Package | Description |
---|---|
de.lmu.ifi.dbs.elki.algorithm.outlier |
Outlier detection algorithms
|
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.trivial |
Trivial outlier detection algorithms: no outliers, all outliers, label outliers.
|
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.roc |
Evaluation of rankings using ROC AUC (Receiver Operation Characteristics - Area Under Curve)
|
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.vis2d |
Visualizers based on 2D projections.
|
Modifier and Type | Field and Description |
---|---|
private OutlierResult |
LOF.LOFResult.result
The result of the run of the
LOF algorithm. |
Modifier and Type | Method and Description |
---|---|
OutlierResult |
ABOD.getFastRanking(Relation<V> relation,
int k,
int sampleSize)
Main part of the algorithm.
|
OutlierResult |
ABOD.getRanking(Relation<V> relation,
int k)
Main part of the algorithm.
|
OutlierResult |
LOF.LOFResult.getResult() |
OutlierResult |
INFLO.run(Database database) |
OutlierResult |
LOCI.run(Database database)
Runs the algorithm in the timed evaluation part.
|
OutlierResult |
OutlierAlgorithm.run(Database database) |
OutlierResult |
KNNOutlier.run(Database database,
Relation<O> relation)
Runs the algorithm in the timed evaluation part.
|
OutlierResult |
LoOP.run(Database database,
Relation<O> relation)
Performs the LoOP algorithm on the given database.
|
OutlierResult |
AbstractDBOutlier.run(Database database,
Relation<O> relation)
Runs the algorithm in the timed evaluation part.
|
OutlierResult |
OPTICSOF.run(Database database,
Relation<O> relation)
Perform OPTICS-based outlier detection.
|
OutlierResult |
LDOF.run(Database database,
Relation<O> relation) |
OutlierResult |
KNNWeightOutlier.run(Database database,
Relation<O> relation)
Runs the algorithm in the timed evaluation part.
|
OutlierResult |
ABOD.run(Database database,
Relation<V> relation)
Run ABOD on the data set
|
OutlierResult |
EMOutlier.run(Database database,
Relation<V> relation)
Runs the algorithm in the timed evaluation part.
|
OutlierResult |
AggarwalYuEvolutionary.run(Database database,
Relation<V> relation)
Performs the evolutionary algorithm on the given database.
|
OutlierResult |
OnlineLOF.run(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 |
LOF.run(Relation<O> relation)
Performs the Generalized LOF_SCORE algorithm on the given database by
calling
#doRunInTime(Database) . |
OutlierResult |
GaussianUniformMixture.run(Relation<V> relation) |
OutlierResult |
ReferenceBasedOutlierDetection.run(Relation<V> relation)
Run the algorithm on the given relation.
|
OutlierResult |
GaussianModel.run(Relation<V> relation) |
OutlierResult |
AggarwalYuNaive.run(Relation<V> relation)
Run the algorithm on the given relation.
|
OutlierResult |
SOD.run(Relation<V> relation)
Performs the SOD algorithm on the given database.
|
Constructor and Description |
---|
LOF.LOFResult(OutlierResult result,
KNNQuery<O,D> kNNRefer,
KNNQuery<O,D> kNNReach,
WritableDataStore<Double> lrds,
WritableDataStore<Double> lofs)
Encapsulates information generated during a run of the
LOF
algorithm. |
Modifier and Type | Method and Description |
---|---|
private OutlierResult |
RescaleMetaOutlierAlgorithm.getOutlierResult(Result result)
Find an OutlierResult to work with.
|
OutlierResult |
RescaleMetaOutlierAlgorithm.run(Database database) |
OutlierResult |
ExternalDoubleOutlierScore.run(Database database,
Relation<?> relation)
Run the algorithm.
|
OutlierResult |
FeatureBagging.run(Relation<NumberVector<?,?>> relation)
Run the algorithm on a data set.
|
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 |
SLOM.run(Database database,
Relation<N> spatial,
Relation<O> relation) |
OutlierResult |
SOF.run(Database database,
Relation<N> spatial,
Relation<O> relation)
The main run method
|
OutlierResult |
CTLuRandomWalkEC.run(Relation<N> spatial,
Relation<? extends NumberVector<?,?>> relation)
Run the algorithm
|
OutlierResult |
CTLuScatterplotOutlier.run(Relation<N> nrel,
Relation<? extends NumberVector<?,?>> relation)
Main method
|
OutlierResult |
CTLuMoranScatterplotOutlier.run(Relation<N> nrel,
Relation<? extends NumberVector<?,?>> relation)
Main method
|
OutlierResult |
CTLuMedianAlgorithm.run(Relation<N> nrel,
Relation<? extends NumberVector<?,?>> relation)
Main method
|
OutlierResult |
CTLuMeanMultipleAttributes.run(Relation<N> spatial,
Relation<O> attributes) |
OutlierResult |
CTLuMedianMultipleAttributes.run(Relation<N> spatial,
Relation<O> attributes)
Run the algorithm
|
OutlierResult |
CTLuGLSBackwardSearchAlgorithm.run(Relation<V> relationx,
Relation<? extends NumberVector<?,?>> relationy)
Run the algorithm
|
Modifier and Type | Method and Description |
---|---|
OutlierResult |
ByLabelOutlier.run(Database database) |
OutlierResult |
TrivialNoOutlier.run(Relation<?> relation)
Run the actual algorithm.
|
OutlierResult |
ByLabelOutlier.run(Relation<?> relation)
Run the algorithm
|
OutlierResult |
TrivialAllOutlier.run(Relation<?> relation)
Run the actual algorithm.
|
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 |
---|---|
protected JudgeOutlierScores.ScoreResult |
JudgeOutlierScores.computeScore(DBIDs ids,
DBIDs outlierIds,
OutlierResult or)
Evaluate a single outlier score result.
|
Modifier and Type | Method and Description |
---|---|
private ComputeROCCurve.ROCResult |
ComputeROCCurve.computeROCResult(int size,
SetDBIDs positiveids,
OutlierResult or) |
Constructor and Description |
---|
ROC.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.writeKMLData(XMLStreamWriter out,
OutlierResult outlierResult,
Database database) |
Modifier and Type | Method and Description |
---|---|
private static double |
MultiplicativeInverseScaling.getScaleValue(OutlierResult or)
Compute the scaling value in a linear scan over the annotation.
|
void |
OutlierScalingFunction.prepare(OutlierResult or)
Prepare is called once for each data set, before getScaled() will be
called.
|
void |
OutlierMinusLogScaling.prepare(OutlierResult or) |
void |
RankingPseudoOutlierScaling.prepare(OutlierResult or) |
void |
OutlierLinearScaling.prepare(OutlierResult or) |
void |
StandardDeviationScaling.prepare(OutlierResult or) |
void |
MixtureModelOutlierScalingFunction.prepare(OutlierResult or) |
void |
MinusLogGammaScaling.prepare(OutlierResult or) |
void |
TopKOutlierScaling.prepare(OutlierResult or) |
void |
OutlierSqrtScaling.prepare(OutlierResult or) |
void |
SigmoidOutlierScalingFunction.prepare(OutlierResult or) |
void |
SqrtStandardDeviationScaling.prepare(OutlierResult or) |
void |
HeDESNormalizationOutlierScaling.prepare(OutlierResult or) |
void |
MinusLogStandardDeviationScaling.prepare(OutlierResult or) |
void |
OutlierGammaScaling.prepare(OutlierResult or) |
void |
MultiplicativeInverseScaling.prepare(OutlierResult or) |
Modifier and Type | Field and Description |
---|---|
protected OutlierResult |
BubbleVisualization.result
The outlier result to visualize
|