Package elki.clustering.em

Class BetulaGMMWeighted

java.lang.Object

elki.clustering.em.BetulaGMM

elki.clustering.em.BetulaGMMWeighted

All Implemented Interfaces:

Algorithm, ClusteringAlgorithm<Clustering<EMModel>>

@Priority(-100)
@Reference(authors="Andreas Lang and Erich Schubert",
           title="BETULA: Fast Clustering of Large Data with Improved BIRCH CF-Trees",
           booktitle="Information Systems",
           url="https://doi.org/10.1016/j.is.2021.101918",
           bibkey="DBLP:journals/is/LangS22")
public class BetulaGMMWeighted
extends BetulaGMM

Clustering by expectation maximization (EM-Algorithm), also known as Gaussian Mixture Modeling (GMM), with optional MAP regularization. This version performs a more complex weighting based on the overlap of Gaussians – this is more expensive, and experimentally did not produce much better results than BetulaGMM.

Reference:

Andreas Lang and Erich Schubert
BETULA: Fast Clustering of Large Data with Improved BIRCH CF-Trees
Information Systems

Since:

0.8.0

Author:

Andreas Lang

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static class	BetulaGMMWeighted.Par	Parameterizer

Nested classes/interfaces inherited from interface elki.Algorithm

Algorithm.Utils

Field Summary

Fields inherited from class elki.clustering.em.BetulaGMM

cffactory, initializer, k, maxiter, MIN_LOGLIKELIHOOD, SOFT_TYPE

Constructor Summary

Constructors

Constructor	Description
BetulaGMMWeighted(CFTree.Factory<?> cffactory, double delta, int k, int maxiter, boolean soft, BetulaClusterModelFactory<?> initialization, double prior)	Constructor.

Method Summary

Modifier and Type	Method	Description
double	assignProbabilitiesToInstances(java.util.ArrayList<? extends ClusterFeature> cfs, java.util.List<? extends BetulaClusterModel> models, java.util.Map<ClusterFeature,double[]> probClusterIGivenX)	Assigns the current probability values to the instances in the database and compute the expectation value of the current mixture of distributions.

Methods inherited from class elki.clustering.em.BetulaGMM

assignProbabilitiesToInstances, getInputTypeRestriction, recomputeCovarianceMatrices, run

Methods inherited from class java.lang.Object

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

Methods inherited from interface elki.clustering.ClusteringAlgorithm

autorun

Constructor Detail

BetulaGMMWeighted

public BetulaGMMWeighted(CFTree.Factory<?> cffactory,
                         double delta,
                         int k,
                         int maxiter,
                         boolean soft,
                         BetulaClusterModelFactory<?> initialization,
                         double prior)

Constructor.

Parameters:

cffactory - CFTree factory

k - Number of clusters

maxiter - Maximum number of iterations

initialization - Initialization method

prior - MAP prior

Method Detail

assignProbabilitiesToInstances

public double assignProbabilitiesToInstances(java.util.ArrayList<? extends ClusterFeature> cfs,
                                             java.util.List<? extends BetulaClusterModel> models,
                                             java.util.Map<ClusterFeature,double[]> probClusterIGivenX)

Description copied from class: BetulaGMM

Assigns the current probability values to the instances in the database and compute the expectation value of the current mixture of distributions.

Computed as the sum of the logarithms of the prior probability of each instance.

Overrides:

assignProbabilitiesToInstances in class BetulaGMM

Parameters:

cfs - the cluster features to evaluate

models - Cluster models

probClusterIGivenX - Output storage for cluster probabilities

Returns:

the expectation value of the current mixture of distributions