weka.clusterers
Class SimpleKMeans

java.lang.Object
  weka.clusterers.Clusterer
      weka.clusterers.SimpleKMeans

All Implemented Interfaces:

java.lang.Cloneable, OptionHandler, java.io.Serializable, WeightedInstancesHandler

public class SimpleKMeans

extends Clusterer

implements OptionHandler, WeightedInstancesHandler

Simple k means clustering class. Valid options are:

-N
Specify the number of clusters to generate.

-S
Specify random number seed.

Version:

$Revision: 1.13 $

Author:

Mark Hall (mhall@cs.waikato.ac.nz), Eibe Frank (eibe@cs.waikato.ac.nz)

See Also:

Clusterer, OptionHandler, Serialized Form

Field Summary

private Instances	m_ClusterCentroids holds the cluster centroids
private Instances	m_ClusterStdDevs Holds the standard deviations of attributes in each cluster
private int	m_Iterations Keep track of the number of iterations completed before convergence
private double[]	m_Max attribute max values
private double[]	m_Min attribute min values
private int	m_NumClusters number of clusters to generate
private ReplaceMissingValues	m_ReplaceMissingFilter replace missing values in training instances
private int	m_Seed random seed

Constructor Summary

SimpleKMeans()

Method Summary

void	buildClusterer(Instances data) Generates a clusterer.
int	clusterInstance(Instance instance) Classifies a given instance.
private int	clusterProcessedInstance(Instance instance) clusters an instance that has been through the filters
private double	difference(int index, double val1, double val2) Computes the difference between two given attribute values.
private double	distance(Instance first, Instance second) Calculates the distance between two instances
int	getNumClusters() gets the number of clusters to generate
java.lang.String[]	getOptions() Gets the current settings of SimpleKMeans
int	getSeed() Get the random number seed
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.
private double	norm(double x, int i) Normalizes a given value of a numeric attribute.
int	numberOfClusters() Returns the number of clusters.
java.lang.String	numClustersTipText() Returns the tip text for this property
java.lang.String	seedTipText() Returns the tip text for this property
void	setNumClusters(int n) set the number of clusters to generate
void	setOptions(java.lang.String[] options) Parses a given list of options.
void	setSeed(int s) Set the random number seed
java.lang.String	toString() return a string describing this clusterer
private void	updateMinMax(Instance instance) Updates the minimum and maximum values for all the attributes based on a new instance.

Methods inherited from class weka.clusterers.Clusterer

distributionForInstance, forName, makeCopies

Methods inherited from class java.lang.Object

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

Field Detail

m_ReplaceMissingFilter

private ReplaceMissingValues m_ReplaceMissingFilter

replace missing values in training instances

m_NumClusters

private int m_NumClusters

number of clusters to generate

m_ClusterCentroids

private Instances m_ClusterCentroids

holds the cluster centroids

m_ClusterStdDevs

private Instances m_ClusterStdDevs

Holds the standard deviations of attributes in each cluster

m_Seed

private int m_Seed

random seed

m_Min

private double[] m_Min

attribute min values

m_Max

private double[] m_Max

attribute max values

m_Iterations

private int m_Iterations

Keep track of the number of iterations completed before convergence

Constructor Detail

SimpleKMeans

public SimpleKMeans()

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

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 class Clusterer

Parameters:

data - set of instances serving as training data

Throws:

java.lang.Exception - if the clusterer has not been generated successfully

clusterProcessedInstance

private int clusterProcessedInstance(Instance instance)

clusters an instance that has been through the filters

Parameters:

instance - the instance to assign a cluster to

Returns:

a cluster number

clusterInstance

public int clusterInstance(Instance instance)
                    throws java.lang.Exception

Classifies a given instance.

Overrides:

clusterInstance in class Clusterer

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

distance

private double distance(Instance first,
                        Instance second)

Calculates the distance between two instances

Returns:

the distance between the two given instances, between 0 and 1

difference

private double difference(int index,
                          double val1,
                          double val2)

Computes the difference between two given attribute values.

norm

private double norm(double x,
                    int i)

Normalizes a given value of a numeric attribute.

Parameters:

x - the value to be normalized

i - the attribute's index

updateMinMax

private void updateMinMax(Instance instance)

Updates the minimum and maximum values for all the attributes based on a new instance.

Parameters:

instance - the new instance

numberOfClusters

public int numberOfClusters()
                     throws java.lang.Exception

Returns the number of clusters.

Specified by:

numberOfClusters in class Clusterer

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..

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.

Specified by:

listOptions in interface OptionHandler

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)

set the number of clusters to generate

Parameters:

n - the number of clusters to generate

getNumClusters

public int getNumClusters()

gets the number of clusters to generate

Returns:

the number of clusters to generate

seedTipText

public java.lang.String seedTipText()

Returns the tip text for this property

Returns:

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

setSeed

public void setSeed(int s)

Set the random number seed

Parameters:

s - the seed

getSeed

public int getSeed()

Get the random number seed

Returns:

the seed

setOptions

public void setOptions(java.lang.String[] options)
                throws java.lang.Exception

Parses a given list of options.

Specified by:

setOptions in interface OptionHandler

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

Returns:

an array of strings suitable for passing to setOptions()

toString

public java.lang.String toString()

return a string describing this clusterer

Returns:

a description of the clusterer as a string

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]