@Reference(authors="Q. Zhao, M. Xu, P. Fr\u00e4nti", title="Knee Point Detection on Bayesian Information Criterion", booktitle="20th IEEE International Conference on Tools with Artificial Intelligence", url="https://doi.org/10.1109/ICTAI.2008.154", bibkey="DBLP:conf/ictai/ZhaoXF08") public class BayesianInformationCriterionZhao extends AbstractKMeansQualityMeasure<NumberVector>
Reference:
Q. Zhao, M. Xu, P. Fränti
Knee Point Detection on Bayesian Information Criterion
20th IEEE International Conference on Tools with Artificial Intelligence
Constructor and Description |
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BayesianInformationCriterionZhao() |
Modifier and Type | Method and Description |
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boolean |
isBetter(double currentCost,
double bestCost)
Compare two scores.
|
static <V extends NumberVector> |
logLikelihoodZhao(Relation<V> relation,
Clustering<? extends MeanModel> clustering,
NumberVectorDistanceFunction<? super V> distanceFunction)
Computes log likelihood of an entire clustering.
|
<V extends NumberVector> |
quality(Clustering<? extends MeanModel> clustering,
NumberVectorDistanceFunction<? super V> distanceFunction,
Relation<V> relation)
Calculates and returns the quality measure.
|
logLikelihood, numberOfFreeParameters, numPoints, varianceOfCluster
public <V extends NumberVector> double quality(Clustering<? extends MeanModel> clustering, NumberVectorDistanceFunction<? super V> distanceFunction, Relation<V> relation)
KMeansQualityMeasure
V
- Actual vector type (could be a subtype of O!)clustering
- Clustering to analyzedistanceFunction
- Distance function to use (usually Euclidean or
squared Euclidean!)relation
- Relation for accessing objectspublic static <V extends NumberVector> double logLikelihoodZhao(Relation<V> relation, Clustering<? extends MeanModel> clustering, NumberVectorDistanceFunction<? super V> distanceFunction)
Version as used by Zhao et al.
V
- Vector typerelation
- Data relationclustering
- ClusteringdistanceFunction
- Distance functionpublic boolean isBetter(double currentCost, double bestCost)
KMeansQualityMeasure
currentCost
- New (candiate) cost/scorebestCost
- Existing best cost/score (may be NaN
)true
when the new score is better, or the old score is
NaN
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