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

Packages that use de.lmu.ifi.dbs.elki.evaluation.clustering.internal

Package	Description
de.lmu.ifi.dbs.elki.algorithm.outlier.clustering	Clustering based outlier detection.
de.lmu.ifi.dbs.elki.evaluation.clustering.internal	Internal evaluation measures for clusterings.

Classes in de.lmu.ifi.dbs.elki.evaluation.clustering.internal used by de.lmu.ifi.dbs.elki.algorithm.outlier.clustering

Class and Description
NoiseHandling Options for handling noise in internal measures.

Classes in de.lmu.ifi.dbs.elki.evaluation.clustering.internal used by de.lmu.ifi.dbs.elki.evaluation.clustering.internal

Class and Description
EvaluateCIndex Compute the C-index of a data set.
EvaluateConcordantPairs Compute the Gamma Criterion of a data set.
EvaluateDaviesBouldin Compute the Davies-Bouldin index of a data set.
EvaluateDBCV Compute the Density-Based Clustering Validation Index.
EvaluatePBMIndex Compute the PBM index of a clustering Reference: M.
EvaluateSilhouette Compute the silhouette of a data set.
EvaluateSimplifiedSilhouette Compute the simplified silhouette of a data set.
EvaluateSquaredErrors Evaluate a clustering by reporting the squared errors (SSE, SSQ), as used by k-means.
EvaluateVarianceRatioCriteria Compute the Variance Ratio Criteria of a data set, also known as Calinski-Harabasz index.
NoiseHandling Options for handling noise in internal measures.