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

Class KMedoidsPAM<V>

java.lang.Object

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

de.lmu.ifi.dbs.elki.algorithm.AbstractDistanceBasedAlgorithm<V,Clustering<MedoidModel>>

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

Type Parameters:

V - vector datatype

All Implemented Interfaces:

Algorithm, ClusteringAlgorithm<Clustering<MedoidModel>>, DistanceBasedAlgorithm<V>

Direct Known Subclasses:

CLARA

@Title(value="Partioning Around Medoids")
 @Reference(title="Clustering by means of Medoids",
           authors="Kaufman, L. and Rousseeuw, P.J.",
           booktitle="Statistical Data Analysis Based on the L1-Norm and Related Methods")
public class KMedoidsPAM<V>
extends AbstractDistanceBasedAlgorithm<V,Clustering<MedoidModel>>
implements ClusteringAlgorithm<Clustering<MedoidModel>>

The original PAM algorithm or k-medoids clustering, as proposed by Kaufman and Rousseeuw in "Partitioning Around Medoids". Reference:

Clustering my means of Medoids
Kaufman, L. and Rousseeuw, P.J.
in: Statistical Data Analysis Based on the L1-Norm and Related Methods

Author:

Erich Schubert

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	KMedoidsPAM.Parameterizer<V> Parameterization class.

Field Summary

Fields

Modifier and Type	Field and Description
protected KMedoidsInitialization<V>	initializer Method to choose initial means.
protected int	k The number of clusters to produce.
private static Logging	LOG The logger for this class.
protected int	maxiter The maximum number of iterations.

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

DISTANCE_FUNCTION_ID

Constructor Summary

Constructors

Constructor and Description
KMedoidsPAM(DistanceFunction<? super V> distanceFunction, int k, int maxiter, KMedoidsInitialization<V> initializer) Constructor.

Method Summary

Modifier and Type	Method and Description
protected boolean	assignToNearestCluster(ArrayDBIDs means, DBIDs ids, WritableDoubleDataStore second, List<? extends ModifiableDBIDs> clusters, DistanceQuery<V> distQ) Returns a list of clusters.
TypeInformation[]	getInputTypeRestriction() Get the input type restriction used for negotiating the data query.
protected Logging	getLogger() Get the (STATIC) logger for this class.
Clustering<MedoidModel>	run(Database database, Relation<V> relation) Run k-medoids
protected void	runPAMOptimization(DistanceQuery<V> distQ, DBIDs ids, ArrayModifiableDBIDs medoids, List<ModifiableDBIDs> clusters) Run the PAM optimization phase.

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

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

Field Detail

LOG

private static final Logging LOG

The logger for this class.

k

protected int k

The number of clusters to produce.

maxiter

protected int maxiter

The maximum number of iterations.

initializer

protected KMedoidsInitialization<V> initializer

Method to choose initial means.

Constructor Detail

KMedoidsPAM

public KMedoidsPAM(DistanceFunction<? super V> distanceFunction,
                   int k,
                   int maxiter,
                   KMedoidsInitialization<V> initializer)

Constructor.

Parameters:

distanceFunction - distance function

k - k parameter

maxiter - Maxiter parameter

initializer - Function to generate the initial means

Method Detail

run

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

Run k-medoids

Parameters:

database - Database

relation - relation to use

Returns:

result

runPAMOptimization

protected void runPAMOptimization(DistanceQuery<V> distQ,
                                  DBIDs ids,
                                  ArrayModifiableDBIDs medoids,
                                  List<ModifiableDBIDs> clusters)

Run the PAM optimization phase.

Parameters:

distQ - Distance query

ids - IDs to process

medoids - Medoids list

clusters - Clusters

assignToNearestCluster

protected boolean assignToNearestCluster(ArrayDBIDs means,
                                         DBIDs ids,
                                         WritableDoubleDataStore second,
                                         List<? extends ModifiableDBIDs> clusters,
                                         DistanceQuery<V> distQ)

Returns a list of clusters. The k^th cluster contains the ids of those FeatureVectors, that are nearest to the k^th mean.

Parameters:

means - Object centroids

ids - Object ids

second - Distance to second nearest medoid

clusters - cluster assignment

distQ - distance query

Returns:

true when any object was reassigned

getInputTypeRestriction

public TypeInformation[] getInputTypeRestriction()

Description copied from class: AbstractAlgorithm

Get the input type restriction used for negotiating the data query.

Specified by:

getInputTypeRestriction in interface Algorithm

Specified by:

getInputTypeRestriction in class AbstractAlgorithm<Clustering<MedoidModel>>

Returns:

Type restriction

getLogger

protected Logging getLogger()

Description copied from class: AbstractAlgorithm

Get the (STATIC) logger for this class.

Specified by:

getLogger in class AbstractAlgorithm<Clustering<MedoidModel>>

Returns:

the static logger