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

Class KMediansLloyd<V extends NumberVector<?>,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<M>>

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

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

Type Parameters:

V - vector datatype

D - distance value type

All Implemented Interfaces:

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

@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="http://nips.djvuzone.org/djvu/nips09/0368.djvu")
public class KMediansLloyd<V extends NumberVector<?>,D extends Distance<D>>
extends AbstractKMeans<V,D,MeanModel<V>>

Provides the k-medians clustering algorithm, using Lloyd-style bulk iterations. Reference:

Clustering via Concave Minimization
P. S. Bradley, O. L. Mangasarian, W. N. Street
in: Advances in neural information processing systems

Nested Class Summary

Nested Classes

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

Field Summary

Fields

Modifier and Type	Field and Description
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.AbstractPrimitiveDistanceBasedAlgorithm

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(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 the clustering algorithm.

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

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

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.DistanceBasedAlgorithm

getDistanceFunction

Field Detail

LOG

private static final Logging LOG

The logger for this class.

Constructor Detail

KMediansLloyd

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

Constructor.

Parameters:

distanceFunction - distance function

k - k parameter

maxiter - Maxiter parameter

initializer - Initialization method

Method Detail

run

public Clustering<MeanModel<V>> 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<V extends NumberVector<?>>>>

Returns:

the static logger