WithinClusterVariance (ELKI: Environment for DeveLoping KDD-Applications Supported by Index-Structures)

java.lang.Object
- elki.clustering.kmeans.quality.AbstractKMeansQualityMeasure<NumberVector>
- - elki.clustering.kmeans.quality.WithinClusterVariance

All Implemented Interfaces:

KMeansQualityMeasure<NumberVector>
```
public class WithinClusterVariance
extends AbstractKMeansQualityMeasure<NumberVector>
```
Class for computing the variance in a clustering result (sum-of-squares).

Since:

0.6.0

Author:

Stephan Baier, Erich Schubert

Constructor Summary

Constructors
Constructor Description

WithinClusterVariance()

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`boolean`	`isBetter(double currentCost, double bestCost)`	Compare two scores.
`<V extends NumberVector> double`	`quality(Clustering<? extends MeanModel> clustering, NumberVectorDistance<? super V> distance, Relation<V> relation)`	Calculates and returns the quality measure.

Methods inherited from class elki.clustering.kmeans.quality.AbstractKMeansQualityMeasure
logLikelihood, numberOfFreeParameters, numPoints, varianceContributionOfCluster

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Constructor Detail
  - WithinClusterVariance
```
public WithinClusterVariance()
```
- Method Detail
  - quality
```
public <V extends NumberVector> double quality(Clustering<? extends MeanModel> clustering,
                                               NumberVectorDistance<? super V> distance,
                                               Relation<V> relation)
```
    Description copied from interface: KMeansQualityMeasure
    
    Calculates and returns the quality measure.
    
    Type Parameters:
    
    V - Actual vector type (could be a subtype of O!)
    
    Parameters:
    
    clustering - Clustering to analyze
    
    distance - Distance function to use (usually Euclidean or squared Euclidean!)
    
    relation - Relation for accessing objects
    
    Returns:
    
    quality measure
  - isBetter
```
public boolean isBetter(double currentCost,
                        double bestCost)
```
    Description copied from interface: KMeansQualityMeasure
    
    Compare two scores.
    
    Parameters:
    
    currentCost - New (candiate) cost/score
    
    bestCost - Existing best cost/score (may be NaN)
    
    Returns:
    
    true when the new score is better, or the old score is NaN.