SimpleKMeans (interruptible-weka 0.1.4 API)

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

All Implemented Interfaces:: java.io.Serializable, java.lang.Cloneable, Clusterer, NumberOfClustersRequestable, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, Randomizable, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler

public class SimpleKMeans
extends RandomizableClusterer
implements NumberOfClustersRequestable, WeightedInstancesHandler, TechnicalInformationHandler

Cluster data using the k means algorithm. Can use either the Euclidean distance (default) or the Manhattan distance. If the Manhattan distance is used, then centroids are computed as the component-wise median rather than mean. For more information see:

D. Arthur, S. Vassilvitskii: k-means++: the advantages of carefull seeding. In: Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, 1027-1035, 2007.

BibTeX:

 @inproceedings{Arthur2007,
    author = {D. Arthur and S. Vassilvitskii},
    booktitle = {Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms},
    pages = {1027-1035},
    title = {k-means++: the advantages of carefull seeding},
    year = {2007}
 }

Valid options are:

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

 -init
  Initialization method to use.
  0 = random, 1 = k-means++, 2 = canopy, 3 = farthest first.
  (default = 0)

 -C
  Use canopies to reduce the number of distance calculations.

 -max-candidates <num>
  Maximum number of candidate canopies to retain in memory
  at any one time when using canopy clustering.
  T2 distance plus, data characteristics,
  will determine how many candidate canopies are formed before
  periodic and final pruning are performed, which might result
  in exceess memory consumption. This setting avoids large numbers
  of candidate canopies consuming memory. (default = 100)

 -periodic-pruning <num>
  How often to prune low density canopies when using canopy clustering. 
  (default = every 10,000 training instances)

 -min-density
  Minimum canopy density, when using canopy clustering, below which
   a canopy will be pruned during periodic pruning. (default = 2 instances)

 -t2
  The T2 distance to use when using canopy clustering. Values < 0 indicate that
  a heuristic based on attribute std. deviation should be used to set this.
  (default = -1.0)

 -t1
  The T1 distance to use when using canopy clustering. A value < 0 is taken as a
  positive multiplier for T2. (default = -1.5)

 -V
  Display std. deviations for centroids.

 -M
  Don't replace missing values with mean/mode.

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

 -I <num>
  Maximum number of iterations.

 -O
  Preserve order of instances.

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

 -num-slots <num>
  Number of execution slots.
  (default 1 - i.e. no parallelism)

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

 -output-debug-info
  If set, clusterer is run in debug mode and
  may output additional info to the console

 -do-not-check-capabilities
  If set, clusterer capabilities are not checked before clusterer is built
  (use with caution).

See Also:: RandomizableClusterer, Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`static int`	`CANOPY`
`static int`	`FARTHEST_FIRST`
`static int`	`KMEANS_PLUS_PLUS`
`protected int[]`	`m_Assignments` Assignments obtained.
`protected Canopy`	`m_canopyClusters` The canopy clusterer (if being used)
`protected java.util.List<long[]>`	`m_centroidCanopyAssignments` Canopies that each centroid falls into (determined by T1 radius)
`protected Instances`	`m_ClusterCentroids` holds the cluster centroids.
`protected double[][]`	`m_ClusterMissingCounts`
`protected double[][][]`	`m_ClusterNominalCounts` For each cluster, holds the frequency counts for the values of each nominal attribute.
`protected double[]`	`m_ClusterSizes` The number of instances in each cluster.
`protected Instances`	`m_ClusterStdDevs` Holds the standard deviations of the numeric attributes in each cluster.
`protected int`	`m_completed`
`protected java.util.List<long[]>`	`m_dataPointCanopyAssignments` Canopies that each training instance falls into (determined by T1 radius)
`protected boolean`	`m_displayStdDevs` Display standard deviations for numeric atts.
`protected DistanceFunction`	`m_DistanceFunction` the distance function used.
`protected boolean`	`m_dontReplaceMissing` Replace missing values globally?
`protected int`	`m_executionSlots` Number of threads to run
`protected java.util.concurrent.ExecutorService`	`m_executorPool` For parallel execution mode
`protected int`	`m_failed`
`protected boolean`	`m_FastDistanceCalc` whether to use fast calculation of distances (using a cut-off).
`protected double[]`	`m_FullMeansOrMediansOrModes` Stats on the full data set for comparison purposes.
`protected double[]`	`m_FullMissingCounts`
`protected double[][]`	`m_FullNominalCounts`
`protected double[]`	`m_FullStdDevs`
`protected int`	`m_initializationMethod` The initialization method to use
`protected Instances`	`m_initialStartPoints` Holds the initial start points, as supplied by the initialization method used
`protected int`	`m_Iterations` Keep track of the number of iterations completed before convergence.
`protected int`	`m_maxCanopyCandidates` The maximum number of candidate canopies to hold in memory at any one time (if using canopy clustering)
`protected int`	`m_MaxIterations` Maximum number of iterations to be executed.
`protected double`	`m_minClusterDensity` The minimum cluster density (according to T2 distance) allowed.
`protected int`	`m_NumClusters` number of clusters to generate.
`protected int`	`m_periodicPruningRate` Prune low-density candidate canopies after every x instances have been seen (if using canopy clustering)
`protected boolean`	`m_PreserveOrder` Preserve order of instances.
`protected ReplaceMissingValues`	`m_ReplaceMissingFilter` replace missing values in training instances.
`protected boolean`	`m_speedUpDistanceCompWithCanopies` Whether to reducet the number of distance calcs done by k-means with canopies
`protected double[]`	`m_squaredErrors` Holds the squared errors for all clusters.
`protected double`	`m_t1` The t1 radius to pass through to Canopy
`protected double`	`m_t2` The t2 radius to pass through to Canopy
`static int`	`RANDOM`
`static Tag[]`	`TAGS_SELECTION` Initialization methods

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

Fields inherited from class weka.clusterers.AbstractClusterer
m_Debug, m_DoNotCheckCapabilities

Constructor Summary

Constructors
Constructor and Description

SimpleKMeans()
the default constructor.

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

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`buildClusterer(Instances data)` Generates a clusterer.
`protected void`	`canopyInit(Instances data)` Initialize with the canopy centers of the Canopy clustering method
`java.lang.String`	`canopyMaxNumCanopiesToHoldInMemoryTipText()` Returns the tip text for this property.
`java.lang.String`	`canopyMinimumCanopyDensityTipText()` Returns the tip text for this property.
`java.lang.String`	`canopyPeriodicPruningRateTipText()` Returns the tip text for this property.
`java.lang.String`	`canopyT1TipText()` Tip text for this property
`java.lang.String`	`canopyT2TipText()` Tip text for this property
`int`	`clusterInstance(Instance instance)` Classifies a given instance.
`java.lang.String`	`displayStdDevsTipText()` Returns the tip text for this property.
`java.lang.String`	`distanceFunctionTipText()` Returns the tip text for this property.
`java.lang.String`	`dontReplaceMissingValuesTipText()` Returns the tip text for this property.
`protected void`	`farthestFirstInit(Instances data)` Initialize with the fartherst first centers
`java.lang.String`	`fastDistanceCalcTipText()` Returns the tip text for this property.
`int[]`	`getAssignments()` Gets the assignments for each instance.
`int`	`getCanopyMaxNumCanopiesToHoldInMemory()` Get the maximum number of candidate canopies to retain in memory during training.
`double`	`getCanopyMinimumCanopyDensity()` Get the minimum T2-based density below which a canopy will be pruned during periodic pruning.
`int`	`getCanopyPeriodicPruningRate()` Get the how often to prune low density canopies during training (if using canopy clustering)
`double`	`getCanopyT1()` Get the t1 radius to use when canopy clustering is being used as start points and/or to reduce the number of distance calcs
`double`	`getCanopyT2()` Get the t2 radius to use when canopy clustering is being used as start points and/or to reduce the number of distance calcs
`Capabilities`	`getCapabilities()` Returns default capabilities of the clusterer.
`Instances`	`getClusterCentroids()` Gets the the cluster centroids.
`double[][][]`	`getClusterNominalCounts()` Returns for each cluster the weighted frequency counts for the values of each nominal attribute.
`double[]`	`getClusterSizes()` Gets the sum of weights for all the instances in each cluster.
`Instances`	`getClusterStandardDevs()` Gets the standard deviations of the numeric attributes in each cluster.
`boolean`	`getDisplayStdDevs()` Gets whether standard deviations and nominal count.
`DistanceFunction`	`getDistanceFunction()` returns the distance function currently in use.
`boolean`	`getDontReplaceMissingValues()` Gets whether missing values are to be replaced.
`boolean`	`getFastDistanceCalc()` Gets whether to use faster distance calculation.
`SelectedTag`	`getInitializationMethod()` Get the initialization method to use
`int`	`getMaxIterations()` gets the number of maximum iterations to be executed.
`int`	`getNumClusters()` gets the number of clusters to generate.
`int`	`getNumExecutionSlots()` Get the degree of parallelism to use.
`java.lang.String[]`	`getOptions()` Gets the current settings of SimpleKMeans.
`boolean`	`getPreserveInstancesOrder()` Gets whether order of instances must be preserved.
`boolean`	`getReduceNumberOfDistanceCalcsViaCanopies()` Get whether to use canopies to reduce the number of distance computations required
`java.lang.String`	`getRevision()` Returns the revision string.
`double`	`getSquaredError()` Gets the squared error for all clusters.
`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.
`java.lang.String`	`globalInfo()` Returns a string describing this clusterer.
`java.lang.String`	`initializationMethodTipText()` Returns the tip text for this property.
`protected void`	`kMeansPlusPlusInit(Instances data)` Initialize using the k-means++ method
`protected boolean`	`launchAssignToClusters(Instances insts, int[] clusterAssignments)` Launch the tasks that assign instances to clusters
`protected int`	`launchMoveCentroids(Instances[] clusters)` Launch the move centroids tasks
`java.util.Enumeration<Option>`	`listOptions()` Returns an enumeration describing the available options.
`static void`	`main(java.lang.String[] args)` Main method for executing this class.
`java.lang.String`	`maxIterationsTipText()` Returns the tip text for this property.
`protected double[]`	`moveCentroid(int centroidIndex, Instances members, boolean updateClusterInfo, boolean addToCentroidInstances)` Move the centroid to it's new coordinates.
`int`	`numberOfClusters()` Returns the number of clusters.
`java.lang.String`	`numClustersTipText()` Returns the tip text for this property.
`java.lang.String`	`numExecutionSlotsTipText()` Returns the tip text for this property
`java.lang.String`	`preserveInstancesOrderTipText()` Returns the tip text for this property.
`java.lang.String`	`reduceNumberOfDistanceCalcsViaCanopiesTipText()` Returns the tip text for this property.
`void`	`setCanopyMaxNumCanopiesToHoldInMemory(int max)` Set the maximum number of candidate canopies to retain in memory during training.
`void`	`setCanopyMinimumCanopyDensity(double dens)` Set the minimum T2-based density below which a canopy will be pruned during periodic pruning.
`void`	`setCanopyPeriodicPruningRate(int p)` Set the how often to prune low density canopies during training (if using canopy clustering)
`void`	`setCanopyT1(double t1)` Set the t1 radius to use when canopy clustering is being used as start points and/or to reduce the number of distance calcs
`void`	`setCanopyT2(double t2)` Set the t2 radius to use when canopy clustering is being used as start points and/or to reduce the number of distance calcs
`void`	`setDisplayStdDevs(boolean stdD)` Sets whether standard deviations and nominal count.
`void`	`setDistanceFunction(DistanceFunction df)` sets the distance function to use for instance comparison.
`void`	`setDontReplaceMissingValues(boolean r)` Sets whether missing values are to be replaced.
`void`	`setFastDistanceCalc(boolean value)` Sets whether to use faster distance calculation.
`void`	`setInitializationMethod(SelectedTag method)` Set the initialization method to use
`void`	`setMaxIterations(int n)` set the maximum number of iterations to be executed.
`void`	`setNumClusters(int n)` set the number of clusters to generate.
`void`	`setNumExecutionSlots(int slots)` Set the degree of parallelism to use.
`void`	`setOptions(java.lang.String[] options)` Parses a given list of options.
`void`	`setPreserveInstancesOrder(boolean r)` Sets whether order of instances must be preserved.
`void`	`setReduceNumberOfDistanceCalcsViaCanopies(boolean c)` Set whether to use canopies to reduce the number of distance computations required
`protected void`	`startExecutorPool()` Start the pool of execution threads
`java.lang.String`	`toString()` return a string describing this clusterer.

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
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

- Field Detail
  - m_ReplaceMissingFilter
```
protected ReplaceMissingValues m_ReplaceMissingFilter
```
    replace missing values in training instances.
  - m_NumClusters
```
protected int m_NumClusters
```
    number of clusters to generate.
  - m_initialStartPoints
```
protected Instances m_initialStartPoints
```
    Holds the initial start points, as supplied by the initialization method used
  - m_ClusterCentroids
```
protected Instances m_ClusterCentroids
```
    holds the cluster centroids.
  - m_ClusterStdDevs
```
protected Instances m_ClusterStdDevs
```
    Holds the standard deviations of the numeric attributes in each cluster.
  - m_ClusterNominalCounts
```
protected double[][][] m_ClusterNominalCounts
```
    For each cluster, holds the frequency counts for the values of each nominal attribute.
  - m_ClusterMissingCounts
```
protected double[][] m_ClusterMissingCounts
```
  - m_FullMeansOrMediansOrModes
```
protected double[] m_FullMeansOrMediansOrModes
```
    Stats on the full data set for comparison purposes. In case the attribute is numeric the value is the mean if is being used the Euclidian distance or the median if Manhattan distance and if the attribute is nominal then it's mode is saved.
  - m_FullStdDevs
```
protected double[] m_FullStdDevs
```
  - m_FullNominalCounts
```
protected double[][] m_FullNominalCounts
```
  - m_FullMissingCounts
```
protected double[] m_FullMissingCounts
```
  - m_displayStdDevs
```
protected boolean m_displayStdDevs
```
    Display standard deviations for numeric atts.
  - m_dontReplaceMissing
```
protected boolean m_dontReplaceMissing
```
    Replace missing values globally?
  - m_ClusterSizes
```
protected double[] m_ClusterSizes
```
    The number of instances in each cluster.
  - m_MaxIterations
```
protected int m_MaxIterations
```
    Maximum number of iterations to be executed.
  - m_Iterations
```
protected int m_Iterations
```
    Keep track of the number of iterations completed before convergence.
  - m_squaredErrors
```
protected double[] m_squaredErrors
```
    Holds the squared errors for all clusters.
  - m_DistanceFunction
```
protected DistanceFunction m_DistanceFunction
```
    the distance function used.
  - m_PreserveOrder
```
protected boolean m_PreserveOrder
```
    Preserve order of instances.
  - m_Assignments
```
protected int[] m_Assignments
```
    Assignments obtained.
  - m_FastDistanceCalc
```
protected boolean m_FastDistanceCalc
```
    whether to use fast calculation of distances (using a cut-off).
  - RANDOM
```
public static final int RANDOM
```
    See Also:
    
    Constant Field Values
  - KMEANS_PLUS_PLUS
```
public static final int KMEANS_PLUS_PLUS
```
    See Also:
    
    Constant Field Values
  - CANOPY
```
public static final int CANOPY
```
    See Also:
    
    Constant Field Values
  - FARTHEST_FIRST
```
public static final int FARTHEST_FIRST
```
    See Also:
    
    Constant Field Values
  - TAGS_SELECTION
```
public static final Tag[] TAGS_SELECTION
```
    Initialization methods
  - m_initializationMethod
```
protected int m_initializationMethod
```
    The initialization method to use
  - m_speedUpDistanceCompWithCanopies
```
protected boolean m_speedUpDistanceCompWithCanopies
```
    Whether to reducet the number of distance calcs done by k-means with canopies
  - m_centroidCanopyAssignments
```
protected java.util.List<long[]> m_centroidCanopyAssignments
```
    Canopies that each centroid falls into (determined by T1 radius)
  - m_dataPointCanopyAssignments
```
protected java.util.List<long[]> m_dataPointCanopyAssignments
```
    Canopies that each training instance falls into (determined by T1 radius)
  - m_canopyClusters
```
protected Canopy m_canopyClusters
```
    The canopy clusterer (if being used)
  - m_maxCanopyCandidates
```
protected int m_maxCanopyCandidates
```
    The maximum number of candidate canopies to hold in memory at any one time (if using canopy clustering)
  - m_periodicPruningRate
```
protected int m_periodicPruningRate
```
    Prune low-density candidate canopies after every x instances have been seen (if using canopy clustering)
  - m_minClusterDensity
```
protected double m_minClusterDensity
```
    The minimum cluster density (according to T2 distance) allowed. Used when periodically pruning candidate canopies (if using canopy clustering)
  - m_t2
```
protected double m_t2
```
    The t2 radius to pass through to Canopy
  - m_t1
```
protected double m_t1
```
    The t1 radius to pass through to Canopy
  - m_executionSlots
```
protected int m_executionSlots
```
    Number of threads to run
  - m_executorPool
```
protected transient java.util.concurrent.ExecutorService m_executorPool
```
    For parallel execution mode
  - m_completed
```
protected int m_completed
```
  - m_failed
```
protected int m_failed
```
- Constructor Detail
  - SimpleKMeans
```
public SimpleKMeans()
```
    the default constructor.
- Method Detail
  - startExecutorPool
```
protected void startExecutorPool()
```
    Start the pool of execution threads
  - getTechnicalInformation
```
public TechnicalInformation getTechnicalInformation()
```
    Description copied from interface: TechnicalInformationHandler
    
    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 TechnicalInformationHandler
    
    Returns:
    
    the technical information about this class
  - 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
  - getCapabilities
```
public Capabilities getCapabilities()
```
    Returns default capabilities of the clusterer.
    
    Specified by:
    
    getCapabilities in interface Clusterer
    
    Specified by:
    
    getCapabilities in interface CapabilitiesHandler
    
    Overrides:
    
    getCapabilities in class AbstractClusterer
    
    Returns:
    
    the capabilities of this clusterer
    
    See Also:
    
    Capabilities
  - launchMoveCentroids
```
protected int launchMoveCentroids(Instances[] clusters)
```
    Launch the move centroids tasks
    
    Parameters:
    
    clusters - the cluster centroids
    
    Returns:
    
    the number of empty clusters
  - launchAssignToClusters
```
protected boolean launchAssignToClusters(Instances insts,
                                         int[] clusterAssignments)
                                  throws java.lang.Exception
```
    Launch the tasks that assign instances to clusters
    
    Parameters:
    
    insts - the instances to be clustered
    
    clusterAssignments - the array of cluster assignments
    
    Returns:
    
    true if k means has converged
    
    Throws:
    
    java.lang.Exception - if a problem occurs
  - buildClusterer
```
public void buildClusterer(Instances data)
                    throws java.lang.Exception
```
    Generates a clusterer. Has to initialize all fields of the clusterer that are not being set via options.
    
    Specified by:
    
    buildClusterer in interface Clusterer
    
    Specified by:
    
    buildClusterer in class AbstractClusterer
    
    Parameters:
    
    data - set of instances serving as training data
    
    Throws:
    
    java.lang.Exception - if the clusterer has not been generated successfully
  - canopyInit
```
protected void canopyInit(Instances data)
                   throws java.lang.Exception
```
    Initialize with the canopy centers of the Canopy clustering method
    
    Parameters:
    
    data - the training data
    
    Throws:
    
    java.lang.Exception - if a problem occurs
  - farthestFirstInit
```
protected void farthestFirstInit(Instances data)
                          throws java.lang.Exception
```
    Initialize with the fartherst first centers
    
    Parameters:
    
    data - the training data
    
    Throws:
    
    java.lang.Exception - if a problem occurs
  - kMeansPlusPlusInit
```
protected void kMeansPlusPlusInit(Instances data)
                           throws java.lang.Exception
```
    Initialize using the k-means++ method
    
    Parameters:
    
    data - the training data
    
    Throws:
    
    java.lang.Exception - if a problem occurs
  - moveCentroid
```
protected double[] moveCentroid(int centroidIndex,
                                Instances members,
                                boolean updateClusterInfo,
                                boolean addToCentroidInstances)
```
    Move the centroid to it's new coordinates. Generate the centroid coordinates based on it's members (objects assigned to the cluster of the centroid) and the distance function being used.
    
    Parameters:
    
    centroidIndex - index of the centroid which the coordinates will be computed
    
    members - the objects that are assigned to the cluster of this centroid
    
    updateClusterInfo - if the method is supposed to update the m_Cluster arrays
    
    addToCentroidInstances - true if the method is to add the computed coordinates to the Instances holding the centroids
    
    Returns:
    
    the centroid coordinates
  - clusterInstance
```
public int clusterInstance(Instance instance)
                    throws java.lang.Exception
```
    Classifies a given instance.
    
    Specified by:
    
    clusterInstance in interface Clusterer
    
    Overrides:
    
    clusterInstance in class AbstractClusterer
    
    Parameters:
    
    instance - the instance to be assigned to a cluster
    
    Returns:
    
    the number of the assigned cluster as an interger if the class is enumerated, otherwise the predicted value
    
    Throws:
    
    java.lang.Exception - if instance could not be classified successfully
  - numberOfClusters
```
public int numberOfClusters()
                     throws java.lang.Exception
```
    Returns the number of clusters.
    
    Specified by:
    
    numberOfClusters in interface Clusterer
    
    Specified by:
    
    numberOfClusters in class AbstractClusterer
    
    Returns:
    
    the number of clusters generated for a training dataset.
    
    Throws:
    
    java.lang.Exception - if number of clusters could not be returned successfully
  - listOptions
```
public java.util.Enumeration<Option> listOptions()
```
    Returns an enumeration describing the available options.
    
    Specified by:
    
    listOptions in interface OptionHandler
    
    Overrides:
    
    listOptions in class RandomizableClusterer
    
    Returns:
    
    an enumeration of all the available options.
  - numClustersTipText
```
public java.lang.String numClustersTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setNumClusters
```
public void setNumClusters(int n)
                    throws java.lang.Exception
```
    set the number of clusters to generate.
    
    Specified by:
    
    setNumClusters in interface NumberOfClustersRequestable
    
    Parameters:
    
    n - the number of clusters to generate
    
    Throws:
    
    java.lang.Exception - if number of clusters is negative
  - getNumClusters
```
public int getNumClusters()
```
    gets the number of clusters to generate.
    
    Returns:
    
    the number of clusters to generate
  - initializationMethodTipText
```
public java.lang.String initializationMethodTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setInitializationMethod
```
public void setInitializationMethod(SelectedTag method)
```
    Set the initialization method to use
    
    Parameters:
    
    method - the initialization method to use
  - getInitializationMethod
```
public SelectedTag getInitializationMethod()
```
    Get the initialization method to use
    
    Returns:
    
    method the initialization method to use
  - reduceNumberOfDistanceCalcsViaCanopiesTipText
```
public java.lang.String reduceNumberOfDistanceCalcsViaCanopiesTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setReduceNumberOfDistanceCalcsViaCanopies
```
public void setReduceNumberOfDistanceCalcsViaCanopies(boolean c)
```
    Set whether to use canopies to reduce the number of distance computations required
    
    Parameters:
    
    c - true if canopies are to be used to reduce the number of distance computations
  - getReduceNumberOfDistanceCalcsViaCanopies
```
public boolean getReduceNumberOfDistanceCalcsViaCanopies()
```
    Get whether to use canopies to reduce the number of distance computations required
    
    Returns:
    
    true if canopies are to be used to reduce the number of distance computations
  - canopyPeriodicPruningRateTipText
```
public java.lang.String canopyPeriodicPruningRateTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setCanopyPeriodicPruningRate
```
public void setCanopyPeriodicPruningRate(int p)
```
    Set the how often to prune low density canopies during training (if using canopy clustering)
    
    Parameters:
    
    p - how often (every p instances) to prune low density canopies
  - getCanopyPeriodicPruningRate
```
public int getCanopyPeriodicPruningRate()
```
    Get the how often to prune low density canopies during training (if using canopy clustering)
    
    Returns:
    
    how often (every p instances) to prune low density canopies
  - canopyMinimumCanopyDensityTipText
```
public java.lang.String canopyMinimumCanopyDensityTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setCanopyMinimumCanopyDensity
```
public void setCanopyMinimumCanopyDensity(double dens)
```
    Set the minimum T2-based density below which a canopy will be pruned during periodic pruning.
    
    Parameters:
    
    dens - the minimum canopy density
  - getCanopyMinimumCanopyDensity
```
public double getCanopyMinimumCanopyDensity()
```
    Get the minimum T2-based density below which a canopy will be pruned during periodic pruning.
    
    Returns:
    
    the minimum canopy density
  - canopyMaxNumCanopiesToHoldInMemoryTipText
```
public java.lang.String canopyMaxNumCanopiesToHoldInMemoryTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setCanopyMaxNumCanopiesToHoldInMemory
```
public void setCanopyMaxNumCanopiesToHoldInMemory(int max)
```
    Set the maximum number of candidate canopies to retain in memory during training. T2 distance and data characteristics determine how many candidate canopies are formed before periodic and final pruning are performed. There may not be enough memory available if T2 is set too low.
    
    Parameters:
    
    max - the maximum number of candidate canopies to retain in memory during training
  - getCanopyMaxNumCanopiesToHoldInMemory
```
public int getCanopyMaxNumCanopiesToHoldInMemory()
```
    Get the maximum number of candidate canopies to retain in memory during training. T2 distance and data characteristics determine how many candidate canopies are formed before periodic and final pruning are performed. There may not be enough memory available if T2 is set too low.
    
    Returns:
    
    the maximum number of candidate canopies to retain in memory during training
  - canopyT2TipText
```
public java.lang.String canopyT2TipText()
```
    Tip text for this property
    
    Returns:
    
    the tip text for this property
  - setCanopyT2
```
public void setCanopyT2(double t2)
```
    Set the t2 radius to use when canopy clustering is being used as start points and/or to reduce the number of distance calcs
    
    Parameters:
    
    t2 - the t2 radius to use
  - getCanopyT2
```
public double getCanopyT2()
```
    Get the t2 radius to use when canopy clustering is being used as start points and/or to reduce the number of distance calcs
    
    Returns:
    
    the t2 radius to use
  - canopyT1TipText
```
public java.lang.String canopyT1TipText()
```
    Tip text for this property
    
    Returns:
    
    the tip text for this property
  - setCanopyT1
```
public void setCanopyT1(double t1)
```
    Set the t1 radius to use when canopy clustering is being used as start points and/or to reduce the number of distance calcs
    
    Parameters:
    
    t1 - the t1 radius to use
  - getCanopyT1
```
public double getCanopyT1()
```
    Get the t1 radius to use when canopy clustering is being used as start points and/or to reduce the number of distance calcs
    
    Returns:
    
    the t1 radius to use
  - maxIterationsTipText
```
public java.lang.String maxIterationsTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setMaxIterations
```
public void setMaxIterations(int n)
                      throws java.lang.Exception
```
    set the maximum number of iterations to be executed.
    
    Parameters:
    
    n - the maximum number of iterations
    
    Throws:
    
    java.lang.Exception - if maximum number of iteration is smaller than 1
  - getMaxIterations
```
public int getMaxIterations()
```
    gets the number of maximum iterations to be executed.
    
    Returns:
    
    the number of clusters to generate
  - displayStdDevsTipText
```
public java.lang.String displayStdDevsTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setDisplayStdDevs
```
public void setDisplayStdDevs(boolean stdD)
```
    Sets whether standard deviations and nominal count. Should be displayed in the clustering output.
    
    Parameters:
    
    stdD - true if std. devs and counts should be displayed
  - getDisplayStdDevs
```
public boolean getDisplayStdDevs()
```
    Gets whether standard deviations and nominal count. Should be displayed in the clustering output.
    
    Returns:
    
    true if std. devs and counts should be displayed
  - dontReplaceMissingValuesTipText
```
public java.lang.String dontReplaceMissingValuesTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setDontReplaceMissingValues
```
public void setDontReplaceMissingValues(boolean r)
```
    Sets whether missing values are to be replaced.
    
    Parameters:
    
    r - true if missing values are to be replaced
  - getDontReplaceMissingValues
```
public boolean getDontReplaceMissingValues()
```
    Gets whether missing values are to be replaced.
    
    Returns:
    
    true if missing values are to be replaced
  - distanceFunctionTipText
```
public java.lang.String distanceFunctionTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getDistanceFunction
```
public DistanceFunction getDistanceFunction()
```
    returns the distance function currently in use.
    
    Returns:
    
    the distance function
  - setDistanceFunction
```
public void setDistanceFunction(DistanceFunction df)
                         throws java.lang.Exception
```
    sets the distance function to use for instance comparison.
    
    Parameters:
    
    df - the new distance function to use
    
    Throws:
    
    java.lang.Exception - if instances cannot be processed
  - preserveInstancesOrderTipText
```
public java.lang.String preserveInstancesOrderTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setPreserveInstancesOrder
```
public void setPreserveInstancesOrder(boolean r)
```
    Sets whether order of instances must be preserved.
    
    Parameters:
    
    r - true if missing values are to be replaced
  - getPreserveInstancesOrder
```
public boolean getPreserveInstancesOrder()
```
    Gets whether order of instances must be preserved.
    
    Returns:
    
    true if missing values are to be replaced
  - fastDistanceCalcTipText
```
public java.lang.String fastDistanceCalcTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setFastDistanceCalc
```
public void setFastDistanceCalc(boolean value)
```
    Sets whether to use faster distance calculation.
    
    Parameters:
    
    value - true if faster calculation to be used
  - getFastDistanceCalc
```
public boolean getFastDistanceCalc()
```
    Gets whether to use faster distance calculation.
    
    Returns:
    
    true if faster calculation is used
  - numExecutionSlotsTipText
```
public java.lang.String numExecutionSlotsTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setNumExecutionSlots
```
public void setNumExecutionSlots(int slots)
```
    Set the degree of parallelism to use.
    
    Parameters:
    
    slots - the number of tasks to run in parallel when computing the nearest neighbors and evaluating different values of k between the lower and upper bounds
  - getNumExecutionSlots
```
public int getNumExecutionSlots()
```
    Get the degree of parallelism to use.
    
    Returns:
    
    the number of tasks to run in parallel when computing the nearest neighbors and evaluating different values of k between the lower and upper bounds
  - setOptions
```
public void setOptions(java.lang.String[] options)
                throws java.lang.Exception
```
    Parses a given list of options.
    Valid options are:
```
 -N <num>
  Number of clusters.
  (default 2).
 
```
```
 -init
  Initialization method to use.
  0 = random, 1 = k-means++, 2 = canopy, 3 = farthest first.
  (default = 0)
 
```
```
 -C
  Use canopies to reduce the number of distance calculations.
 
```
```
 -max-candidates <num>
  Maximum number of candidate canopies to retain in memory
  at any one time when using canopy clustering.
  T2 distance plus, data characteristics,
  will determine how many candidate canopies are formed before
  periodic and final pruning are performed, which might result
  in exceess memory consumption. This setting avoids large numbers
  of candidate canopies consuming memory. (default = 100)
 
```
```
 -periodic-pruning <num>
  How often to prune low density canopies when using canopy clustering. 
  (default = every 10,000 training instances)
 
```
```
 -min-density
  Minimum canopy density, when using canopy clustering, below which
   a canopy will be pruned during periodic pruning. (default = 2 instances)
 
```
```
 -t2
  The T2 distance to use when using canopy clustering. Values < 0 indicate that
  a heuristic based on attribute std. deviation should be used to set this.
  (default = -1.0)
 
```
```
 -t1
  The T1 distance to use when using canopy clustering. A value < 0 is taken as a
  positive multiplier for T2. (default = -1.5)
 
```
```
 -V
  Display std. deviations for centroids.
 
```
```
 -M
  Don't replace missing values with mean/mode.
 
```
```
 -A <classname and options>
  Distance function to use.
  (default: weka.core.EuclideanDistance)
 
```
```
 -I <num>
  Maximum number of iterations.
 
```
```
 -O
  Preserve order of instances.
 
```
```
 -fast
  Enables faster distance calculations, using cut-off values.
  Disables the calculation/output of squared errors/distances.
 
```
```
 -num-slots <num>
  Number of execution slots.
  (default 1 - i.e. no parallelism)
 
```
```
 -S <num>
  Random number seed.
  (default 10)
 
```
```
 -output-debug-info
  If set, clusterer is run in debug mode and
  may output additional info to the console
 
```
```
 -do-not-check-capabilities
  If set, clusterer capabilities are not checked before clusterer is built
  (use with caution).
 
```
    Specified by:
    
    setOptions in interface OptionHandler
    
    Overrides:
    
    setOptions in class 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 OptionHandler
    
    Overrides:
    
    getOptions in class 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
  - getClusterCentroids
```
public Instances getClusterCentroids()
```
    Gets the the cluster centroids.
    
    Returns:
    
    the cluster centroids
  - getClusterStandardDevs
```
public Instances getClusterStandardDevs()
```
    Gets the standard deviations of the numeric attributes in each cluster.
    
    Returns:
    
    the standard deviations of the numeric attributes in each cluster
  - getClusterNominalCounts
```
public double[][][] getClusterNominalCounts()
```
    Returns for each cluster the weighted frequency counts for the values of each nominal attribute.
    
    Returns:
    
    the counts
  - getSquaredError
```
public double getSquaredError()
```
    Gets the squared error for all clusters.
    
    Returns:
    
    the squared error, NaN if fast distance calculation is used
    
    See Also:
    
    m_FastDistanceCalc
  - getClusterSizes
```
public double[] getClusterSizes()
```
    Gets the sum of weights for all the instances in each cluster.
    
    Returns:
    
    The number of instances in each cluster
  - getAssignments
```
public int[] getAssignments()
                     throws java.lang.Exception
```
    Gets the assignments for each instance.
    
    Returns:
    
    Array of indexes of the centroid assigned to each instance
    
    Throws:
    
    java.lang.Exception - if order of instances wasn't preserved or no assignments were made
  - getRevision
```
public java.lang.String getRevision()
```
    Returns the revision string.
    
    Specified by:
    
    getRevision in interface RevisionHandler
    
    Overrides:
    
    getRevision in class AbstractClusterer
    
    Returns:
    
    the revision
  - main
```
public static void main(java.lang.String[] args)
```
    Main method for executing this class.
    
    Parameters:
    
    args - use -h to list all parameters

Class SimpleKMeans

Field Summary

Fields inherited from class weka.clusterers.RandomizableClusterer

Fields inherited from class weka.clusterers.AbstractClusterer

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_ReplaceMissingFilter

m_NumClusters

m_initialStartPoints

m_ClusterCentroids

m_ClusterStdDevs

m_ClusterNominalCounts

m_ClusterMissingCounts

m_FullMeansOrMediansOrModes

m_FullStdDevs

m_FullNominalCounts

m_FullMissingCounts

m_displayStdDevs

m_dontReplaceMissing

m_ClusterSizes

m_MaxIterations

m_Iterations

m_squaredErrors

m_DistanceFunction

m_PreserveOrder

m_Assignments

m_FastDistanceCalc

RANDOM

KMEANS_PLUS_PLUS

CANOPY

FARTHEST_FIRST

TAGS_SELECTION

m_initializationMethod

m_speedUpDistanceCompWithCanopies

m_centroidCanopyAssignments

m_dataPointCanopyAssignments

m_canopyClusters

m_maxCanopyCandidates

m_periodicPruningRate

m_minClusterDensity

m_t2

m_t1

m_executionSlots

m_executorPool

m_completed

m_failed

Constructor Detail

SimpleKMeans

Method Detail

startExecutorPool

getTechnicalInformation

globalInfo

getCapabilities

launchMoveCentroids

launchAssignToClusters

buildClusterer

canopyInit

farthestFirstInit

kMeansPlusPlusInit

moveCentroid

clusterInstance

numberOfClusters

listOptions

numClustersTipText

setNumClusters

getNumClusters

initializationMethodTipText

setInitializationMethod

getInitializationMethod

reduceNumberOfDistanceCalcsViaCanopiesTipText

setReduceNumberOfDistanceCalcsViaCanopies

getReduceNumberOfDistanceCalcsViaCanopies

canopyPeriodicPruningRateTipText

setCanopyPeriodicPruningRate

getCanopyPeriodicPruningRate

canopyMinimumCanopyDensityTipText