Package elki.clustering.kmeans.initialization

Class RandomUniformGenerated

java.lang.Object

elki.clustering.kmeans.initialization.AbstractKMeansInitialization

elki.clustering.kmeans.initialization.RandomUniformGenerated

All Implemented Interfaces:

KMeansInitialization

@Priority(-101)
@Reference(authors="R. C. Jancey",
           title="Multidimensional group analysis",
           booktitle="Australian Journal of Botany 14(1)",
           url="https://doi.org/10.1071/BT9660127",
           bibkey="doi:10.1071/BT9660127")
public class RandomUniformGenerated
extends AbstractKMeansInitialization

Initialize k-means by generating random vectors (uniform, within the value range of the data set).

This is attributed to Jancey, but who wrote little more details but "introduced into known but arbitrary positions". This class assumes this refers to uniform positions within the value domain. For a normal distributed variant, see RandomNormalGenerated.

Warning: this tends to produce empty clusters, and is one of the least effective initialization strategies, not recommended for use.

Reference:

R. C. Jancey
Multidimensional group analysis
Australian Journal of Botany 14(1)

Since:

0.5.0

Author:

Erich Schubert

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static class	RandomUniformGenerated.Par	Parameterization class.

Field Summary

Fields inherited from class elki.clustering.kmeans.initialization.AbstractKMeansInitialization

rnd

Constructor Summary

Constructors

Constructor	Description
RandomUniformGenerated(RandomFactory rnd)	Constructor.

Method Summary

Modifier and Type	Method	Description
double[][]	chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)	Choose initial means

Methods inherited from class elki.clustering.kmeans.initialization.AbstractKMeansInitialization

unboxVectors

Methods inherited from class java.lang.Object

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

Constructor Detail

RandomUniformGenerated

public RandomUniformGenerated(RandomFactory rnd)

Constructor.

Parameters:

rnd - Random generator.

Method Detail

chooseInitialMeans

public double[][] chooseInitialMeans(Relation<? extends NumberVector> relation,
                                     int k,
                                     NumberVectorDistance<?> distance)

Description copied from interface: KMeansInitialization

Choose initial means

Parameters:

relation - Relation

k - Parameter k

distance - Distance function

Returns:

List of chosen means for k-means