Package elki.clustering.em.models

Interface BetulaClusterModel

All Superinterfaces:

EMClusterModel<NumberVector,EMModel>

All Known Implementing Classes:

DiagonalGaussianModel, MultivariateGaussianModel, SphericalGaussianModel

@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 interface BetulaClusterModel
extends EMClusterModel<NumberVector,EMModel>

Models usable in Betula EM clustering.

References:

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

Since:

0.8.0

Author:

Erich Schubert

Method Summary

Modifier and Type	Method	Description
double	estimateLogDensity(ClusterFeature cf)	Estimate the log likelihood of a clustering feature.
void	updateE(ClusterFeature cf, double prob)	Process one clustering feature in the E step.

Methods inherited from interface elki.clustering.em.models.EMClusterModel

beginEStep, estimateLogDensity, finalizeCluster, finalizeEStep, finalizeFirstPassE, firstPassE, getWeight, needsTwoPass, setWeight, updateE

Method Detail

estimateLogDensity

double estimateLogDensity(ClusterFeature cf)

Estimate the log likelihood of a clustering feature.

Parameters:

cf - ClusteringFeature

Returns:

log likelihood

updateE

void updateE(ClusterFeature cf,
             double prob)

Process one clustering feature in the E step.

Parameters:

cf - Clustering feature to process.

prob - weight of the clustering feature.