weka.clusterers

## Class SimpleKMeans

- 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, 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).

  Version:
    $Revision: 11298 $
  Author:
    Mark Hall (mhall@cs.waikato.ac.nz), Eibe Frank (eibe@cs.waikato.ac.nz)
  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`
    `static int` | `RANDOM`
    `static Tag[]` | `TAGS_SELECTION` Initialization methods

  - ### Constructor Summary

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

  - ### Method Summary

    Methods
    Modifier and Type | Method and Description
    --- | ---
    `void` | `buildClusterer(Instances data)` Generates a clusterer.
    `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.
    `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 number of 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.
    `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.
    `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
    `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, runClusterer, setDebug, setDoNotCheckCapabilities`

    - ### Methods inherited from class java.lang.Object

      `equals, getClass, hashCode, notify, notifyAll, wait, wait, wait`

- - ### Field Detail

    - #### 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

  - ### Constructor Detail

    - #### SimpleKMeans

          public SimpleKMeans()

      the default constructor.

  - ### Method Detail

    - #### 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`

    - #### 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

    - #### 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 number of 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