Package elki.clustering.kmeans.initialization

Class KMeansPlusPlus.MedoidsInstance

java.lang.Object

elki.clustering.kmeans.initialization.KMeansPlusPlus.Instance<DBIDRef>

elki.clustering.kmeans.initialization.KMeansPlusPlus.MedoidsInstance

Enclosing class:

KMeansPlusPlus<O>

protected static class KMeansPlusPlus.MedoidsInstance
extends KMeansPlusPlus.Instance<DBIDRef>

Instance for k-medoids.

Author:

Erich Schubert

Field Summary

Fields

Modifier and Type	Field	Description
(package private) DistanceQuery<?>	distQ	Distance query

Fields inherited from class elki.clustering.kmeans.initialization.KMeansPlusPlus.Instance

diststat, ids, random, weights

Constructor Summary

Constructors

Constructor	Description
MedoidsInstance(DBIDs ids, DistanceQuery<?> distQ, RandomFactory rnd)

Method Summary

Modifier and Type	Method	Description
protected void	chooseRemaining(int k, ArrayModifiableDBIDs means, double weightsum)	Choose remaining means, weighted by distance.
protected double	distance(DBIDRef a, DBIDRef b)	Compute the distance of two objects.
DBIDs	run(int k)

Methods inherited from class elki.clustering.kmeans.initialization.KMeansPlusPlus.Instance

initialWeights, nextDouble, updateWeights

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

distQ

DistanceQuery<?> distQ

Distance query

Constructor Detail

MedoidsInstance

public MedoidsInstance(DBIDs ids,
                       DistanceQuery<?> distQ,
                       RandomFactory rnd)

Method Detail

run

public DBIDs run(int k)

distance

protected double distance(DBIDRef a,
                          DBIDRef b)

Description copied from class: KMeansPlusPlus.Instance

Compute the distance of two objects.

Specified by:

distance in class KMeansPlusPlus.Instance<DBIDRef>

Parameters:

a - First object

b - Second object

Returns:

Distance

chooseRemaining

protected void chooseRemaining(int k,
                               ArrayModifiableDBIDs means,
                               double weightsum)

Choose remaining means, weighted by distance.

Parameters:

k - Number of means to choose

means - Means storage

weightsum - Sum of weights