Package elki.evaluation.clustering.internal

Class VarianceRatioCriterion

java.lang.Object

elki.evaluation.clustering.internal.VarianceRatioCriterion

All Implemented Interfaces:

Evaluator, ResultProcessor

@Reference(authors="R. B. Calinski, J. Harabasz",
           title="A dendrite method for cluster analysis",
           booktitle="Communications in Statistics - Theory and Methods 3(1)",
           url="https://doi.org/10.1080/03610927408827101",
           bibkey="doi:10.1080/03610927408827101")
@Alias("calinski-harabasz")
public class VarianceRatioCriterion
extends java.lang.Object
implements Evaluator

Compute the Variance Ratio Criterion of a data set, also known as Calinski-Harabasz index.

Reference:

R. B. Calinski, J. Harabasz
A dendrite method for cluster analysis
Communications in Statistics - Theory and Methods 3(1)

Since:

0.7.0

Author:

Stephan Baier, Erich Schubert

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static class	VarianceRatioCriterion.Par	Parameterization class.

Field Summary

Fields

Modifier and Type	Field	Description
private java.lang.String	key	Key for logging statistics.
private static Logging	LOG	Logger for debug output.
private NoiseHandling	noiseOption	Option for noise handling.
private boolean	penalize	Penalize noise, if NoiseHandling.IGNORE_NOISE is set.

Constructor Summary

Constructors

Constructor	Description
VarianceRatioCriterion(NoiseHandling noiseOption, boolean penalize)	Constructor.

Method Summary

Modifier and Type	Method	Description
double	evaluateClustering(Relation<? extends NumberVector> rel, Clustering<?> c)	Evaluate a single clustering.
static int	globalCentroid(Centroid overallCentroid, Relation<? extends NumberVector> rel, java.util.List<? extends Cluster<?>> clusters, NumberVector[] centroids, NoiseHandling noiseOption)	Update the global centroid.
void	processNewResult(java.lang.Object result)	Process a result.

Methods inherited from class java.lang.Object

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

Field Detail

LOG

private static final Logging LOG

Logger for debug output.

noiseOption

private NoiseHandling noiseOption

Option for noise handling.

penalize

private boolean penalize

Penalize noise, if NoiseHandling.IGNORE_NOISE is set.

key

private java.lang.String key

Key for logging statistics.

Constructor Detail

VarianceRatioCriterion

public VarianceRatioCriterion(NoiseHandling noiseOption,
                              boolean penalize)

Constructor.

Parameters:

noiseOption - Flag to control noise handling

penalize - noise, if NoiseHandling.IGNORE_NOISE is set.

Method Detail

evaluateClustering

public double evaluateClustering(Relation<? extends NumberVector> rel,
                                 Clustering<?> c)

Evaluate a single clustering.

Parameters:

rel - Data relation

c - Clustering

Returns:

Variance Ratio Criteria

globalCentroid

public static int globalCentroid(Centroid overallCentroid,
                                 Relation<? extends NumberVector> rel,
                                 java.util.List<? extends Cluster<?>> clusters,
                                 NumberVector[] centroids,
                                 NoiseHandling noiseOption)

Update the global centroid.

Parameters:

overallCentroid - Centroid to udpate

rel - Data relation

clusters - Clusters

centroids - Cluster centroids

Returns:

Number of clusters

processNewResult

public void processNewResult(java.lang.Object result)

Description copied from interface: ResultProcessor

Process a result.

Specified by:

processNewResult in interface ResultProcessor

Parameters:

result - Newly added result subtree.