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

Class KMedoidsPAM<V,D extends NumberDistance<D,?>>

java.lang.Object

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

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

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

Type Parameters:

V - vector datatype

D - distance value type

All Implemented Interfaces:

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

@Title(value="Partioning Around Medoids")
@Reference(title="Clustering my means of Medoids",
           authors="Kaufman, L. and Rousseeuw, P.J.",
           booktitle="Statistical Data Analysis Based on the L_1\u2013Norm and Related Methods")
public class KMedoidsPAM<V,D extends NumberDistance<D,?>>
extends AbstractDistanceBasedAlgorithm<V,D,Clustering<MedoidModel>>
implements ClusteringAlgorithm<Clustering<MedoidModel>>

Provides the k-medoids clustering algorithm, using the "Partitioning Around Medoids" approach. Reference:

Clustering my means of Medoids
Kaufman, L. and Rousseeuw, P.J.
in: Statistical Data Analysis Based on the L_1–Norm and Related Methods

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	KMedoidsPAM.Parameterizer<V,D extends NumberDistance<D,?>> Parameterization class.

Field Summary

Fields

Modifier and Type	Field and Description
protected KMedoidsInitialization<V>	initializer Method to choose initial means.
protected int	k Holds the value of KMeans.K_ID.
private static Logging	LOG The logger for this class.
protected int	maxiter Holds the value of KMeans.MAXITER_ID.

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

DISTANCE_FUNCTION_ID

Constructor Summary

Constructors

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

Method Summary

Methods

Modifier and Type	Method and Description
protected boolean	assignToNearestCluster(ArrayDBIDs means, DBIDs ids, WritableDoubleDataStore second, List<? extends ModifiableDBIDs> clusters, DistanceQuery<V,D> 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

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

Holds the value of KMeans.K_ID.

maxiter

protected int maxiter

Holds the value of KMeans.MAXITER_ID.

initializer

protected KMedoidsInitialization<V> initializer

Method to choose initial means.

Constructor Detail

KMedoidsPAM

public KMedoidsPAM(PrimitiveDistanceFunction<? super V,D> 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

assignToNearestCluster

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