Package elki.clustering.kmeans

Class HamerlyKMeans<V extends NumberVector>

java.lang.Object

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

elki.clustering.kmeans.HamerlyKMeans<V>

Type Parameters:

V - vector datatype

All Implemented Interfaces:

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

Direct Known Subclasses:

AnnulusKMeans, ExponionKMeans

@Reference(authors="G. Hamerly",
           title="Making k-means even faster",
           booktitle="Proc. 2010 SIAM International Conference on Data Mining",
           url="https://doi.org/10.1137/1.9781611972801.12",
           bibkey="DBLP:conf/sdm/Hamerly10")
public class HamerlyKMeans<V extends NumberVector>
extends AbstractKMeans<V,KMeansModel>

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

Reference:

G. Hamerly
Making k-means even faster
Proc. 2010 SIAM International Conference on Data Mining

Since:

0.7.0

Author:

Erich Schubert

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
protected static class	HamerlyKMeans.Instance	Inner instance, storing state for a single data set.
static class	HamerlyKMeans.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.
protected boolean	varstat	Flag whether to compute the final variance statistic.

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
HamerlyKMeans(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.

varstat

protected boolean varstat

Flag whether to compute the final variance statistic.

Constructor Detail

HamerlyKMeans

public HamerlyKMeans(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.

Parameters:

relation - Relation to process.

Returns:

Clustering result

getLogger

protected Logging getLogger()

Description copied from class: AbstractKMeans

Get the (STATIC) logger for this class.

Specified by:

getLogger in class AbstractKMeans<V extends NumberVector,KMeansModel>

Returns:

the static logger