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

Class KMediansLloyd<V extends NumberVector>

java.lang.Object

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

de.lmu.ifi.dbs.elki.algorithm.AbstractNumberVectorDistanceBasedAlgorithm<V,Clustering<M>>

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

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMediansLloyd<V>

Type Parameters:

V - vector datatype

All Implemented Interfaces:

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

@Title(value="K-Medians")
 @Reference(title="Clustering via Concave Minimization",
           authors="P. S. Bradley, O. L. Mangasarian, W. N. Street",
           booktitle="Advances in Neural Information Processing Systems",
           url="https://papers.nips.cc/paper/1260-clustering-via-concave-minimization.pdf")
public class KMediansLloyd<V extends NumberVector>
extends AbstractKMeans<V,MeanModel>

k-medians clustering algorithm, but using Lloyd-style bulk iterations instead of the more complicated approach suggested by Kaufman and Rousseeuw (see KMedoidsPAM instead). Reference:

Clustering via Concave Minimization
P. S. Bradley, O. L. Mangasarian, W. N. Street
in: Advances in Neural Information Processing Systems (NIPS)

Author:

Erich Schubert

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	KMediansLloyd.Parameterizer<V extends NumberVector> Parameterization class.

Field Summary

Fields

Modifier and Type	Field and Description
private static String	KEY Key for statistics logging.
private static Logging	LOG The logger for this class.

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

initializer, k, maxiter

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

distanceFunction

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

INIT_ID, K_ID, MAXITER_ID, SEED_ID

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

DISTANCE_FUNCTION_ID

Constructor Summary

Constructors

Constructor and Description
KMediansLloyd(NumberVectorDistanceFunction<? super V> distanceFunction, int k, int maxiter, KMeansInitialization<? super V> initializer) Constructor.

Method Summary

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

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

assignToNearestCluster, getInputTypeRestriction, incrementalUpdateMean, logVarstat, macQueenIterate, means, medians, setDistanceFunction, setK, updateAssignment

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

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.DistanceBasedAlgorithm

getDistanceFunction

Field Detail

LOG

private static final Logging LOG

The logger for this class.

KEY

private static final String KEY

Key for statistics logging.

Constructor Detail

KMediansLloyd

public KMediansLloyd(NumberVectorDistanceFunction<? super V> distanceFunction,
                     int k,
                     int maxiter,
                     KMeansInitialization<? super V> initializer)

Constructor.

Parameters:

distanceFunction - distance function

k - k parameter

maxiter - Maxiter parameter

initializer - Initialization method

Method Detail

run

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

Description copied from interface: KMeans

Run the clustering algorithm.

Parameters:

database - Database to run on.

relation - Relation to process.

Returns:

Clustering 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>>

Returns:

the static logger