weka.clusterers

Class FarthestFirst

java.lang.Object

weka.clusterers.AbstractClusterer

weka.clusterers.RandomizableClusterer

weka.clusterers.FarthestFirst

All Implemented Interfaces:

Serializable, Cloneable, Clusterer, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, Randomizable, RevisionHandler, TechnicalInformationHandler

public class FarthestFirst
extends RandomizableClusterer
implements TechnicalInformationHandler

Cluster data using the FarthestFirst algorithm.

For more information see:

Hochbaum, Shmoys (1985). A best possible heuristic for the k-center problem. Mathematics of Operations Research. 10(2):180-184.

Sanjoy Dasgupta: Performance Guarantees for Hierarchical Clustering. In: 15th Annual Conference on Computational Learning Theory, 351-363, 2002.

Notes:
- works as a fast simple approximate clusterer
- modelled after SimpleKMeans, might be a useful initializer for it

BibTeX:

 @article{Hochbaum1985,
    author = {Hochbaum and Shmoys},
    journal = {Mathematics of Operations Research},
    number = {2},
    pages = {180-184},
    title = {A best possible heuristic for the k-center problem},
    volume = {10},
    year = {1985}
 }
 
 @inproceedings{Dasgupta2002,
    author = {Sanjoy Dasgupta},
    booktitle = {15th Annual Conference on Computational Learning Theory},
    pages = {351-363},
    publisher = {Springer},
    title = {Performance Guarantees for Hierarchical Clustering},
    year = {2002}
 }
 

Valid options are:

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

 -S <num>
  Random number seed.
  (default 1)

Version:

$Revision: 10453 $

Author:

Bernhard Pfahringer ([email protected])

See Also:

RandomizableClusterer, Serialized Form

Constructor Summary

Constructors

Constructor and Description
FarthestFirst()

Method Summary

Modifier and Type	Method and Description
void	buildClusterer(Instances data) Generates a clusterer.
int	clusterInstance(Instance instance) Classifies a given instance.
Capabilities	getCapabilities() Returns default capabilities of the clusterer.
Instances	getClusterCentroids() Get the centroids found by FarthestFirst
int	getNumClusters() gets the number of clusters to generate
String[]	getOptions() Gets the current settings of FarthestFirst
String	getRevision() Returns the revision string.
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.
String	globalInfo() Returns a string describing this clusterer
Enumeration<Option>	listOptions() Returns an enumeration describing the available options.
static void	main(String[] argv) Main method for testing this class.
int	numberOfClusters() Returns the number of clusters.
String	numClustersTipText() Returns the tip text for this property
void	setNumClusters(int n) set the number of clusters to generate
void	setOptions(String[] options) Parses a given list of options.
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, postExecution, preExecution, run, runClusterer, setDebug, setDoNotCheckCapabilities

Methods inherited from class java.lang.Object

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

Methods inherited from interface weka.core.OptionHandler

makeCopy

Constructor Detail

FarthestFirst

public FarthestFirst()

Method Detail

globalInfo

public String globalInfo()

Returns a string describing this clusterer

Returns:

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

getTechnicalInformation

public 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.

Specified by:

getTechnicalInformation in interface TechnicalInformationHandler

Returns:

the technical information about this class

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 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:

Exception - if the clusterer has not been generated successfully

clusterInstance

public int clusterInstance(Instance instance)
                    throws 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 integer if the class is enumerated, otherwise the predicted value

Throws:

Exception - if instance could not be classified successfully

numberOfClusters

public int numberOfClusters()
                     throws 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:

Exception - if number of clusters could not be returned successfully

getClusterCentroids

public Instances getClusterCentroids()

Get the centroids found by FarthestFirst

Returns:

the centroids found by FarthestFirst

listOptions

public 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 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 Exception

set the number of clusters to generate

Parameters:

n - the number of clusters to generate

Throws:

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

setOptions

public void setOptions(String[] options)
                throws Exception

Parses a given list of options.

Valid options are:

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

 -S <num>
  Random number seed.
  (default 1)

Specified by:

setOptions in interface OptionHandler

Overrides:

setOptions in class RandomizableClusterer

Parameters:

options - the list of options as an array of strings

Throws:

Exception - if an option is not supported

getOptions

public String[] getOptions()

Gets the current settings of FarthestFirst

Specified by:

getOptions in interface OptionHandler

Overrides:

getOptions in class RandomizableClusterer

Returns:

an array of strings suitable for passing to setOptions()

toString

public String toString()

return a string describing this clusterer

Overrides:

toString in class Object

Returns:

a description of the clusterer as a string

getRevision

public 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(String[] argv)

Main method for testing this class.

Parameters:

argv - should contain the following arguments:

-t training file [-N number of clusters]