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Class weka.clusterers.SimpleKMeans

java.lang.Object
   |
   +----weka.clusterers.Clusterer
           |
           +----weka.clusterers.SimpleKMeans

public class SimpleKMeans

extends Clusterer

implements OptionHandler

-N
Specify the number of clusters to generate.

-S
Specify random number seed.

Author:

Mark Hall (mhall@cs.waikato.ac.nz)

See Also:

Clusterer, OptionHandler

SimpleKMeans()

buildClusterer(Instances)

Generates a clusterer.

clusterInstance(Instance)

Classifies a given instance.

getNumClusters()

gets the number of clusters to generate

getOptions()

Gets the current settings of SimpleKMeans

getSeed()

Get the random number seed

globalInfo()

Returns a string describing this clusterer

listOptions()

Returns an enumeration describing the available options.

main(String[])

Main method for testing this class.

numberOfClusters()

Returns the number of clusters.

numClustersTipText()

Returns the tip text for this property

seedTipText()

Returns the tip text for this property

setNumClusters(int)

set the number of clusters to generate

setOptions(String[])

Parses a given list of options.

setSeed(int)

Set the random number seed

toString()

return a string describing this clusterer

 public SimpleKMeans()

 public String globalInfo()

Returns a string describing this clusterer

Returns:

a description of the evaluator suitable for displaying in the explorer/experimenter gui

 public void buildClusterer(Instances data) throws Exception

Generates a clusterer. Has to initialize all fields of the clusterer that are not being set via options.

Parameters:

data - set of instances serving as training data

Throws: Exception

if the clusterer has not been generated successfully

Overrides:

buildClusterer in class Clusterer

 public int clusterInstance(Instance instance) throws Exception

Classifies a given instance.

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: Exception

if instance could not be classified successfully

Overrides:

clusterInstance in class Clusterer

 public int numberOfClusters() throws Exception

Returns the number of clusters.

Returns:

the number of clusters generated for a training dataset.

Throws: Exception

if number of clusters could not be returned successfully

Overrides:

numberOfClusters in class Clusterer

 public Enumeration listOptions()

Returns an enumeration describing the available options.

Valid options are:

-N
Specify the number of clusters to generate. If omitted, EM will use cross validation to select the number of clusters automatically.

-S
Specify random number seed.

Returns:

an enumeration of all the available options

 public String numClustersTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

 public void setNumClusters(int n)

set the number of clusters to generate

Parameters:

n - the number of clusters to generate

 public int getNumClusters()

gets the number of clusters to generate

Returns:

the number of clusters to generate

 public String seedTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

 public void setSeed(int s)

Set the random number seed

Parameters:

s - the seed

 public int getSeed()

Get the random number seed

Returns:

the seed

 public void setOptions(String options[]) throws Exception

Parses a given list of options.

Parameters:

options - the list of options as an array of strings

Throws: Exception

if an option is not supported

 public String[] getOptions()

Gets the current settings of SimpleKMeans

Returns:

an array of strings suitable for passing to setOptions()

 public String toString()

return a string describing this clusterer

Returns:

a description of the clusterer as a string

Overrides:

toString in class Object

 public static void main(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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