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

Class KMeansLloyd<V extends NumberVector<V,?>,D extends Distance<D>>

java.lang.Object

de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm<R>

de.lmu.ifi.dbs.elki.algorithm.AbstractPrimitiveDistanceBasedAlgorithm<NumberVector<?,?>,D,Clustering<MeanModel<V>>>

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.AbstractKMeans<V,D>

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansLloyd<V,D>

Type Parameters:

V - vector datatype

D - distance value type

All Implemented Interfaces:

Algorithm, ClusteringAlgorithm<Clustering<MeanModel<V>>>, KMeans, InspectionUtilFrequentlyScanned, Parameterizable

@Title(value="K-Means")
@Description(value="Finds a partitioning into k clusters.")
@Reference(authors="S. Lloyd",
           title="Least squares quantization in PCM",
           booktitle="IEEE Transactions on Information Theory 28 (2): 129\u2013137.",
           url="http://dx.doi.org/10.1109/TIT.1982.1056489")
public class KMeansLloyd<V extends NumberVector<V,?>,D extends Distance<D>>
extends AbstractKMeans<V,D>
implements ClusteringAlgorithm<Clustering<MeanModel<V>>>

Provides the k-means algorithm, using Lloyd-style bulk iterations.

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

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	KMeansLloyd.Parameterizer<V extends NumberVector<V,?>,D extends Distance<D>> Parameterization class.

Field Summary

Fields

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

Fields inherited from class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.AbstractKMeans

initializer, k, maxiter

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

INIT_ID, K_ID, MAXITER_ID, SEED_ID

Constructor Summary

Constructors

Constructor and Description
KMeansLloyd(PrimitiveDistanceFunction<NumberVector<?,?>,D> distanceFunction, int k, int maxiter, KMeansInitialization<V> initializer) Constructor.

Method Summary

Methods

Modifier and Type	Method and Description
protected Logging	getLogger() Get the (STATIC) logger for this class.
Clustering<MeanModel<V>>	run(Database database, Relation<V> relation) Run k-means

Methods inherited from class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.AbstractKMeans

assignToNearestCluster, getInputTypeRestriction, incrementalUpdateMean, macQueenIterate, means, medians

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

getDistanceFunction

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

makeParameterDistanceFunction, 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

Methods inherited from interface de.lmu.ifi.dbs.elki.algorithm.Algorithm

getInputTypeRestriction

Field Detail

logger

private static final Logging logger

The logger for this class.

Constructor Detail

KMeansLloyd

public KMeansLloyd(PrimitiveDistanceFunction<NumberVector<?,?>,D> distanceFunction,
           int k,
           int maxiter,
           KMeansInitialization<V> initializer)

Constructor.

Parameters:

distanceFunction - distance function

k - k parameter

maxiter - Maxiter parameter

Method Detail

run

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

Run k-means

Parameters:

database - Database

relation - relation to use

Returns:

result

getLogger

protected Logging getLogger()

Description copied from class: AbstractAlgorithm

Get the (STATIC) logger for this class.

Specified by:

getLogger in class AbstractAlgorithm<Clustering<MeanModel<V extends NumberVector<V,?>>>>

Returns:

the static logger