Package elki.clustering.kmeans.initialization

Class KMeansPlusPlus<O>

java.lang.Object

elki.clustering.kmeans.initialization.AbstractKMeansInitialization

elki.clustering.kmeans.initialization.KMeansPlusPlus<O>

Type Parameters:

O - Vector type

All Implemented Interfaces:

KMeansInitialization, KMedoidsInitialization<O>

@Title("K-means++")
@Reference(authors="D. Arthur, S. Vassilvitskii",
           title="k-means++: the advantages of careful seeding",
           booktitle="Proc. 18th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 2007)",
           url="http://dl.acm.org/citation.cfm?id=1283383.1283494",
           bibkey="DBLP:conf/soda/ArthurV07")
public class KMeansPlusPlus<O>
extends AbstractKMeansInitialization
implements KMedoidsInitialization<O>

K-Means++ initialization for k-means.

Reference:

D. Arthur, S. Vassilvitskii
k-means++: the advantages of careful seeding
Proc. 18th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 2007)

Since:

0.5.0

Author:

Erich Schubert

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
protected static class	KMeansPlusPlus.Instance<T>	Abstract instance implementing the weight handling.
protected static class	KMeansPlusPlus.MedoidsInstance	Instance for k-medoids.
protected static class	KMeansPlusPlus.NumberVectorInstance	Instance for k-means, number vector based.
static class	KMeansPlusPlus.Par<V>	Parameterization class.

Field Summary

Fields

Modifier and Type	Field	Description
private static elki.logging.Logging	LOG	Class logger.

Fields inherited from class elki.clustering.kmeans.initialization.AbstractKMeansInitialization

rnd

Constructor Summary

Constructors

Constructor	Description
KMeansPlusPlus(elki.utilities.random.RandomFactory rnd)	Constructor.

Method Summary

Modifier and Type	Method	Description
double[][]	chooseInitialMeans(elki.database.relation.Relation<? extends elki.data.NumberVector> relation, int k, elki.distance.NumberVectorDistance<?> distance)	Choose initial means
elki.database.ids.DBIDs	chooseInitialMedoids(int k, elki.database.ids.DBIDs ids, elki.database.query.distance.DistanceQuery<? super O> distQ)	Choose initial means

Methods inherited from class elki.clustering.kmeans.initialization.AbstractKMeansInitialization

unboxVectors

Methods inherited from class java.lang.Object

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

Field Detail

LOG

private static final elki.logging.Logging LOG

Class logger.

Constructor Detail

KMeansPlusPlus

public KMeansPlusPlus(elki.utilities.random.RandomFactory rnd)

Constructor.

Parameters:

rnd - Random generator.

Method Detail

chooseInitialMeans

public double[][] chooseInitialMeans(elki.database.relation.Relation<? extends elki.data.NumberVector> relation,
                                     int k,
                                     elki.distance.NumberVectorDistance<?> distance)

Description copied from interface: KMeansInitialization

Choose initial means

Specified by:

chooseInitialMeans in interface KMeansInitialization

Parameters:

relation - Relation

k - Parameter k

distance - Distance function

Returns:

List of chosen means for k-means

chooseInitialMedoids

public elki.database.ids.DBIDs chooseInitialMedoids(int k,
                                                    elki.database.ids.DBIDs ids,
                                                    elki.database.query.distance.DistanceQuery<? super O> distQ)

Description copied from interface: KMedoidsInitialization

Choose initial means

Specified by:

chooseInitialMedoids in interface KMedoidsInitialization<O>

Parameters:

k - Parameter k

ids - Candidate IDs.

distQ - Distance function

Returns:

List of chosen means for k-means