de.lmu.ifi.dbs.elki.evaluation.clustering.internal

Class EvaluateVarianceRatioCriteria<O>

java.lang.Object

de.lmu.ifi.dbs.elki.evaluation.clustering.internal.EvaluateVarianceRatioCriteria<O>

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(value="calinski-harabasz")
public class EvaluateVarianceRatioCriteria<O>
extends java.lang.Object
implements Evaluator

Compute the Variance Ratio Criteria 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 and Description
static class	EvaluateVarianceRatioCriteria.Parameterizer<O> Parameterization class.

Field Summary

Fields

Modifier and Type	Field and 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 and Description
EvaluateVarianceRatioCriteria(NoiseHandling noiseOption, boolean penalize) Constructor.

Method Summary

Modifier and Type	Method and Description
double	evaluateClustering(Database db, 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(ResultHierarchy hier, Result 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

EvaluateVarianceRatioCriteria

public EvaluateVarianceRatioCriteria(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(Database db,
                                 Relation<? extends NumberVector> rel,
                                 Clustering<?> c)

Evaluate a single clustering.

Parameters:

db - Database

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(ResultHierarchy hier,
                             Result result)

Description copied from interface: ResultProcessor

Process a result.

Specified by:

processNewResult in interface ResultProcessor

Parameters:

hier - The base of the result tree.

result - Newly added result subtree.