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

Class CLARA<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.CLARA<V>

Type Parameters:

V - Vector type

All Implemented Interfaces:

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

@Reference(authors="L. Kaufman, P. J. Rousseeuw",
           title="Clustering Large Data Sets (with discussion)",
           booktitle="Pattern Recognition in Practice II")
public class CLARA<V>
extends KMedoidsPAM<V>

Clustering Large Applications (CLARA) is a clustering method for large data sets based on PAM, partitioning around medoids (KMedoidsPAM) based on sampling. Reference:

L. Kaufman, P. J. Rousseeuw
Clustering Large Data Sets (with discussion)
in: Pattern Recognition in Practice II

Author:

Erich Schubert

Nested Class Summary

Nested Classes

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

Field Summary

Fields

Modifier and Type	Field and Description
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.KMedoidsPAM

initializer, k, maxiter

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

DISTANCE_FUNCTION_ID

Constructor Summary

Constructors

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

Method Summary

Methods

Modifier and Type	Method and Description
protected double	assignRemainingToNearestCluster(ArrayDBIDs means, DBIDs ids, DBIDs rids, WritableIntegerDataStore assignment, DistanceQuery<V> distQ) Returns a list of clusters.
Clustering<MedoidModel>	run(Database database, Relation<V> relation) Run k-medoids

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

assignToNearestCluster, getInputTypeRestriction, getLogger, runPAMOptimization

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

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).

random

RandomFactory random

Random factory for initialization.

Constructor Detail

CLARA

public CLARA(DistanceFunction<? super V> distanceFunction,
     int k,
     int maxiter,
     KMedoidsInitialization<V> initializer,
     int numsamples,
     double sampling,
     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)

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

assignRemainingToNearestCluster

protected double assignRemainingToNearestCluster(ArrayDBIDs means,
                                     DBIDs ids,
                                     DBIDs rids,
                                     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

rids - Sample that was already assigned

assignment - cluster assignment

distQ - distance query

Returns:

Sum of distances.