Package | Description |
---|---|
de.lmu.ifi.dbs.elki.algorithm.outlier |
Outlier detection algorithms
|
de.lmu.ifi.dbs.elki.algorithm.outlier.anglebased |
Angle-based outlier detection algorithms.
|
de.lmu.ifi.dbs.elki.algorithm.outlier.clustering |
Clustering based outlier detection.
|
de.lmu.ifi.dbs.elki.algorithm.outlier.distance |
Distance-based outlier detection algorithms, such as DBOutlier and kNN.
|
de.lmu.ifi.dbs.elki.algorithm.outlier.distance.parallel |
Parallel implementations of distance-based outlier detectors.
|
de.lmu.ifi.dbs.elki.algorithm.outlier.intrinsic |
Outlier detection algorithms based on intrinsic dimensionality.
|
de.lmu.ifi.dbs.elki.algorithm.outlier.lof |
LOF family of outlier detection algorithms.
|
de.lmu.ifi.dbs.elki.algorithm.outlier.lof.parallel |
Parallelized variants of LOF.
|
de.lmu.ifi.dbs.elki.algorithm.outlier.meta |
Meta outlier detection algorithms: external scores, score rescaling.
|
de.lmu.ifi.dbs.elki.algorithm.outlier.spatial |
Spatial outlier detection algorithms
|
de.lmu.ifi.dbs.elki.algorithm.outlier.subspace |
Subspace outlier detection methods.
|
de.lmu.ifi.dbs.elki.algorithm.outlier.svm |
Support-Vector-Machines for outlier detection.
|
de.lmu.ifi.dbs.elki.algorithm.outlier.trivial |
Trivial outlier detection algorithms: no outliers, all outliers, label outliers.
|
de.lmu.ifi.dbs.elki.application.greedyensemble |
Greedy ensembles for outlier detection.
|
de.lmu.ifi.dbs.elki.evaluation.histogram |
Functionality for the evaluation of algorithms using histograms.
|
de.lmu.ifi.dbs.elki.evaluation.outlier |
Evaluate an outlier score using a misclassification based cost model.
|
de.lmu.ifi.dbs.elki.evaluation.scores |
Evaluation of rankings and scorings.
|
de.lmu.ifi.dbs.elki.evaluation.scores.adapter |
Adapter classes for ranking and scoring measures.
|
de.lmu.ifi.dbs.elki.result |
Result types, representation and handling
|
de.lmu.ifi.dbs.elki.utilities.scaling.outlier |
Scaling of Outlier scores, that require a statistical analysis of the occurring values
|
de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.outlier |
Visualizers for outlier scores based on 2D projections.
|
Modifier and Type | Method and Description |
---|---|
OutlierResult |
OutlierAlgorithm.run(Database database) |
OutlierResult |
OPTICSOF.run(Database database,
Relation<O> relation)
Perform OPTICS-based outlier detection.
|
OutlierResult |
DWOF.run(Database database,
Relation<O> relation)
Performs the Generalized DWOF_SCORE algorithm on the given database by
calling all the other methods in the proper order.
|
OutlierResult |
SimpleCOP.run(Database database,
Relation<V> data) |
OutlierResult |
GaussianUniformMixture.run(Relation<V> relation)
Run the algorithm
|
OutlierResult |
GaussianModel.run(Relation<V> relation)
Run the algorithm
|
OutlierResult |
COP.run(Relation<V> relation)
Process a single relation.
|
Modifier and Type | Method and Description |
---|---|
OutlierResult |
LBABOD.run(Database db,
Relation<V> relation)
Run LB-ABOD on the data set.
|
OutlierResult |
FastABOD.run(Database db,
Relation<V> relation)
Run Fast-ABOD on the data set.
|
OutlierResult |
ABOD.run(Database db,
Relation<V> relation)
Run ABOD on the data set.
|
Modifier and Type | Method and Description |
---|---|
OutlierResult |
SilhouetteOutlierDetection.run(Database database) |
OutlierResult |
KMeansOutlierDetection.run(Database database,
Relation<O> relation)
Run the outlier detection algorithm.
|
OutlierResult |
EMOutlier.run(Database database,
Relation<V> relation)
Runs the algorithm in the timed evaluation part.
|
Modifier and Type | Method and Description |
---|---|
OutlierResult |
ReferenceBasedOutlierDetection.run(Database database,
Relation<? extends NumberVector> relation)
Run the algorithm on the given relation.
|
OutlierResult |
ODIN.run(Database database,
Relation<O> relation)
Run the ODIN algorithm
|
OutlierResult |
LocalIsolationCoefficient.run(Database database,
Relation<O> relation)
Runs the algorithm in the timed evaluation part.
|
OutlierResult |
KNNWeightOutlier.run(Database database,
Relation<O> relation)
Runs the algorithm in the timed evaluation part.
|
OutlierResult |
KNNOutlier.run(Database database,
Relation<O> relation)
Runs the algorithm in the timed evaluation part.
|
OutlierResult |
HilOut.run(Database database,
Relation<O> relation) |
OutlierResult |
AbstractDBOutlier.run(Database database,
Relation<O> relation)
Runs the algorithm in the timed evaluation part.
|
OutlierResult |
KNNOutlier.run(Relation<O> relation)
Runs the algorithm in the timed evaluation part.
|
Modifier and Type | Method and Description |
---|---|
OutlierResult |
ParallelKNNWeightOutlier.run(Database database,
Relation<O> relation)
Run the parallel kNN weight outlier detector.
|
OutlierResult |
ParallelKNNOutlier.run(Database database,
Relation<O> relation) |
Modifier and Type | Method and Description |
---|---|
OutlierResult |
IntrinsicDimensionalityOutlier.run(Database database,
Relation<O> relation)
Run the algorithm
|
OutlierResult |
IDOS.run(Database database,
Relation<O> relation)
Run the algorithm
|
Modifier and Type | Field and Description |
---|---|
private OutlierResult |
FlexibleLOF.LOFResult.result
The result of the run of the
FlexibleLOF algorithm. |
Modifier and Type | Method and Description |
---|---|
OutlierResult |
FlexibleLOF.LOFResult.getResult()
Get the outlier result.
|
OutlierResult |
VarianceOfVolume.run(Database database,
Relation<O> relation)
Runs the VOV algorithm on the given database.
|
OutlierResult |
SimplifiedLOF.run(Database database,
Relation<O> relation)
Run the Simple LOF algorithm.
|
OutlierResult |
SimpleKernelDensityLOF.run(Database database,
Relation<O> relation)
Run the naive kernel density LOF algorithm.
|
OutlierResult |
OnlineLOF.run(Database database,
Relation<O> relation)
Performs the Generalized LOF_SCORE algorithm on the given database by
calling
#doRunInTime(Database) and adds a OnlineLOF.LOFKNNListener to
the preprocessors. |
OutlierResult |
LoOP.run(Database database,
Relation<O> relation)
Performs the LoOP algorithm on the given database.
|
OutlierResult |
LOF.run(Database database,
Relation<O> relation)
Runs the LOF algorithm on the given database.
|
OutlierResult |
LOCI.run(Database database,
Relation<O> relation)
Run the algorithm
|
OutlierResult |
LDOF.run(Database database,
Relation<O> relation)
Run the algorithm
|
OutlierResult |
LDF.run(Database database,
Relation<O> relation)
Run the naive kernel density LOF algorithm.
|
OutlierResult |
KDEOS.run(Database database,
Relation<O> rel)
Run the KDEOS outlier detection algorithm.
|
OutlierResult |
INFLO.run(Database database,
Relation<O> relation)
Run the algorithm
|
OutlierResult |
FlexibleLOF.run(Database database,
Relation<O> relation)
Performs the Generalized LOF algorithm on the given database by calling
FlexibleLOF.doRunInTime(de.lmu.ifi.dbs.elki.database.ids.DBIDs, de.lmu.ifi.dbs.elki.database.query.knn.KNNQuery<O>, de.lmu.ifi.dbs.elki.database.query.knn.KNNQuery<O>, de.lmu.ifi.dbs.elki.logging.progress.StepProgress) . |
OutlierResult |
COF.run(Database database,
Relation<O> relation)
Runs the COF algorithm on the given database.
|
OutlierResult |
ALOCI.run(Database database,
Relation<O> relation) |
Constructor and Description |
---|
FlexibleLOF.LOFResult(OutlierResult result,
KNNQuery<O> kNNRefer,
KNNQuery<O> kNNReach,
WritableDoubleDataStore lrds,
WritableDoubleDataStore lofs)
Encapsulates information generated during a run of the
FlexibleLOF algorithm. |
Modifier and Type | Method and Description |
---|---|
OutlierResult |
ParallelSimplifiedLOF.run(Database database,
Relation<O> relation) |
OutlierResult |
ParallelLOF.run(Database database,
Relation<O> relation) |
Modifier and Type | Method and Description |
---|---|
private OutlierResult |
RescaleMetaOutlierAlgorithm.getOutlierResult(ResultHierarchy hier,
Result result)
Find an OutlierResult to work with.
|
OutlierResult |
SimpleOutlierEnsemble.run(Database database) |
OutlierResult |
RescaleMetaOutlierAlgorithm.run(Database database) |
OutlierResult |
ExternalDoubleOutlierScore.run(Database database,
Relation<?> relation)
Run the algorithm.
|
OutlierResult |
FeatureBagging.run(Database database,
Relation<NumberVector> relation)
Run the algorithm on a data set.
|
OutlierResult |
HiCS.run(Relation<V> relation)
Perform HiCS on a given database.
|
Modifier and Type | Method and Description |
---|---|
OutlierResult |
TrimmedMeanApproach.run(Database database,
Relation<N> nrel,
Relation<? extends NumberVector> relation)
Run the algorithm.
|
OutlierResult |
CTLuZTestOutlier.run(Database database,
Relation<N> nrel,
Relation<? extends NumberVector> relation)
Main method.
|
OutlierResult |
CTLuScatterplotOutlier.run(Database database,
Relation<N> nrel,
Relation<? extends NumberVector> relation)
Main method.
|
OutlierResult |
CTLuMoranScatterplotOutlier.run(Database database,
Relation<N> nrel,
Relation<? extends NumberVector> relation)
Main method.
|
OutlierResult |
CTLuMedianAlgorithm.run(Database database,
Relation<N> nrel,
Relation<? extends NumberVector> relation)
Main method.
|
OutlierResult |
SOF.run(Database database,
Relation<N> spatial,
Relation<O> relation)
The main run method
|
OutlierResult |
SLOM.run(Database database,
Relation<N> spatial,
Relation<O> relation) |
OutlierResult |
CTLuMedianMultipleAttributes.run(Database database,
Relation<N> spatial,
Relation<O> attributes)
Run the algorithm
|
OutlierResult |
CTLuMeanMultipleAttributes.run(Database database,
Relation<N> spatial,
Relation<O> attributes)
Run the algorithm
|
OutlierResult |
CTLuGLSBackwardSearchAlgorithm.run(Database database,
Relation<V> relationx,
Relation<? extends NumberVector> relationy)
Run the algorithm
|
OutlierResult |
CTLuRandomWalkEC.run(Relation<P> spatial,
Relation<? extends NumberVector> relation)
Run the algorithm.
|
Modifier and Type | Method and Description |
---|---|
OutlierResult |
OutRankS1.run(Database database) |
OutlierResult |
AggarwalYuEvolutionary.run(Database database,
Relation<V> relation)
Performs the evolutionary algorithm on the given database.
|
OutlierResult |
SOD.run(Relation<V> relation)
Performs the SOD algorithm on the given database.
|
OutlierResult |
OUTRES.run(Relation<V> relation)
Main loop for OUTRES
|
OutlierResult |
AggarwalYuNaive.run(Relation<V> relation)
Run the algorithm on the given relation.
|
Modifier and Type | Method and Description |
---|---|
OutlierResult |
LibSVMOneClassOutlierDetection.run(Relation<V> relation)
Run one-class SVM.
|
Modifier and Type | Method and Description |
---|---|
OutlierResult |
TrivialGeneratedOutlier.run(Database database) |
OutlierResult |
ByLabelOutlier.run(Database database) |
OutlierResult |
TrivialNoOutlier.run(Relation<?> relation)
Run the actual algorithm.
|
OutlierResult |
TrivialAllOutlier.run(Relation<?> relation)
Run the actual algorithm.
|
OutlierResult |
ByLabelOutlier.run(Relation<?> relation)
Run the algorithm
|
OutlierResult |
TrivialAverageCoordinateOutlier.run(Relation<? extends NumberVector> relation)
Run the actual algorithm.
|
OutlierResult |
TrivialGeneratedOutlier.run(Relation<Model> models,
Relation<NumberVector> vecs,
Relation<?> labels)
Run the algorithm
|
Modifier and Type | Method and Description |
---|---|
(package private) void |
ComputeKNNOutlierScores.writeResult(PrintStream out,
DBIDs ids,
OutlierResult result,
ScalingFunction scaling,
String label)
Write a single output line.
|
Modifier and Type | Method and Description |
---|---|
HistogramResult<DoubleVector> |
ComputeOutlierHistogram.evaluateOutlierResult(Database database,
OutlierResult or)
Evaluate a single outlier result as histogram.
|
Modifier and Type | Method and Description |
---|---|
private OutlierROCCurve.ROCResult |
OutlierROCCurve.computeROCResult(int size,
SetDBIDs positiveids,
OutlierResult or) |
protected JudgeOutlierScores.ScoreResult |
JudgeOutlierScores.computeScore(DBIDs ids,
DBIDs outlierIds,
OutlierResult or)
Evaluate a single outlier score result.
|
private OutlierSmROCCurve.SmROCResult |
OutlierSmROCCurve.computeSmROCResult(SetDBIDs positiveids,
OutlierResult or) |
private EvaluationResult |
OutlierRankingEvaluation.evaluateOutlierResult(int size,
SetDBIDs positiveids,
OutlierResult or) |
private Clustering<Model> |
OutlierThresholdClustering.split(OutlierResult or) |
Modifier and Type | Method and Description |
---|---|
double |
ScoreEvaluation.evaluate(DBIDs ids,
OutlierResult outlier)
Evaluate given a set of positives and a scoring.
|
double |
AbstractScoreEvaluation.evaluate(DBIDs ids,
OutlierResult outlier) |
Constructor and Description |
---|
OutlierScoreAdapter(OutlierResult o)
Constructor.
|
Modifier and Type | Method and Description |
---|---|
static List<OutlierResult> |
ResultUtil.getOutlierResults(Result r)
Collect all outlier results from a Result
|
Modifier and Type | Method and Description |
---|---|
private void |
KMLOutputHandler.writeOutlierResult(XMLStreamWriter xmlw,
OutlierResult outlierResult,
Database database) |
Modifier and Type | Method and Description |
---|---|
void |
TopKOutlierScaling.prepare(OutlierResult or) |
void |
StandardDeviationScaling.prepare(OutlierResult or) |
void |
SqrtStandardDeviationScaling.prepare(OutlierResult or) |
void |
SigmoidOutlierScalingFunction.prepare(OutlierResult or) |
void |
RankingPseudoOutlierScaling.prepare(OutlierResult or) |
void |
OutlierSqrtScaling.prepare(OutlierResult or) |
void |
OutlierScalingFunction.prepare(OutlierResult or)
Prepare is called once for each data set, before getScaled() will be
called.
|
void |
OutlierMinusLogScaling.prepare(OutlierResult or) |
void |
OutlierLinearScaling.prepare(OutlierResult or) |
void |
OutlierGammaScaling.prepare(OutlierResult or) |
void |
MultiplicativeInverseScaling.prepare(OutlierResult or) |
void |
MixtureModelOutlierScalingFunction.prepare(OutlierResult or) |
void |
MinusLogStandardDeviationScaling.prepare(OutlierResult or) |
void |
MinusLogGammaScaling.prepare(OutlierResult or) |
void |
LogRankingPseudoOutlierScaling.prepare(OutlierResult or) |
void |
HeDESNormalizationOutlierScaling.prepare(OutlierResult or) |
void |
COPOutlierScaling.prepare(OutlierResult or) |
Modifier and Type | Field and Description |
---|---|
protected OutlierResult |
BubbleVisualization.Instance.result
The outlier result to visualize
|
Copyright © 2015 ELKI Development Team, Lehr- und Forschungseinheit für Datenbanksysteme, Ludwig-Maximilians-Universität München. License information.