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: 10537 $
Author:: Mark Hall (mhall@cs.waikato.ac.nz), Eibe Frank (eibe@cs.waikato.ac.nz)
See Also:: RandomizableClusterer, Serialized Form

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.
`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
    
    Overrides:
    
    getRevision in class AbstractClusterer
    
    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]

Class SimpleKMeans

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

Constructor Detail

SimpleKMeans

Method Detail

globalInfo

getCapabilities

buildClusterer

clusterInstance

numberOfClusters

listOptions

numClustersTipText

setNumClusters

getNumClusters

maxIterationsTipText

setMaxIterations

getMaxIterations

displayStdDevsTipText

setDisplayStdDevs

getDisplayStdDevs

dontReplaceMissingValuesTipText

setDontReplaceMissingValues

getDontReplaceMissingValues

distanceFunctionTipText

getDistanceFunction

setDistanceFunction

preserveInstancesOrderTipText

setPreserveInstancesOrder

getPreserveInstancesOrder

setOptions

getOptions

toString

getClusterCentroids

getClusterStandardDevs

getClusterNominalCounts

getSquaredError

getClusterSizes

getAssignments

getRevision

main