Package elki.clustering.kmeans.spherical

Class SphericalSimplifiedHamerlyKMeans<V extends NumberVector>

java.lang.Object

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

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

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

Type Parameters:

V - vector datatype

All Implemented Interfaces:

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

@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") @Reference(authors="Erich Schubert",title="A Triangle Inequality for Cosine Similarity",booktitle="Int. Conf. on Similarity Search and Applications, SISAP 2021",url="https://doi.org/10.1007/978-3-030-89657-7_3",bibkey="DBLP:conf/sisap/Schubert21")
public class SphericalSimplifiedHamerlyKMeans<V extends NumberVector>
extends SphericalKMeans<V>

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

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

The underlying triangle inequality used for pruning is introduced in:

Erich Schubert
A Triangle Inequality for Cosine Similarity
Int. Conf. on Similarity Search and Applications, SISAP 2021

Since:

0.8.0

Author:

Erich Schubert

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
protected static class	SphericalSimplifiedHamerlyKMeans.Instance	Inner instance, storing state for a single data set.
static class	SphericalSimplifiedHamerlyKMeans.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
SphericalSimplifiedHamerlyKMeans(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.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 Logging LOG

The logger for this class.

varstat

protected boolean varstat

Flag whether to compute the final variance statistic.

Constructor Detail

SphericalSimplifiedHamerlyKMeans

public SphericalSimplifiedHamerlyKMeans(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(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 SphericalKMeans<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 SphericalKMeans<V extends NumberVector>

Returns:

the static logger