de.lmu.ifi.dbs.elki.algorithm.clustering.em

Class TextbookMultivariateGaussianModelFactory<V extends NumberVector>

java.lang.Object

de.lmu.ifi.dbs.elki.algorithm.clustering.em.AbstractEMModelFactory<V,EMModel>

de.lmu.ifi.dbs.elki.algorithm.clustering.em.TextbookMultivariateGaussianModelFactory<V>

Type Parameters:

V - vector type

All Implemented Interfaces:

EMClusterModelFactory<V,EMModel>

public class TextbookMultivariateGaussianModelFactory<V extends NumberVector>
extends AbstractEMModelFactory<V,EMModel>

Factory for EM with multivariate Gaussian model, using the textbook algorithm. There is no reason to use this in practice, it is only useful to study the reliability of the textbook approach.

"Textbook" refers to the E[XY]-E[X]E[Y] equation for covariance, that is numerically not reliable with floating point math, but popular in textbooks.

Again, do not use this. Always prefer MultivariateGaussianModelFactory.

Since:

0.7.5

Author:

Erich Schubert

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	TextbookMultivariateGaussianModelFactory.Parameterizer<V extends NumberVector> Parameterization class

Field Summary

Fields inherited from class de.lmu.ifi.dbs.elki.algorithm.clustering.em.AbstractEMModelFactory

initializer

Constructor Summary

Constructors

Constructor and Description
TextbookMultivariateGaussianModelFactory(KMeansInitialization initializer) Constructor.

Method Summary

Modifier and Type	Method and Description
java.util.List<TextbookMultivariateGaussianModel>	buildInitialModels(Database database, Relation<V> relation, int k, NumberVectorDistanceFunction<? super V> df) Build the initial models

Methods inherited from class java.lang.Object

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

Constructor Detail

TextbookMultivariateGaussianModelFactory

public TextbookMultivariateGaussianModelFactory(KMeansInitialization initializer)

Constructor.

Parameters:

initializer - Class for choosing the initial seeds.

Method Detail

buildInitialModels

public java.util.List<TextbookMultivariateGaussianModel> buildInitialModels(Database database,
                                                                            Relation<V> relation,
                                                                            int k,
                                                                            NumberVectorDistanceFunction<? super V> df)

Description copied from interface: EMClusterModelFactory

Build the initial models

Parameters:

database - Database

relation - Relation

k - Number of clusters

df - Distance function

Returns:

Initial models