Package elki.clustering.kmeans.spherical

Class EuclideanSphericalSimplifiedElkanKMeans<V extends elki.data.NumberVector>

java.lang.Object

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

elki.clustering.kmeans.spherical.SphericalKMeans<V>

elki.clustering.kmeans.spherical.EuclideanSphericalSimplifiedElkanKMeans<V>

Type Parameters:

V - vector datatype

All Implemented Interfaces:

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

Direct Known Subclasses:

EuclideanSphericalElkanKMeans

@Priority(-100)
@Reference(authors="Erich Schubert, Andreas Lang, Gloria Feher",
           title="Accelerating Spherical k-Means",
           booktitle="Int. Conf. on Similarity Search and Applications, SISAP 2021",
           url="https://doi.org/10.1007/978-3-030-89657-7_17",
           bibkey="DBLP:conf/sisap/SchubertLF21")
public class EuclideanSphericalSimplifiedElkanKMeans<V extends elki.data.NumberVector>
extends SphericalKMeans<V>

A spherical k-Means algorithm based on Hamerly's fast k-means by exploiting the triangle inequality in the corresponding Euclidean space.

Please prefer SphericalSimplifiedElkanKMeans, which uses a tighter bound based on Cosines instead.

FIXME: currently requires the vectors to be L2 normalized beforehand

Reference:

Erich Schubert, Andreas Lang, Gloria Feher
Accelerating Spherical k-Means
Int. Conf. on Similarity Search and Applications, SISAP 2021

Since:

0.8.0

Author:

Alexander Voß, Erich Schubert

Nested Class Summary

Nested Classes

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

Nested classes/interfaces inherited from interface elki.Algorithm

elki.Algorithm.Utils

Field Summary

Fields

Modifier and Type	Field	Description
private static elki.logging.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
EuclideanSphericalSimplifiedElkanKMeans(int k, int maxiter, KMeansInitialization initializer, boolean varstat)	Constructor.

Method Summary

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

Methods inherited from class elki.clustering.kmeans.spherical.SphericalKMeans

getInputTypeRestriction

Methods inherited from class elki.clustering.kmeans.AbstractKMeans

getDistance, 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 elki.logging.Logging LOG

The logger for this class.

varstat

protected boolean varstat

Flag whether to compute the final variance statistic.

Constructor Detail

EuclideanSphericalSimplifiedElkanKMeans

public EuclideanSphericalSimplifiedElkanKMeans(int k,
                                               int maxiter,
                                               KMeansInitialization initializer,
                                               boolean varstat)

Constructor.

Parameters:

k - k parameter

maxiter - Maxiter parameter

initializer - Initialization method

varstat - Compute the variance statistic

Method Detail

run

public Clustering<KMeansModel> run(elki.database.relation.Relation<V> relation)

Description copied from interface: KMeans

Run the clustering algorithm.

Specified by:

run in interface KMeans<V extends elki.data.NumberVector,KMeansModel>

Overrides:

run in class SphericalKMeans<V extends elki.data.NumberVector>

Parameters:

relation - Relation to process.

Returns:

Clustering result

getLogger

protected elki.logging.Logging getLogger()

Description copied from class: AbstractKMeans

Get the (STATIC) logger for this class.

Overrides:

getLogger in class SphericalKMeans<V extends elki.data.NumberVector>

Returns:

the static logger