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 String	KEY Key for statistics logging.
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 double	assignToNearestCluster(ArrayDBIDs means, DBIDs ids, WritableDoubleDataStore nearest, WritableDoubleDataStore second, WritableIntegerDataStore assignment, 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, WritableIntegerDataStore assignment) 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.

KEY

private static final String KEY

Key for statistics logging.

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,
                                  WritableIntegerDataStore assignment)

Run the PAM optimization phase.

Parameters:

distQ - Distance query

ids - IDs to process

medoids - Medoids list

assignment - Cluster assignment

assignToNearestCluster

protected double assignToNearestCluster(ArrayDBIDs means,
                                        DBIDs ids,
                                        WritableDoubleDataStore nearest,
                                        WritableDoubleDataStore second,
                                        WritableIntegerDataStore assignment,
                                        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

nearest - Distance to nearest medoid

second - Distance to second nearest medoid

assignment - Cluster assignment

distQ - distance query

Returns:

Assignment cost

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