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

Interface EMClusterModel<M extends MeanModel>

All Known Implementing Classes:

DiagonalGaussianModel, MultivariateGaussianModel, SphericalGaussianModel

public interface EMClusterModel<M extends MeanModel>

Models useable in EM clustering.

Since:

0.3

Author:

Erich Schubert

Method Summary

Methods

Modifier and Type	Method and Description
void	beginEStep() Begin the E step.
double	estimateDensity(NumberVector vec) Estimate the likelihood of a vector.
M	finalizeCluster() Finalize a cluster model.
void	finalizeEStep() Finalize the E step.
double	getWeight() Get the cluster weight.
void	setWeight(double weight) Set the cluster weight.
void	updateE(NumberVector vec, double weight) Update the

Method Detail

beginEStep

void beginEStep()

Begin the E step.

updateE

void updateE(NumberVector vec,
           double weight)

Update the

Parameters:

vec - Vector to process

weight - Weight

finalizeEStep

void finalizeEStep()

Finalize the E step.

estimateDensity

double estimateDensity(NumberVector vec)

Estimate the likelihood of a vector.

Parameters:

vec - Vector

Returns:

Likelihood.

finalizeCluster

M finalizeCluster()

Finalize a cluster model.

Returns:

Cluster model

getWeight

double getWeight()

Get the cluster weight.

Returns:

Cluster weight

setWeight

void setWeight(double weight)

Set the cluster weight.

Parameters:

weight - Cluster weight