| Package | Description | 
|---|---|
| de.lmu.ifi.dbs.elki.evaluation | 
 Functionality for the evaluation of algorithms. 
 | 
| de.lmu.ifi.dbs.elki.evaluation.clustering | 
 Evaluation of clustering results 
 | 
| de.lmu.ifi.dbs.elki.evaluation.clustering.extractor | 
 Classes to extract clusterings from hierarchical clustering. 
 | 
| de.lmu.ifi.dbs.elki.evaluation.clustering.internal | 
 Internal evaluation measures for clusterings. 
 | 
| de.lmu.ifi.dbs.elki.evaluation.clustering.pairsegments | 
 Pair-segment analysis of multiple clusterings 
 | 
| de.lmu.ifi.dbs.elki.evaluation.index | 
 Simple index evaluation methods 
 | 
| de.lmu.ifi.dbs.elki.evaluation.outlier | 
 Evaluate an outlier score using a misclassification based cost model 
 | 
| de.lmu.ifi.dbs.elki.evaluation.similaritymatrix | 
 Render a distance matrix to visualize a clustering-distance-combination. 
 | 
| de.lmu.ifi.dbs.elki.workflow | 
 Work flow packages, e.g., following the usual KDD model. 
 | 
| Modifier and Type | Class and Description | 
|---|---|
class  | 
AutomaticEvaluation
Evaluator that tries to auto-run a number of evaluation methods. 
 | 
class  | 
NoAutomaticEvaluation
No-operation evaluator, that only serves the purpose of explicitely disabling
 the default value of  
AutomaticEvaluation, if you do not want
 evaluation to run. | 
| Modifier and Type | Class and Description | 
|---|---|
class  | 
EvaluateClustering
Evaluate a clustering result by comparing it to an existing cluster label. 
 | 
class  | 
LogClusterSizes
This class will log simple statistics on the clusters detected, such as the
 cluster sizes and the number of clusters. 
 | 
| Modifier and Type | Class and Description | 
|---|---|
class  | 
CutDendrogramByHeightExtractor
Extract clusters from a hierarchical clustering, during the evaluation phase. 
 | 
class  | 
CutDendrogramByNumberOfClustersExtractor
Extract clusters from a hierarchical clustering, during the evaluation phase. 
 | 
class  | 
HDBSCANHierarchyExtractionEvaluator
Extract clusters from a hierarchical clustering, during the evaluation phase. 
 | 
class  | 
SimplifiedHierarchyExtractionEvaluator
Extract clusters from a hierarchical clustering, during the evaluation phase. 
 | 
| Modifier and Type | Class and Description | 
|---|---|
class  | 
EvaluateCIndex<O>
Compute the C-index of a data set. 
 | 
class  | 
EvaluateConcordantPairs<O>
Compute the Gamma Criterion of a data set. 
 | 
class  | 
EvaluateDaviesBouldin
Compute the Davies-Bouldin index of a data set. 
 | 
class  | 
EvaluateDBCV<O>
Compute the Density-Based Clustering Validation Index. 
 | 
class  | 
EvaluatePBMIndex
Compute the PBM index of a clustering
 
 Reference:
 
 M. 
 | 
class  | 
EvaluateSilhouette<O>
Compute the silhouette of a data set. 
 | 
class  | 
EvaluateSimplifiedSilhouette
Compute the simplified silhouette of a data set. 
 | 
class  | 
EvaluateSquaredErrors
Evaluate a clustering by reporting the squared errors (SSE, SSQ), as used by
 k-means. 
 | 
class  | 
EvaluateVarianceRatioCriteria<O>
Compute the Variance Ratio Criteria of a data set, also known as
 Calinski-Harabasz index. 
 | 
| Modifier and Type | Class and Description | 
|---|---|
class  | 
ClusterPairSegmentAnalysis
Evaluate clustering results by building segments for their pairs: shared
 pairs and differences. 
 | 
| Modifier and Type | Class and Description | 
|---|---|
class  | 
IndexPurity
Compute the purity of index pages as a naive measure for performance
 capabilities using the Gini index. 
 | 
class  | 
IndexStatistics
Simple index analytics, which includes the toString() dump of index
 information. 
 | 
| Modifier and Type | Class and Description | 
|---|---|
class  | 
ComputeOutlierHistogram
Compute a Histogram to evaluate a ranking algorithm. 
 | 
class  | 
JudgeOutlierScores
Compute a Histogram to evaluate a ranking algorithm. 
 | 
class  | 
OutlierPrecisionAtKCurve
Compute a curve containing the precision values for an outlier detection
 method. 
 | 
class  | 
OutlierPrecisionRecallCurve
Compute a curve containing the precision values for an outlier detection
 method. 
 | 
class  | 
OutlierRankingEvaluation
Evaluate outlier scores by their ranking 
 | 
class  | 
OutlierROCCurve
Compute a ROC curve to evaluate a ranking algorithm and compute the
 corresponding ROCAUC value. 
 | 
class  | 
OutlierSmROCCurve
Smooth ROC curves are a variation of classic ROC curves that takes the scores
 into account. 
 | 
class  | 
OutlierThresholdClustering
Pseudo clustering algorithm that builds clusters based on their outlier
 score. 
 | 
| Modifier and Type | Class and Description | 
|---|---|
class  | 
ComputeSimilarityMatrixImage<O>
Compute a similarity matrix for a distance function. 
 | 
| Modifier and Type | Field and Description | 
|---|---|
private java.util.List<? extends Evaluator> | 
EvaluationStep.evaluators
Evaluators to run. 
 | 
private java.util.List<? extends Evaluator> | 
EvaluationStep.Evaluation.evaluators
Evaluators to run. 
 | 
private java.util.List<Evaluator> | 
EvaluationStep.Parameterizer.evaluators
Evaluators to run 
 | 
| Constructor and Description | 
|---|
Evaluation(ResultHierarchy hier,
          java.util.List<? extends Evaluator> evaluators)
Constructor. 
 | 
EvaluationStep(java.util.List<? extends Evaluator> evaluators)
Constructor. 
 | 
Copyright © 2019 ELKI Development Team. License information.