- 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.OPTICS extends weka.clusterers.AbstractClusterer implements Serializable
  
  serialVersionUID:
  
  274552680222105221L
  - Serialized Fields
    - epsilon
```
double epsilon
```
      Specifies the radius for a range-query
    - minPoints
```
int minPoints
```
      Specifies the density (the range-query must contain at least minPoints DataObjects)
    - replaceMissingValues_Filter
```
weka.filters.unsupervised.attribute.ReplaceMissingValues replaceMissingValues_Filter
```
      Replace missing values in training instances
    - numberOfGeneratedClusters
```
int numberOfGeneratedClusters
```
      Holds the number of clusters generated
    - m_DistanceFunction
```
weka.core.DistanceFunction m_DistanceFunction
```
      the distance function used.
    - database
```
Database database
```
      The database that is used for OPTICS
    - elapsedTime
```
double elapsedTime
```
      Holds the time-value (seconds) for the duration of the clustering-process
    - writeOPTICSresults
```
boolean writeOPTICSresults
```
      Flag that indicates if the results are written to a file or not
    - resultVector
```
java.util.ArrayList<E> resultVector
```
      Holds the ClusterOrder (dataObjects with their r_dist and c_dist) for the GUI
    - showGUI
```
boolean showGUI
```
      whether to display the GUI after building the clusterer or not.
    - databaseOutput
```
java.io.File databaseOutput
```
      the file to save the generated database object to.
- 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
```
Package weka.clusterers.forOPTICSAndDBScan.Databases
- Class weka.clusterers.forOPTICSAndDBScan.Databases.Database extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  787245523118665778L
  - Serialized Fields
    - treeMap
```
java.util.TreeMap<K,V> treeMap
```
      Internal, sorted Treemap for storing all the DataObjects
    - instances
```
weka.core.Instances instances
```
      Holds the original instances delivered from WEKA
    - m_DistanceFunction
```
weka.core.DistanceFunction m_DistanceFunction
```
      The distance function to use.
Package weka.clusterers.forOPTICSAndDBScan.DataObjects
- Class weka.clusterers.forOPTICSAndDBScan.DataObjects.DataObject extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -4408119914898291075L
  - Serialized Fields
    - instance
```
weka.core.Instance instance
```
      Holds the original instance
    - key
```
java.lang.String key
```
      Holds the (unique) key that is associated with this DataObject
    - clusterID
```
int clusterID
```
      Holds the ID of the cluster, to which this DataObject is assigned
    - processed
```
boolean processed
```
      Holds the status for this DataObject (true, if it has been processed, else false)
    - c_dist
```
double c_dist
```
      Holds the coreDistance for this DataObject
    - r_dist
```
double r_dist
```
      Holds the reachabilityDistance for this DataObject
    - database
```
Database database
```
      Holds the database, that is the keeper of this DataObject
Package weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI
- Class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.GraphPanel extends javax.swing.JComponent implements Serializable
  
  serialVersionUID:
  
  7917937528738361470L
  - Serialized Fields
    - resultVector
```
java.util.ArrayList<E> resultVector
```
      Holds the clustering results
    - verticalAdjustment
```
int verticalAdjustment
```
      Holds the value that is multiplied with the original values of core- and reachability distances in order to get better graphical views
    - coreDistanceColor
```
java.awt.Color coreDistanceColor
```
      Specifies the color for displaying core-distances
    - reachabilityDistanceColor
```
java.awt.Color reachabilityDistanceColor
```
      Specifies the color for displaying reachability-distances
    - widthSlider
```
int widthSlider
```
      Specifies the width for displaying the distances
    - showCoreDistances
```
boolean showCoreDistances
```
      Holds the flag for showCoreDistances
    - showReachabilityDistances
```
boolean showReachabilityDistances
```
      Holds the flag for showrRechabilityDistances
    - recentIndex
```
int recentIndex
```
      Holds the index of the last toolTip
- Class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.ResultVectorTableModel extends javax.swing.table.AbstractTableModel implements Serializable
  
  serialVersionUID:
  
  -7732711470435549210L
  - Serialized Fields
    - resultVector
```
java.util.ArrayList<E> resultVector
```
      Holds the ClusterOrder (dataObjects with their r_dist and c_dist) for the GUI
- Class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.SERObject extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  -6022057864970639151L
  - Serialized Fields
    - resultVector
```
java.util.ArrayList<E> resultVector
```
    - databaseSize
```
int databaseSize
```
    - numberOfAttributes
```
int numberOfAttributes
```
    - epsilon
```
double epsilon
```
    - minPoints
```
int minPoints
```
    - opticsOutputs
```
boolean opticsOutputs
```
    - distanceFunction
```
weka.core.DistanceFunction distanceFunction
```
    - numberOfGeneratedClusters
```
int numberOfGeneratedClusters
```
    - elapsedTime
```
java.lang.String elapsedTime
```

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.DBSCAN extends weka.clusterers.AbstractClusterer implements Serializable

Serialized Fields

epsilon

minPoints

replaceMissingValues_Filter

numberOfGeneratedClusters

m_DistanceFunction

database

clusterID

processed_InstanceID

elapsedTime

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