Package elki.clustering.kmeans

Class ShallotKMeans<V extends elki.data.NumberVector>

java.lang.Object

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

elki.clustering.kmeans.HamerlyKMeans<V>

elki.clustering.kmeans.ExponionKMeans<V>

elki.clustering.kmeans.ShallotKMeans<V>

Type Parameters:

V - vector datatype

All Implemented Interfaces:

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

@Reference(authors="C. Borgelt",
           title="Even Faster Exact k-Means Clustering",
           booktitle="Proc. 18th Int. Symp. Intelligent Data Analysis (IDA)",
           url="https://doi.org/10.1007/978-3-030-44584-3_8",
           bibkey="DBLP:conf/ida/Borgelt20")
@Priority(200)
public class ShallotKMeans<V extends elki.data.NumberVector>
extends ExponionKMeans<V>

Borgelt's Shallot k-means algorithm, exploiting the triangle inequality.

Reference:

C. Borgelt
Even Faster Exact k-Means Clustering
Proc. 18th Int. Symp. Intelligent Data Analysis (IDA)

Since:

0.8.0

Author:

Erich Schubert

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
protected static class	ShallotKMeans.Instance	Inner instance, storing state for a single data set.
static class	ShallotKMeans.Par<V extends elki.data.NumberVector>	Parameterization class.

Nested classes/interfaces inherited from interface elki.Algorithm

elki.Algorithm.Utils

Field Summary

Fields

Modifier and Type	Field	Description
private static elki.logging.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
ShallotKMeans(elki.distance.NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer, boolean varstat)	Constructor.

Method Summary

Modifier and Type	Method	Description
protected elki.logging.Logging	getLogger()	Get the (STATIC) logger for this class.
Clustering<KMeansModel>	run(elki.database.relation.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 elki.logging.Logging LOG

The logger for this class.

Constructor Detail

ShallotKMeans

public ShallotKMeans(elki.distance.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(elki.database.relation.Relation<V> relation)

Description copied from interface: KMeans

Run the clustering algorithm.

Specified by:

run in interface KMeans<V extends elki.data.NumberVector,KMeansModel>

Overrides:

run in class ExponionKMeans<V extends elki.data.NumberVector>

Parameters:

relation - Relation to process.

Returns:

Clustering result

getLogger

protected elki.logging.Logging getLogger()

Description copied from class: AbstractKMeans

Get the (STATIC) logger for this class.

Overrides:

getLogger in class ExponionKMeans<V extends elki.data.NumberVector>

Returns:

the static logger