Package elki.clustering.kmedoids

Class FasterCLARA<O>

java.lang.Object

elki.clustering.kmedoids.PAM<O>

elki.clustering.kmedoids.FastPAM1<O>

elki.clustering.kmedoids.FastPAM<O>

elki.clustering.kmedoids.FasterPAM<O>

elki.clustering.kmedoids.FasterCLARA<O>

Type Parameters:

O - Data type

All Implemented Interfaces:

elki.Algorithm, ClusteringAlgorithm<Clustering<MedoidModel>>, KMedoidsClustering<O>

@Reference(authors="Erich Schubert and Peter J. Rousseeuw",
           title="Fast and Eager k-Medoids Clustering: O(k) Runtime Improvement of the PAM, CLARA, and CLARANS Algorithms",
           booktitle="arXiv preprint",
           url="https://arxiv.org/abs/2008.05171",
           bibkey="DBLP:journals/corr/abs-2008-05171")
public class FasterCLARA<O>
extends FasterPAM<O>

Clustering Large Applications (CLARA) with the FastPAM improvements, to increase scalability in the number of clusters. This variant will also default to twice the sample size, to improve quality.

TODO: use a triangular distance matrix, rather than a hash-map based cache, for a bit better performance and less memory.

Reference:

Erich Schubert and Peter J. Rousseeuw
Fast and Eager k-Medoids Clustering: O(k) Runtime Improvement of the PAM, CLARA, and CLARANS Algorithms
Preprint

Since:

0.7.0

Author:

Erich Schubert

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static class	FasterCLARA.Par<V>	Parameterization class.

Nested classes/interfaces inherited from class elki.clustering.kmedoids.FasterPAM

FasterPAM.Instance

Nested classes/interfaces inherited from interface elki.Algorithm

elki.Algorithm.Utils

Field Summary

Fields

Modifier and Type	Field	Description
(package private) boolean	keepmed	Keep the previous medoids in the sample (see page 145).
private static elki.logging.Logging	LOG	Class logger.
(package private) int	numsamples	Number of samples to draw (i.e. iterations).
(package private) elki.utilities.random.RandomFactory	random	Random factory for initialization.
(package private) double	sampling	Sampling rate.

Fields inherited from class elki.clustering.kmedoids.FastPAM

fasttol

Fields inherited from class elki.clustering.kmedoids.PAM

distance, initializer, k, maxiter

Constructor Summary

Constructors

Constructor	Description
FasterCLARA(elki.distance.Distance<? super O> distance, int k, int maxiter, KMedoidsInitialization<O> initializer, int numsamples, double sampling, boolean keepmed, elki.utilities.random.RandomFactory random)	Constructor.

Method Summary

Modifier and Type	Method	Description
Clustering<MedoidModel>	run(elki.database.relation.Relation<O> relation)	Run k-medoids clustering.
Clustering<MedoidModel>	run(elki.database.relation.Relation<O> relation, int k, elki.database.query.distance.DistanceQuery<? super O> distQ)	Run k-medoids clustering with a given distance query. Not a very elegant API, but needed for some types of nested k-medoids.

Methods inherited from class elki.clustering.kmedoids.FasterPAM

getLogger

Methods inherited from class elki.clustering.kmedoids.PAM

getInputTypeRestriction, initialMedoids, wrapResult

Methods inherited from class java.lang.Object

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

Methods inherited from interface elki.clustering.ClusteringAlgorithm

autorun

Field Detail

LOG

private static final elki.logging.Logging LOG

Class logger.

sampling

double sampling

Sampling rate. If less than 1, it is considered to be a relative value.

numsamples

int numsamples

Number of samples to draw (i.e. iterations).

keepmed

boolean keepmed

Keep the previous medoids in the sample (see page 145).

random

elki.utilities.random.RandomFactory random

Random factory for initialization.

Constructor Detail

FasterCLARA

public FasterCLARA(elki.distance.Distance<? super O> distance,
                   int k,
                   int maxiter,
                   KMedoidsInitialization<O> initializer,
                   int numsamples,
                   double sampling,
                   boolean keepmed,
                   elki.utilities.random.RandomFactory random)

Constructor.

Parameters:

distance - Distance function to use

k - Number of clusters to produce

maxiter - Maximum number of iterations

initializer - Initialization function

numsamples - Number of samples (sampling iterations)

sampling - Sampling rate (absolute or relative)

keepmed - Keep the previous medoids in the next sample

random - Random generator

Method Detail

run

public Clustering<MedoidModel> run(elki.database.relation.Relation<O> relation)

Description copied from interface: KMedoidsClustering

Run k-medoids clustering.

Specified by:

run in interface KMedoidsClustering<O>

Overrides:

run in class PAM<O>

Parameters:

relation - relation to use

Returns:

result

run

public Clustering<MedoidModel> run(elki.database.relation.Relation<O> relation,
                                   int k,
                                   elki.database.query.distance.DistanceQuery<? super O> distQ)

Description copied from interface: KMedoidsClustering

Run k-medoids clustering with a given distance query.
Not a very elegant API, but needed for some types of nested k-medoids.

Specified by:

run in interface KMedoidsClustering<O>

Overrides:

run in class FasterPAM<O>

Parameters:

relation - relation to use

k - Number of clusters

distQ - Distance query to use

Returns:

result