SimpleKMeans

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## 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, OptionHandler, Randomizable, RevisionHandler, WeightedInstancesHandler

    public class SimpleKMeans

extends RandomizableClusterer
implements NumberOfClustersRequestable, WeightedInstancesHandler

Cluster data using the k means algorithm

Valid options are:

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

     -V
      Display std. deviations for centroids.

     -M
      Replace missing values with mean/mode.

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

     -A <classname and options>
      Distance function to be used for instance comparison
      (default weka.core.EuclidianDistance)

     -I <num>
      Maximum number of iterations.

     -O
      Preserve order of instances.

Version:
  $Revision: 1.40 $
Author:
  Mark Hall (mhall@cs.waikato.ac.nz), Eibe Frank (eibe@cs.waikato.ac.nz)
See Also:
  `RandomizableClusterer`, Serialized Form

Constructor Summary
`SimpleKMeans()` the default constructor

Method Summary
`void` | `buildClusterer(Instances data)` Generates a clusterer.
`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
`int[]` | `getAssignments()` Gets the assignments for each instance
`Capabilities` | `getCapabilities()` Returns default capabilities of the clusterer.
`Instances` | `getClusterCentroids()` Gets the the cluster centroids
`int[][][]` | `getClusterNominalCounts()` Returns for each cluster the frequency counts for the values of each nominal attribute
`int[]` | `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 Should be displayed in the clustering output
`DistanceFunction` | `getDistanceFunction()` returns the distance function currently in use.
`boolean` | `getDontReplaceMissingValues()` Gets whether missing values are to be replaced
`int` | `getMaxIterations()` gets the number of maximum iterations to be executed
`int` | `getNumClusters()` gets the number of clusters to generate
`java.lang.String[]` | `getOptions()` Gets the current settings of SimpleKMeans
`boolean` | `getPreserveInstancesOrder()` Gets whether order of instances must be preserved
`java.lang.String` | `getRevision()` Returns the revision string.
`double` | `getSquaredError()` Gets the squared error for all clusters
`java.lang.String` | `globalInfo()` Returns a string describing this clusterer
`java.util.Enumeration` | `listOptions()` Returns an enumeration describing the available options.
`static void` | `main(java.lang.String[] argv)` Main method for testing 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` | `preserveInstancesOrderTipText()` Returns the tip text for this property
`void` | `setDisplayStdDevs(boolean stdD)` Sets whether standard deviations and nominal count Should be displayed in the clustering output
`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` | `setMaxIterations(int n)` set the maximum number of iterations to be executed
`void` | `setNumClusters(int n)` set the number of clusters to generate
`void` | `setOptions(java.lang.String[] options)` Parses a given list of options.
`void` | `setPreserveInstancesOrder(boolean r)` Sets whether order of instances must be preserved
`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
`distributionForInstance, forName, makeCopies, makeCopy`

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

Constructor Detail

### SimpleKMeans

    public SimpleKMeans()

  the default constructor

Method Detail

### 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 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

### 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

### 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).

       -V
        Display std. deviations for centroids.

       -M
        Replace missing values with mean/mode.

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

       -A <classname and options>
        Distance function to be used for instance comparison
        (default weka.core.EuclidianDistance)

       -I <num>
        Maximum number of iterations.

       -O
        Preserve order of instances.

  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 int[][][] getClusterNominalCounts()

  Returns for each cluster the 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

### getClusterSizes

    public int[] 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`
  Returns:
    the revision

### main

    public static void main(java.lang.String[] argv)

  Main method for testing this class.

  Parameters:
    `argv` - should contain the following arguments:

    -t training file [-N number of clusters]

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