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

Class RepresentativeUncertainClustering.Parameterizer

java.lang.Object

de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer

de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain.RepresentativeUncertainClustering.Parameterizer

All Implemented Interfaces:

Parameterizer

Enclosing class:

RepresentativeUncertainClustering

public static class RepresentativeUncertainClustering.Parameterizer
extends AbstractParameterizer

Parameterization class.

Author:

Alexander Koos, Erich Schubert

Field Summary

Fields

Modifier and Type	Field and Description
static OptionID	ALGORITHM_ID Parameter to hand an algorithm to be wrapped and run to our instance of RepresentativeUncertainClustering.
protected double	alpha Alpha parameter for confidence.
static OptionID	ALPHA_ID Alpha parameter for confidence estimation.
static OptionID	CLUSTERDISTANCE_ID Distance function to measure the similarity of clusterings.
static int	DEFAULT_ENSEMBLE_DEPTH Default number of clusterings to run.
protected ClusteringDistanceSimilarityFunction	distance Distance (dissimilarity) for clusterinogs.
protected boolean	keep Keep all samples (not only the representative results).
static OptionID	KEEP_SAMPLES_ID Flag to keep all samples.
static OptionID	META_ALGORITHM_ID Parameter to hand an algorithm for creating the meta-clustering to our instance of RepresentativeUncertainClustering.
protected ClusteringAlgorithm<?>	metaAlgorithm Field to store the inner algorithm for meta-clustering
protected int	numsamples Field to store parameter the number of samples.
protected RandomFactory	random Random factory for sampling.
static OptionID	RANDOM_ID Parameter to specify the random generator.
static OptionID	SAMPLES_ID Parameter to specify the amount of clusterings that shall be created and compared.
protected ClusteringAlgorithm<?>	samplesAlgorithm Field to store the algorithm.

Constructor Summary

Constructors

Constructor and Description
Parameterizer()

Method Summary

Modifier and Type	Method and Description
protected RepresentativeUncertainClustering	makeInstance() Make an instance after successful configuration.
protected void	makeOptions(Parameterization config) Add all options.

Methods inherited from class de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer

configure, make

Methods inherited from class java.lang.Object

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

Field Detail

DEFAULT_ENSEMBLE_DEPTH

public static final int DEFAULT_ENSEMBLE_DEPTH

Default number of clusterings to run.

See Also:

Constant Field Values

CLUSTERDISTANCE_ID

public static final OptionID CLUSTERDISTANCE_ID

Distance function to measure the similarity of clusterings.

META_ALGORITHM_ID

public static final OptionID META_ALGORITHM_ID

Parameter to hand an algorithm for creating the meta-clustering to our instance of RepresentativeUncertainClustering. It has to use a metric distance function to work on the sample-clusterings.

ALGORITHM_ID

public static final OptionID ALGORITHM_ID

Parameter to hand an algorithm to be wrapped and run to our instance of RepresentativeUncertainClustering.

SAMPLES_ID

public static final OptionID SAMPLES_ID

Parameter to specify the amount of clusterings that shall be created and compared.

KEEP_SAMPLES_ID

public static final OptionID KEEP_SAMPLES_ID

Flag to keep all samples.

RANDOM_ID

public static final OptionID RANDOM_ID

Parameter to specify the random generator.

ALPHA_ID

public static final OptionID ALPHA_ID

Alpha parameter for confidence estimation.

distance

protected ClusteringDistanceSimilarityFunction distance

Distance (dissimilarity) for clusterinogs.

metaAlgorithm

protected ClusteringAlgorithm<?> metaAlgorithm

Field to store the inner algorithm for meta-clustering

samplesAlgorithm

protected ClusteringAlgorithm<?> samplesAlgorithm

Field to store the algorithm.

numsamples

protected int numsamples

Field to store parameter the number of samples.

random

protected RandomFactory random

Random factory for sampling.

alpha

protected double alpha

Alpha parameter for confidence.

keep

protected boolean keep

Keep all samples (not only the representative results).

Constructor Detail

Parameterizer

public Parameterizer()

Method Detail

makeOptions

protected void makeOptions(Parameterization config)

Description copied from class: AbstractParameterizer

Add all options. ALWAYS call super.makeOptions(config), unless you have a strong reason to do otherwise!

Overrides:

makeOptions in class AbstractParameterizer

Parameters:

config - Parameterization to add options to.

makeInstance

protected RepresentativeUncertainClustering makeInstance()

Description copied from class: AbstractParameterizer

Make an instance after successful configuration.

Specified by:

makeInstance in class AbstractParameterizer

Returns:

instance