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

Class KMeansHamerly.Parameterizer<V extends NumberVector>

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.KMeansHamerly.Parameterizer<V>

All Implemented Interfaces:

Parameterizer

Direct Known Subclasses:

KMeansAnnulus.Parameterizer, KMeansExponion.Parameterizer

Enclosing class:

KMeansHamerly<V extends NumberVector>

public static class KMeansHamerly.Parameterizer<V extends NumberVector>
extends AbstractKMeans.Parameterizer<V>

Parameterization class.

Author:

Erich Schubert

Field Summary

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 KMeansHamerly<V>	makeInstance() Make an instance after successful configuration.
protected void	makeOptions(Parameterization config) Add all options.
protected boolean	needsMetric() Users could use other non-metric distances at their own risk; but some k-means variants make explicit use of the triangle inequality, we emit extra warnings then.

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

getParameterDistanceFunction, getParameterInitialization, getParameterK, getParameterMaxIter, getParameterVarstat

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

Constructor Detail

Parameterizer

public Parameterizer()

Method Detail

needsMetric

protected boolean needsMetric()

Description copied from class: AbstractKMeans.Parameterizer

Users could use other non-metric distances at their own risk; but some k-means variants make explicit use of the triangle inequality, we emit extra warnings then.

Overrides:

needsMetric in class AbstractKMeans.Parameterizer<V extends NumberVector>

Returns:

true if the algorithm uses triangle inequality

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 KMeansHamerly<V> makeInstance()

Description copied from class: AbstractParameterizer

Make an instance after successful configuration.

Specified by:

makeInstance in class AbstractKMeans.Parameterizer<V extends NumberVector>

Returns:

instance