Package elki.clustering.kmeans

Class GMeans<V extends NumberVector,M extends MeanModel>

java.lang.Object

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

elki.clustering.kmeans.XMeans<V,M>

elki.clustering.kmeans.GMeans<V,M>

Type Parameters:

V - Vector

M - Model

All Implemented Interfaces:

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

@Title("G-means")
@Reference(authors="G. Hamerly and C. Elkan",
           title="Learning the k in k-means",
           booktitle="Neural Information Processing Systems",
           url="https://www.researchgate.net/publication/2869155_Learning_the_K_in_K-Means",
           bibkey="DBLP:conf/nips/HamerlyE03")
public class GMeans<V extends NumberVector,M extends MeanModel>
extends XMeans<V,M>

G-Means extends K-Means and estimates the number of centers with Anderson Darling Test.
Implemented as specialization of XMeans.

Reference:

G. Hamerly and C. Elkan
Learning the K in K-Means
Neural Information Processing Systems

Since:

0.8.0

Author:

Robert Gehde

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static class	GMeans.Par<V extends NumberVector,M extends MeanModel>	Parameterization class.

Nested classes/interfaces inherited from class elki.clustering.kmeans.AbstractKMeans

AbstractKMeans.Instance

Nested classes/interfaces inherited from interface elki.Algorithm

Algorithm.Utils

Field Summary

Fields

Modifier and Type	Field	Description
protected double	critical	Critical value
private static Logging	LOG	The logger for this class.

Fields inherited from class elki.clustering.kmeans.XMeans

informationCriterion, innerKMeans, rnd, splitInitializer

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
GMeans(NumberVectorDistance<? super V> distance, double critical, int k_min, int k_max, int maxiter, KMeans<V,M> innerKMeans, KMeansInitialization initializer, RandomFactory random)	Constructor.

Method Summary

Modifier and Type	Method	Description
protected Logging	getLogger()	Get the (STATIC) logger for this class.
protected double[][]	splitCentroid(Cluster<? extends MeanModel> parentCluster, Relation<V> relation)	Split an existing centroid into two initial centers.
protected java.util.List<Cluster<M>>	splitCluster(Cluster<M> parentCluster, Relation<V> relation)	Conditionally splits the clusters based on the information criterion.

Methods inherited from class elki.clustering.kmeans.XMeans

getInputTypeRestriction, run

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.

critical

protected double critical

Critical value

Constructor Detail

GMeans

public GMeans(NumberVectorDistance<? super V> distance,
              double critical,
              int k_min,
              int k_max,
              int maxiter,
              KMeans<V,M> innerKMeans,
              KMeansInitialization initializer,
              RandomFactory random)

Constructor.

Parameters:

distance - Distance function

critical - Critical value

k_min - Minimum number of clusters

k_max - Maximum number of clusters

maxiter - Maximum number of iterations

innerKMeans - Nested k-means algorithm

initializer - Initialization method

random - Random generator

Method Detail

splitCluster

protected java.util.List<Cluster<M>> splitCluster(Cluster<M> parentCluster,
                                                  Relation<V> relation)

Description copied from class: XMeans

Conditionally splits the clusters based on the information criterion.

Overrides:

splitCluster in class XMeans<V extends NumberVector,M extends MeanModel>

Parameters:

parentCluster - Cluster to split

relation - Data relation

Returns:

Parent cluster when split decreases clustering quality or child clusters when split improves clustering.

splitCentroid

protected double[][] splitCentroid(Cluster<? extends MeanModel> parentCluster,
                                   Relation<V> relation)

Description copied from class: XMeans

Split an existing centroid into two initial centers.

Overrides:

splitCentroid in class XMeans<V extends NumberVector,M extends MeanModel>

Parameters:

parentCluster - Existing cluster

relation - Data relation

Returns:

List of new centroids

getLogger

protected Logging getLogger()

Description copied from class: AbstractKMeans

Get the (STATIC) logger for this class.

Overrides:

getLogger in class XMeans<V extends NumberVector,M extends MeanModel>

Returns:

the static logger