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

Class KMeansElkan<V extends NumberVector>

java.lang.Object

de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm<R>

de.lmu.ifi.dbs.elki.algorithm.AbstractNumberVectorDistanceBasedAlgorithm<V,Clustering<M>>

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.AbstractKMeans<V,KMeansModel>

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

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

Type Parameters:

V - vector datatype

All Implemented Interfaces:

Algorithm, ClusteringAlgorithm<Clustering<KMeansModel>>, KMeans<V,KMeansModel>, DistanceBasedAlgorithm<V>

@Reference(authors="C. Elkan",
           title="Using the triangle inequality to accelerate k-means",
           booktitle="Proc. 20th International Conference on Machine Learning, ICML 2003",
           url="http://www.aaai.org/Library/ICML/2003/icml03-022.php",
           bibkey="DBLP:conf/icml/Elkan03")
public class KMeansElkan<V extends NumberVector>
extends KMeansSimplifiedElkan<V>

Elkan's fast k-means by exploiting the triangle inequality.

This variant needs O(n*k) additional memory to store bounds.

See KMeansHamerly for a close variant that only uses O(n*2) additional memory for bounds.

Reference:

C. Elkan
Using the triangle inequality to accelerate k-means
Proc. 20th International Conference on Machine Learning, ICML 2003

Since:

0.7.0

Author:

Erich Schubert

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
protected static class	KMeansElkan.Instance Inner instance, storing state for a single data set.
static class	KMeansElkan.Parameterizer<V extends NumberVector> Parameterization class.

Field Summary

Fields

Modifier and Type	Field and Description
private static Logging	LOG The logger for this class.

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

varstat

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

initializer, k, maxiter

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

distanceFunction

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

ALGORITHM_ID

Fields inherited from interface de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeans

INIT_ID, K_ID, MAXITER_ID, SEED_ID, VARSTAT_ID

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

DISTANCE_FUNCTION_ID

Constructor Summary

Constructors

Constructor and Description
KMeansElkan(NumberVectorDistanceFunction<? super V> distanceFunction, int k, int maxiter, KMeansInitialization initializer, boolean varstat) Constructor.

Method Summary

Modifier and Type	Method and Description
protected Logging	getLogger() Get the (STATIC) logger for this class.
Clustering<KMeansModel>	run(Database database, Relation<V> relation) Run the clustering algorithm.

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

getInputTypeRestriction, incrementalUpdateMean, initialMeans, means, minusEquals, nearestMeans, plusEquals, plusMinusEquals, setDistanceFunction, setInitializer, setK

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

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

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

getDistanceFunction

Field Detail

LOG

private static final Logging LOG

The logger for this class.

Constructor Detail

KMeansElkan

public KMeansElkan(NumberVectorDistanceFunction<? super V> distanceFunction,
                   int k,
                   int maxiter,
                   KMeansInitialization initializer,
                   boolean varstat)

Constructor.

Parameters:

distanceFunction - distance function

k - k parameter

maxiter - Maxiter parameter

initializer - Initialization method

varstat - Compute the variance statistic

Method Detail

run

public Clustering<KMeansModel> run(Database database,
                                   Relation<V> relation)

Description copied from interface: KMeans

Run the clustering algorithm.

Specified by:

run in interface KMeans<V extends NumberVector,KMeansModel>

Overrides:

run in class KMeansSimplifiedElkan<V extends NumberVector>

Parameters:

database - Database to run on.

relation - Relation to process.

Returns:

Clustering result

getLogger

protected Logging getLogger()

Description copied from class: AbstractAlgorithm

Get the (STATIC) logger for this class.

Overrides:

getLogger in class KMeansSimplifiedElkan<V extends NumberVector>

Returns:

the static logger