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

Class RandomUniformGeneratedInitialMeans

java.lang.Object

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

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

All Implemented Interfaces:

KMeansInitialization

@Priority(value=-101)
 @Alias(value={"de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.RandomlyGeneratedInitialMeans","de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization.RandomlyGeneratedInitialMeans"})
 @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 RandomUniformGeneratedInitialMeans
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 RandomNormalGeneratedInitialMeans.

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 and Description
static class	RandomUniformGeneratedInitialMeans.Parameterizer Parameterization class.

Field Summary

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

rnd

Constructor Summary

Constructors

Constructor and Description
RandomUniformGeneratedInitialMeans(RandomFactory rnd) Constructor.

Method Summary

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

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

RandomUniformGeneratedInitialMeans

public RandomUniformGeneratedInitialMeans(RandomFactory rnd)

Constructor.

Parameters:

rnd - Random generator.

Method Detail

chooseInitialMeans

public double[][] chooseInitialMeans(Database database,
                                     Relation<? extends NumberVector> relation,
                                     int k,
                                     NumberVectorDistanceFunction<?> distanceFunction)

Description copied from interface: KMeansInitialization

Choose initial means

Parameters:

database - Database context

relation - Relation

k - Parameter k

distanceFunction - Distance function

Returns:

List of chosen means for k-means