Package elki.clustering.kmeans

Class ElkanKMeans<V extends NumberVector>

java.lang.Object

elki.clustering.kmeans.AbstractKMeans<V,KMeansModel>

elki.clustering.kmeans.SimplifiedElkanKMeans<V>

elki.clustering.kmeans.ElkanKMeans<V>

Type Parameters:

V - vector datatype

All Implemented Interfaces:

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

@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 ElkanKMeans<V extends NumberVector>
extends SimplifiedElkanKMeans<V>

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

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

See HamerlyKMeans 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	Description
protected static class	ElkanKMeans.Instance	Inner instance, storing state for a single data set.
static class	ElkanKMeans.Par<V extends NumberVector>	Parameterization class.

Nested classes/interfaces inherited from interface elki.Algorithm

Algorithm.Utils

Field Summary

Fields

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

Fields inherited from class elki.clustering.kmeans.SimplifiedElkanKMeans

varstat

Fields inherited from class elki.clustering.kmeans.AbstractKMeans

distance, initializer, k, maxiter

Fields inherited from interface elki.clustering.kmeans.KMeans

DISTANCE_FUNCTION_ID, INIT_ID, K_ID, MAXITER_ID, SEED_ID, VARSTAT_ID

Constructor Summary

Constructors

Constructor	Description
ElkanKMeans(NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer, boolean varstat)	Constructor.

Method Summary

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

Methods inherited from class elki.clustering.kmeans.AbstractKMeans

getDistance, getInputTypeRestriction, incrementalUpdateMean, initialMeans, means, minusEquals, nearestMeans, plusEquals, plusMinusEquals, setDistance, setInitializer, setK

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 Logging LOG

The logger for this class.

Constructor Detail

ElkanKMeans

public ElkanKMeans(NumberVectorDistance<? super V> distance,
                   int k,
                   int maxiter,
                   KMeansInitialization initializer,
                   boolean varstat)

Constructor.

Parameters:

distance - distance function

k - k parameter

maxiter - Maxiter parameter

initializer - Initialization method

varstat - Compute the variance statistic

Method Detail

run

public Clustering<KMeansModel> run(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 SimplifiedElkanKMeans<V extends NumberVector>

Parameters:

relation - Relation to process.

Returns:

Clustering result

getLogger

protected Logging getLogger()

Description copied from class: AbstractKMeans

Get the (STATIC) logger for this class.

Overrides:

getLogger in class SimplifiedElkanKMeans<V extends NumberVector>

Returns:

the static logger