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

Class AbstractKMeans.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>

All Implemented Interfaces:

Parameterizer

Direct Known Subclasses:

KMeansCompare.Parameterizer, KMeansHamerly.Parameterizer, KMeansLloyd.Parameterizer, KMeansMacQueen.Parameterizer, KMeansMinusMinus.Parameterizer, KMeansSimplifiedElkan.Parameterizer, KMeansSort.Parameterizer, KMediansLloyd.Parameterizer, ParallelLloydKMeans.Parameterizer, SingleAssignmentKMeans.Parameterizer, XMeans.Parameterizer

Enclosing class:

AbstractKMeans<V extends NumberVector,M extends Model>

public abstract static class AbstractKMeans.Parameterizer<V extends NumberVector>
extends AbstractNumberVectorDistanceBasedAlgorithm.Parameterizer<V>

Parameterization class.

Author:

Erich Schubert

Field Summary

Fields

Modifier and Type	Field and Description
protected KMeansInitialization	initializer Initialization method.
protected int	k k Parameter.
protected int	maxiter Maximum number of iterations.
protected boolean	varstat Compute the final variance statistic (not used by all).

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 void	getParameterDistanceFunction(Parameterization config) Get the distance function parameter.
protected void	getParameterInitialization(Parameterization config) Get the initialization method parameter.
protected void	getParameterK(Parameterization config) Get the k parameter.
protected void	getParameterMaxIter(Parameterization config) Get the max iterations parameter.
protected void	getParameterVarstat(Parameterization config) Get the variance statistics parameter.
protected abstract AbstractKMeans<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.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

k

protected int k

k Parameter.

maxiter

protected int maxiter

Maximum number of iterations.

initializer

protected KMeansInitialization initializer

Initialization method.

varstat

protected boolean varstat

Compute the final variance statistic (not used by all).

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 AbstractNumberVectorDistanceBasedAlgorithm.Parameterizer<V extends NumberVector>

Parameters:

config - Parameterization to add options to.

getParameterK

protected void getParameterK(Parameterization config)

Get the k parameter.

Parameters:

config - Parameterization

getParameterDistanceFunction

protected void getParameterDistanceFunction(Parameterization config)

Get the distance function parameter.

Parameters:

config - Parameterization

needsMetric

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.

Returns:

true if the algorithm uses triangle inequality

getParameterInitialization

protected void getParameterInitialization(Parameterization config)

Get the initialization method parameter.

Parameters:

config - Parameterization

getParameterMaxIter

protected void getParameterMaxIter(Parameterization config)

Get the max iterations parameter.

Parameters:

config - Parameterization

getParameterVarstat

protected void getParameterVarstat(Parameterization config)

Get the variance statistics parameter.

Parameters:

config - Parameterization

makeInstance

protected abstract AbstractKMeans<V,?> makeInstance()

Description copied from class: AbstractParameterizer

Make an instance after successful configuration.

Specified by:

makeInstance in class AbstractParameterizer

Returns:

instance