Package elki.clustering.kmeans

Class LloydKMeans<V extends elki.data.NumberVector>

java.lang.Object

elki.clustering.kmeans.AbstractKMeans<V,KMeansModel>

elki.clustering.kmeans.LloydKMeans<V>

Type Parameters:

V - vector datatype

All Implemented Interfaces:

elki.Algorithm, ClusteringAlgorithm<Clustering<KMeansModel>>, KMeans<V,KMeansModel>

@Title("k-Means (Lloyd/Forgy Algorithm)")
@Reference(authors="S. Lloyd",title="Least squares quantization in PCM",booktitle="IEEE Transactions on Information Theory 28 (2): 129\u2013137.",url="https://doi.org/10.1109/TIT.1982.1056489",bibkey="DBLP:journals/tit/Lloyd82") @Reference(authors="E. W. Forgy",title="Cluster analysis of multivariate data: efficiency versus interpretability of classifications",booktitle="Biometrics 21(3)",bibkey="journals/biometrics/Forgy65")
@Alias({"lloyd","forgy"})
public class LloydKMeans<V extends elki.data.NumberVector>
extends AbstractKMeans<V,KMeansModel>

The standard k-means algorithm, using bulk iterations and commonly attributed to Lloyd and Forgy (independently).

Reference:

S. Lloyd
Least squares quantization in PCM
IEEE Transactions on Information Theory 28 (2)
previously published as Bell Telephone Laboratories Paper

E. W. Forgy
Cluster analysis of multivariate data: efficiency versus interpretability of classifications
Abstract published in Biometrics 21(3)

Since:

0.5.0

Author:

Arthur Zimek

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
protected static class	LloydKMeans.Instance	Inner instance, storing state for a single data set.
static class	LloydKMeans.Par<V extends elki.data.NumberVector>	Parameterization class.

Nested classes/interfaces inherited from interface elki.Algorithm

elki.Algorithm.Utils

Field Summary

Fields

Modifier and Type	Field	Description
private static elki.logging.Logging	LOG	The logger for this class.

Fields inherited from class elki.clustering.kmeans.AbstractKMeans

distance, initializer, k, maxiter

Fields inherited from interface elki.clustering.kmeans.KMeans

DISTANCE_FUNCTION_ID, INIT_ID, K_ID, MAXITER_ID, SEED_ID, VARSTAT_ID

Constructor Summary

Constructors

Constructor	Description
LloydKMeans(elki.distance.NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer)	Constructor.

Method Summary

Modifier and Type	Method	Description
protected elki.logging.Logging	getLogger()	Get the (STATIC) logger for this class.
Clustering<KMeansModel>	run(elki.database.relation.Relation<V> relation)	Run the clustering algorithm.

Methods inherited from class elki.clustering.kmeans.AbstractKMeans

getDistance, getInputTypeRestriction, incrementalUpdateMean, initialMeans, means, minusEquals, nearestMeans, plusEquals, plusMinusEquals, setDistance, setInitializer, setK

Methods inherited from class java.lang.Object

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

Methods inherited from interface elki.clustering.ClusteringAlgorithm

autorun

Field Detail

LOG

private static final elki.logging.Logging LOG

The logger for this class.

Constructor Detail

LloydKMeans

public LloydKMeans(elki.distance.NumberVectorDistance<? super V> distance,
                   int k,
                   int maxiter,
                   KMeansInitialization initializer)

Constructor.

Parameters:

distance - distance function

k - k parameter

maxiter - Maxiter parameter

initializer - Initialization method

Method Detail

run

public Clustering<KMeansModel> run(elki.database.relation.Relation<V> relation)

Description copied from interface: KMeans

Run the clustering algorithm.

Parameters:

relation - Relation to process.

Returns:

Clustering result

getLogger

protected elki.logging.Logging getLogger()

Description copied from class: AbstractKMeans

Get the (STATIC) logger for this class.

Specified by:

getLogger in class AbstractKMeans<V extends elki.data.NumberVector,KMeansModel>

Returns:

the static logger