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

Type Parameters:

O - Input Object restriction type

All Known Implementing Classes:

AbstractKMeansQualityMeasure, AkaikeInformationCriterion, AkaikeInformationCriterionXMeans, BayesianInformationCriterion, BayesianInformationCriterionXMeans, BayesianInformationCriterionZhao, WithinClusterMeanDistance, WithinClusterVariance
```
public interface KMeansQualityMeasure<O extends NumberVector>
```
Interface for computing the quality of a K-Means clustering.
Important note: some measures are ascending, others are descending, so use the method isBetter(double, double) for ordering.

Since:

0.6.0

Author:

Erich Schubert

Method Summary

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

- Method Detail
  - quality
```
<V extends O> double quality(Clustering<? extends MeanModel> clustering,
                             NumberVectorDistance<? super V> distance,
                             Relation<V> relation)
```
    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
```
boolean isBetter(double currentCost,
                 double bestCost)
```
    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.