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

Class XMeans.Parameterizer<V extends NumberVector,M extends MeanModel>

java.lang.Object

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

de.lmu.ifi.dbs.elki.algorithm.AbstractNumberVectorDistanceBasedAlgorithm.Parameterizer<V>

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.AbstractKMeans.Parameterizer<V>

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.XMeans.Parameterizer<V,M>

Type Parameters:

V - Vector type

M - Model type of inner algorithm

All Implemented Interfaces:

Parameterizer

Enclosing class:

XMeans<V extends NumberVector,M extends MeanModel>

public static class XMeans.Parameterizer<V extends NumberVector,M extends MeanModel>
extends AbstractKMeans.Parameterizer<V>

Parameterization class.

Author:

Tibor Goldschwendt, Erich Schubert

Field Summary

Fields

Modifier and Type	Field and Description
static OptionID	INFORMATION_CRITERION_ID Quality measure to use for evaluating splits.
protected KMeansQualityMeasure<V>	informationCriterion Information criterion.
static OptionID	INNER_KMEANS_ID Parameter to specify the kMeans variant.
protected KMeans<V,M>	innerKMeans Variant of kMeans
protected int	k_max Minimum and maximum number of result clusters.
protected int	k_min Minimum and maximum number of result clusters.
static OptionID	K_MIN_ID Minimum number of clusters.
private RandomFactory	random Random number generator.
static OptionID	SEED_ID Randomization seed.

Fields inherited from class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.AbstractKMeans.Parameterizer

initializer, k, maxiter, varstat

Fields inherited from class de.lmu.ifi.dbs.elki.algorithm.AbstractNumberVectorDistanceBasedAlgorithm.Parameterizer

distanceFunction

Constructor Summary

Constructors

Constructor and Description
Parameterizer()

Method Summary

Modifier and Type	Method and Description
protected XMeans<V,M>	makeInstance() Make an instance after successful configuration.
protected void	makeOptions(Parameterization config) Add all options.

Methods inherited from class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.AbstractKMeans.Parameterizer

getParameterDistanceFunction, getParameterInitialization, getParameterK, getParameterMaxIter, getParameterVarstat, needsMetric

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

INNER_KMEANS_ID

public static final OptionID INNER_KMEANS_ID

Parameter to specify the kMeans variant.

K_MIN_ID

public static final OptionID K_MIN_ID

Minimum number of clusters.

SEED_ID

public static final OptionID SEED_ID

Randomization seed.

INFORMATION_CRITERION_ID

public static final OptionID INFORMATION_CRITERION_ID

Quality measure to use for evaluating splits.

innerKMeans

protected KMeans<V extends NumberVector,M extends MeanModel> innerKMeans

Variant of kMeans

informationCriterion

protected KMeansQualityMeasure<V extends NumberVector> informationCriterion

Information criterion.

k_min

protected int k_min

Minimum and maximum number of result clusters.

k_max

protected int k_max

Minimum and maximum number of result clusters.

random

private RandomFactory random

Random number generator.

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 AbstractKMeans.Parameterizer<V extends NumberVector>

Parameters:

config - Parameterization to add options to.

makeInstance

protected XMeans<V,M> makeInstance()

Description copied from class: AbstractParameterizer

Make an instance after successful configuration.

Specified by:

makeInstance in class AbstractKMeans.Parameterizer<V extends NumberVector>

Returns:

instance