Package elki.clustering.kmeans.initialization

Class RandomNormalGenerated

java.lang.Object

elki.clustering.kmeans.initialization.AbstractKMeansInitialization

elki.clustering.kmeans.initialization.RandomNormalGenerated

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 RandomNormalGenerated
extends AbstractKMeansInitialization

Initialize k-means by generating random vectors (normal distributed with \(N(\mu,\sigma)\) in each dimension).

This is a different interpretation of the work of Jancey, who wrote little more details but "introduced into known but arbitrary positions"; but seemingly worked with standardized scores. In contrast to RandomUniformGenerated (which uses a uniform on the entire value range), this class uses a normal distribution based on the estimated parameters. The resulting means should be more central, and thus a bit less likely to become empty (at least if you assume there is no correlation amongst attributes... it is still not competitive with better methods).

Warning: this still tends to produce empty clusters in many situations, 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:

0.7.5

Author:

Erich Schubert

Nested Class Summary

Nested Classes

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

Field Summary

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

rnd

Constructor Summary

Constructors

Constructor	Description
RandomNormalGenerated(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

RandomNormalGenerated

public RandomNormalGenerated(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