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

Class RandomNormalGeneratedInitialMeans

java.lang.Object

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

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

All Implemented Interfaces:

KMeansInitialization

@Priority(value=-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 RandomNormalGeneratedInitialMeans
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 RandomUniformGeneratedInitialMeans (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 and Description
static class	RandomNormalGeneratedInitialMeans.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
RandomNormalGeneratedInitialMeans(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

RandomNormalGeneratedInitialMeans

public RandomNormalGeneratedInitialMeans(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