Package elki.clustering.kmeans

Class ExponionKMeans<V extends NumberVector>

java.lang.Object

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

elki.clustering.kmeans.HamerlyKMeans<V>

elki.clustering.kmeans.ExponionKMeans<V>

Type Parameters:

V - vector datatype

All Implemented Interfaces:

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

Direct Known Subclasses:

ShallotKMeans

@Reference(authors="J. Newling",
           title="Fast k-means with accurate bounds",
           booktitle="Proc. 33nd Int. Conf. on Machine Learning, ICML 2016",
           url="http://jmlr.org/proceedings/papers/v48/newling16.html",
           bibkey="DBLP:conf/icml/NewlingF16")
@Priority(199)
public class ExponionKMeans<V extends NumberVector>
extends HamerlyKMeans<V>

Newlings's Exponion k-means algorithm, exploiting the triangle inequality.

This is not a complete implementation, the approximative sorting part is missing. We also had to guess on the paper how to make best use of F.

Reference:

J. Newling
Fast k-means with accurate bounds
Proc. 33nd Int. Conf. on Machine Learning, ICML 2016

Since:

0.7.5

Author:

Erich Schubert

Nested Class Summary

Nested Classes

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

Fields inherited from class elki.clustering.kmeans.HamerlyKMeans

varstat

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
ExponionKMeans(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.

Constructor Detail

ExponionKMeans

public ExponionKMeans(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.

Specified by:

run in interface KMeans<V extends NumberVector,KMeansModel>

Overrides:

run in class HamerlyKMeans<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 HamerlyKMeans<V extends NumberVector>

Returns:

the static logger