de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans

Class KMeansExponion<V extends NumberVector>

java.lang.Object

de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm<R>

de.lmu.ifi.dbs.elki.algorithm.AbstractNumberVectorDistanceBasedAlgorithm<V,Clustering<M>>

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.AbstractKMeans<V,KMeansModel>

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansHamerly<V>

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansExponion<V>

Type Parameters:

V - vector datatype

All Implemented Interfaces:

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

@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")
public class KMeansExponion<V extends NumberVector>
extends KMeansHamerly<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 and Description
protected static class	KMeansExponion.Instance Inner instance, storing state for a single data set.
static class	KMeansExponion.Parameterizer<V extends NumberVector> Parameterization class.

Field Summary

Fields

Modifier and Type	Field and Description
private static Logging	LOG The logger for this class.

Fields inherited from class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansHamerly

varstat

Fields inherited from class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.AbstractKMeans

initializer, k, maxiter

Fields inherited from class de.lmu.ifi.dbs.elki.algorithm.AbstractNumberVectorDistanceBasedAlgorithm

distanceFunction

Fields inherited from class de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm

ALGORITHM_ID

Fields inherited from interface de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeans

INIT_ID, K_ID, MAXITER_ID, SEED_ID, VARSTAT_ID

Fields inherited from interface de.lmu.ifi.dbs.elki.algorithm.DistanceBasedAlgorithm

DISTANCE_FUNCTION_ID

Constructor Summary

Constructors

Constructor and Description
KMeansExponion(NumberVectorDistanceFunction<? super V> distanceFunction, int k, int maxiter, KMeansInitialization initializer, boolean varstat) Constructor.

Method Summary

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

Methods inherited from class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.AbstractKMeans

getInputTypeRestriction, incrementalUpdateMean, initialMeans, means, minusEquals, nearestMeans, plusEquals, plusMinusEquals, setDistanceFunction, setInitializer, setK

Methods inherited from class de.lmu.ifi.dbs.elki.algorithm.AbstractNumberVectorDistanceBasedAlgorithm

getDistanceFunction

Methods inherited from class de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm

run

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface de.lmu.ifi.dbs.elki.algorithm.clustering.ClusteringAlgorithm

run

Methods inherited from interface de.lmu.ifi.dbs.elki.algorithm.DistanceBasedAlgorithm

getDistanceFunction

Field Detail

LOG

private static final Logging LOG

The logger for this class.

Constructor Detail

KMeansExponion

public KMeansExponion(NumberVectorDistanceFunction<? super V> distanceFunction,
                      int k,
                      int maxiter,
                      KMeansInitialization initializer,
                      boolean varstat)

Constructor.

Parameters:

distanceFunction - distance function

k - k parameter

maxiter - Maxiter parameter

initializer - Initialization method

varstat - Compute the variance statistic

Method Detail

run

public Clustering<KMeansModel> run(Database database,
                                   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 KMeansHamerly<V extends NumberVector>

Parameters:

database - Database to run on.

relation - Relation to process.

Returns:

Clustering result

getLogger

protected Logging getLogger()

Description copied from class: AbstractAlgorithm

Get the (STATIC) logger for this class.

Overrides:

getLogger in class KMeansHamerly<V extends NumberVector>

Returns:

the static logger