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

Internal evaluation measures for clusterings.

See: Description

Class Summary

Class	Description
EvaluateCIndex<O>	Compute the C-index of a data set.
EvaluateCIndex.Parameterizer<O>	Parameterization class.
EvaluateConcordantPairs<O>	Compute the Gamma Criterion of a data set.
EvaluateConcordantPairs.Parameterizer<O>	Parameterization class.
EvaluateDaviesBouldin	Compute the Davies-Bouldin index of a data set.
EvaluateDaviesBouldin.Parameterizer	Parameterization class.
EvaluateDBCV<O>	Compute the Density-Based Clustering Validation Index.
EvaluateDBCV.Parameterizer<O>	Parameterization class.
EvaluatePBMIndex	Compute the PBM index of a clustering Reference: M.
EvaluatePBMIndex.Parameterizer	Parameterization class.
EvaluateSilhouette<O>	Compute the silhouette of a data set.
EvaluateSilhouette.Parameterizer<O>	Parameterization class.
EvaluateSimplifiedSilhouette	Compute the simplified silhouette of a data set.
EvaluateSimplifiedSilhouette.Parameterizer	Parameterization class.
EvaluateSquaredErrors	Evaluate a clustering by reporting the squared errors (SSE, SSQ), as used by k-means.
EvaluateSquaredErrors.Parameterizer	Parameterization class.
EvaluateVarianceRatioCriteria<O>	Compute the Variance Ratio Criteria of a data set, also known as Calinski-Harabasz index.
EvaluateVarianceRatioCriteria.Parameterizer<O>	Parameterization class.

Enum Summary

Enum	Description
NoiseHandling	Options for handling noise in internal measures.

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

Internal evaluation measures for clusterings.