Package elki.clustering.kmeans.spherical

Class SphericalKMeans<V extends NumberVector>

java.lang.Object

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

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

Type Parameters:

V - vector datatype

All Implemented Interfaces:

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

Direct Known Subclasses:

EuclideanSphericalHamerlyKMeans, EuclideanSphericalSimplifiedElkanKMeans, SphericalHamerlyKMeans, SphericalSimplifiedElkanKMeans, SphericalSimplifiedHamerlyKMeans, SphericalSingleAssignmentKMeans

@Reference(authors="I. S. Dhillon, D. S. Modha",
           title="Concept Decompositions for Large Sparse Text Data Using Clustering",
           booktitle="Machine Learning 42",
           url="https://doi.org/10.1023/A:1007612920971",
           bibkey="DBLP:journals/ml/DhillonM01")
public class SphericalKMeans<V extends NumberVector>
extends AbstractKMeans<V,KMeansModel>

The standard spherical k-means algorithm.

Reference:

I. S. Dhillon, D. S. Modha
Concept Decompositions for Large Sparse Text Data Using Clustering
Machine Learning 42

Since:

0.8.0

Author:

Alexander Voß, Erich Schubert

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static class	SphericalKMeans.Instance	Instance for a particular data set.
static class	SphericalKMeans.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	Class logger

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
SphericalKMeans(int k, int maxiter, KMeansInitialization initializer)	Constructor.

Method Summary

Modifier and Type	Method	Description
TypeInformation[]	getInputTypeRestriction()	Get the input type restriction used for negotiating the data query.
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, 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

Class logger

Constructor Detail

SphericalKMeans

public SphericalKMeans(int k,
                       int maxiter,
                       KMeansInitialization initializer)

Constructor.

Parameters:

k - Number of clusters

maxiter - Maximum number of iterations

initializer - Initialization class

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

getInputTypeRestriction

public TypeInformation[] getInputTypeRestriction()

Description copied from interface: Algorithm

Get the input type restriction used for negotiating the data query.

Specified by:

getInputTypeRestriction in interface Algorithm

Overrides:

getInputTypeRestriction in class AbstractKMeans<V extends NumberVector,KMeansModel>

Returns:

Type restriction

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