de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans

Class KMeansBisecting<V extends NumberVector,M extends MeanModel>

java.lang.Object

de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm<Clustering<M>>

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansBisecting<V,M>

Type Parameters:

V - Vector type

M - Model type

All Implemented Interfaces:

Algorithm, ClusteringAlgorithm<Clustering<M>>, KMeans<V,M>, DistanceBasedAlgorithm<V>

@Reference(authors="M. Steinbach, G. Karypis, V. Kumar",
           title="A Comparison of Document Clustering Techniques",
           booktitle="KDD workshop on text mining. Vol. 400. No. 1",
           url="http://glaros.dtc.umn.edu/gkhome/fetch/papers/docclusterKDDTMW00.pdf",
           bibkey="conf/kdd/SteinbachKK00")
public class KMeansBisecting<V extends NumberVector,M extends MeanModel>
extends AbstractAlgorithm<Clustering<M>>
implements KMeans<V,M>

The bisecting k-means algorithm works by starting with an initial partitioning into two clusters, then repeated splitting of the largest cluster to get additional clusters.

Reference:

M. Steinbach, G. Karypis, V. Kumar
A Comparison of Document Clustering Techniques
KDD workshop on text mining. Vol. 400. No. 1

Since:

0.6.0

Author:

Stephan Baier

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	KMeansBisecting.Parameterizer<V extends NumberVector,M extends MeanModel> Parameterization class.

Field Summary

Fields

Modifier and Type	Field and Description
private KMeans<V,M>	innerkMeans Variant of kMeans for the bisecting step.
private int	k Desired value of k.
private static Logging	LOG The logger for this class.

Fields inherited from class de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm

ALGORITHM_ID

Fields inherited from interface de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeans

INIT_ID, K_ID, MAXITER_ID, SEED_ID, VARSTAT_ID

Fields inherited from interface de.lmu.ifi.dbs.elki.algorithm.DistanceBasedAlgorithm

DISTANCE_FUNCTION_ID

Constructor Summary

Constructors

Constructor and Description
KMeansBisecting(int k, KMeans<V,M> innerkMeans) Constructor.

Method Summary

Modifier and Type	Method and Description
DistanceFunction<? super V>	getDistanceFunction() Returns the distanceFunction.
TypeInformation[]	getInputTypeRestriction() Get the input type restriction used for negotiating the data query.
protected Logging	getLogger() Get the (STATIC) logger for this class.
Clustering<M>	run(Database database, Relation<V> relation) Run the clustering algorithm.
void	setDistanceFunction(NumberVectorDistanceFunction<? super V> distanceFunction) Set the distance function to use.
void	setInitializer(KMeansInitialization init) Set the initialization method.
void	setK(int k) Set the value of k.

Methods inherited from class de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm

run

Methods inherited from class java.lang.Object

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

Methods inherited from interface de.lmu.ifi.dbs.elki.algorithm.clustering.ClusteringAlgorithm

run

Field Detail

LOG

private static final Logging LOG

The logger for this class.

innerkMeans

private KMeans<V extends NumberVector,M extends MeanModel> innerkMeans

Variant of kMeans for the bisecting step.

k

private int k

Desired value of k.

Constructor Detail

KMeansBisecting

public KMeansBisecting(int k,
                       KMeans<V,M> innerkMeans)

Constructor.

Parameters:

k - k parameter - number of result clusters

innerkMeans - KMeans variant parameter - for bisecting step

Method Detail

run

public Clustering<M> run(Database database,
                         Relation<V> relation)

Description copied from interface: KMeans

Run the clustering algorithm.

Specified by:

run in interface KMeans<V extends NumberVector,M extends MeanModel>

Parameters:

database - Database to run on.

relation - Relation to process.

Returns:

Clustering result

getInputTypeRestriction

public TypeInformation[] getInputTypeRestriction()

Description copied from class: AbstractAlgorithm

Get the input type restriction used for negotiating the data query.

Specified by:

getInputTypeRestriction in interface Algorithm

Specified by:

getInputTypeRestriction in class AbstractAlgorithm<Clustering<M extends MeanModel>>

Returns:

Type restriction

getDistanceFunction

public DistanceFunction<? super V> getDistanceFunction()

Description copied from interface: DistanceBasedAlgorithm

Returns the distanceFunction.

Specified by:

getDistanceFunction in interface DistanceBasedAlgorithm<V extends NumberVector>

Returns:

the distanceFunction

setK

public void setK(int k)

Description copied from interface: KMeans

Set the value of k. Needed for some types of nested k-means.

Specified by:

setK in interface KMeans<V extends NumberVector,M extends MeanModel>

Parameters:

k - K parameter

setDistanceFunction

public void setDistanceFunction(NumberVectorDistanceFunction<? super V> distanceFunction)

Description copied from interface: KMeans

Set the distance function to use.

Specified by:

setDistanceFunction in interface KMeans<V extends NumberVector,M extends MeanModel>

Parameters:

distanceFunction - Distance function.

setInitializer

public void setInitializer(KMeansInitialization init)

Description copied from interface: KMeans

Set the initialization method.

Specified by:

setInitializer in interface KMeans<V extends NumberVector,M extends MeanModel>

Parameters:

init - Initialization method

getLogger

protected Logging getLogger()

Description copied from class: AbstractAlgorithm

Get the (STATIC) logger for this class.

Specified by:

getLogger in class AbstractAlgorithm<Clustering<M extends MeanModel>>

Returns:

the static logger