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

Class FastCLARA<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>

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMedoidsFastPAM1<V>

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

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

Type Parameters:

V - Data type

All Implemented Interfaces:

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

@Reference(authors="Erich Schubert, Peter J. Rousseeuw",
           title="Faster k-Medoids Clustering: Improving the PAM, CLARA, and CLARANS Algorithms",
           booktitle="preprint, to appear",
           url="https://arxiv.org/abs/1810.05691",
           bibkey="DBLP:journals/corr/abs-1810-05691")
public class FastCLARA<V>
extends KMedoidsFastPAM<V>

Clustering Large Applications (CLARA) with the KMedoidsFastPAM 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, Peter J. Rousseeuw
Faster k-Medoids Clustering: Improving the PAM, CLARA, and CLARANS Algorithms
preprint, to appear

Since:

0.7.0

Author:

Erich Schubert

Nested Class Summary

Nested Classes

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

Nested classes/interfaces inherited from class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMedoidsFastPAM

KMedoidsFastPAM.Instance

Field Summary

Fields

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

Fields inherited from class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMedoidsFastPAM

fasttol

Fields inherited from class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMedoidsPAM

initializer, k, maxiter

Fields inherited from class de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm

ALGORITHM_ID

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

DISTANCE_FUNCTION_ID

Constructor Summary

Constructors

Constructor and Description
FastCLARA(DistanceFunction<? super V> distanceFunction, int k, int maxiter, KMedoidsInitialization<V> initializer, double fasttol, int numsamples, double sampling, boolean keepmed, RandomFactory random) Constructor.

Method Summary

Modifier and Type	Method and Description
Clustering<MedoidModel>	run(Database database, Relation<V> relation) Run k-medoids

Methods inherited from class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMedoidsFastPAM

getLogger, run

Methods inherited from class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMedoidsPAM

getInputTypeRestriction, initialMedoids

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

getDistanceFunction

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

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

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

RandomFactory random

Random factory for initialization.

Constructor Detail

FastCLARA

public FastCLARA(DistanceFunction<? super V> distanceFunction,
                 int k,
                 int maxiter,
                 KMedoidsInitialization<V> initializer,
                 double fasttol,
                 int numsamples,
                 double sampling,
                 boolean keepmed,
                 RandomFactory random)

Constructor.

Parameters:

distanceFunction - 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(Database database,
                                   Relation<V> relation)

Description copied from class: KMedoidsPAM

Run k-medoids

Overrides:

run in class KMedoidsPAM<V>

Parameters:

database - Database

relation - relation to use

Returns:

result