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.histogram |
Functionality for the evaluation of algorithms using histograms.
|
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, closely related to CRISP-DM
|
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 |
ExtractFlatClusteringFromHierarchyEvaluator
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 |
EvaluatePBMIndex
Compute the PBM of a data set
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.
|
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 |
ComputeOutlierHistogram
Compute a Histogram to evaluate a ranking algorithm.
|
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 |
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 List<Evaluator> |
EvaluationStep.evaluators
Evaluators to run.
|
private List<Evaluator> |
EvaluationStep.Evaluation.evaluators
Evaluators to run.
|
private List<Evaluator> |
EvaluationStep.Parameterizer.evaluators
Evaluators to run
|
Constructor and Description |
---|
EvaluationStep.Evaluation(ResultHierarchy hier,
List<Evaluator> evaluators)
Constructor.
|
EvaluationStep(List<Evaluator> evaluators)
Constructor.
|
Copyright © 2015 ELKI Development Team, Lehr- und Forschungseinheit für Datenbanksysteme, Ludwig-Maximilians-Universität München. License information.