All Classes Interface Summary Class Summary Enum Summary
| Class |
Description |
| ABOD<V extends elki.data.NumberVector> |
Angle-Based Outlier Detection / Angle-Based Outlier Factor.
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| ABOD.Par<V extends elki.data.NumberVector> |
Parameterization class.
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| AbstractAggarwalYuOutlier |
Abstract base class for the sparse-grid-cell based outlier detection of
Aggarwal and Yu.
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| AbstractAggarwalYuOutlier.Par |
Parameterization class.
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| AbstractDBOutlier<O> |
Simple distance based outlier detection algorithms.
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| AbstractDBOutlier.Par<O> |
Parameterization class.
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| AbstractDistanceBasedSpatialOutlier<N,O> |
Abstract base class for distance-based spatial outlier detection methods.
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| AbstractNeighborhoodOutlier<O> |
Abstract base class for spatial outlier detection methods using a spatial
neighborhood.
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| AbstractPrecomputedNeighborhood |
Abstract base class for precomputed neighborhoods.
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| AbstractPrecomputedNeighborhood.Factory<O> |
Factory class.
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| AggarwalYuEvolutionary |
Evolutionary variant (EAFOD) of the high-dimensional outlier detection
algorithm by Aggarwal and Yu.
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| AggarwalYuEvolutionary.Individuum |
Individuum for the evolutionary search.
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| AggarwalYuEvolutionary.Par |
Parameterization class.
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| AggarwalYuNaive |
BruteForce variant of the high-dimensional outlier detection algorithm by
Aggarwal and Yu.
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| AggarwalYuNaive.Par |
Parameterization class.
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| ALOCI<V extends elki.data.NumberVector> |
Fast Outlier Detection Using the "approximate Local Correlation Integral".
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| ALOCI.ALOCIQuadTree |
Simple quadtree for ALOCI.
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| ALOCI.Node |
Node of the ALOCI Quadtree
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| ALOCI.Par<O extends elki.data.NumberVector> |
Parameterization class.
|
| BasicOutlierScoreMeta |
Basic outlier score.
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| ByLabelOutlier |
Trivial algorithm that marks outliers by their label.
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| ByLabelOutlier.Par |
Parameterization class.
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| COF<O> |
Connectivity-based Outlier Factor (COF).
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| ComputeOutlierHistogram |
Compute a Histogram to evaluate a ranking algorithm.
|
| ComputeOutlierHistogram.Par |
Parameterization class.
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| COP<V extends elki.data.NumberVector> |
Correlation outlier probability: Outlier Detection in Arbitrarily Oriented
Subspaces
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| COP.DistanceDist |
Score type.
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| COP.Par<V extends elki.data.NumberVector> |
Parameterization class.
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| COPOutlierScaling |
CDF based outlier score scaling.
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| COPOutlierScaling.Par |
Parameterization class.
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| CTLuGLSBackwardSearchAlgorithm<V extends elki.data.NumberVector> |
GLS-Backward Search is a statistical approach to detecting spatial outliers.
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| CTLuMeanMultipleAttributes<N,O extends elki.data.NumberVector> |
Mean Approach is used to discover spatial outliers with multiple attributes.
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| CTLuMedianAlgorithm<N> |
Median Algorithm of C.
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| CTLuMedianMultipleAttributes<N,O extends elki.data.NumberVector> |
Median Approach is used to discover spatial outliers with multiple
attributes.
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| CTLuMoranScatterplotOutlier<N> |
Moran scatterplot outliers, based on the standardized deviation from the
local and global means.
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| CTLuRandomWalkEC<O> |
Spatial outlier detection based on random walks.
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| CTLuScatterplotOutlier<N> |
Scatterplot-outlier is a spatial outlier detection method that performs a
linear regression of object attributes and their neighbors average value.
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| CTLuZTestOutlier<N> |
Detect outliers by comparing their attribute value to the mean and standard
deviation of their neighborhood.
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| DBOutlierDetection<O> |
Simple distanced based outlier detection algorithm.
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| DBOutlierDetection.Par<O> |
Parameterization class.
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| DBOutlierScore<O> |
Compute percentage of neighbors in the given neighborhood with size d.
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| DBOutlierScore.Par<O> |
Parameterization class.
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| DWOF<O> |
Algorithm to compute dynamic-window outlier factors in a database based on a
specified parameter k, which specifies the number of the neighbors to be
considered during the calculation of the DWOF score.
|
| ExtendedNeighborhood |
Neighborhood obtained by computing the k-fold closure of an existing
neighborhood.
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| ExtendedNeighborhood.Factory<O> |
Factory class.
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| ExtendedNeighborhood.Factory.Par<O> |
Parameterization class.
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| ExternalDoubleOutlierScore |
External outlier detection scores, loading outlier scores from an external
file.
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| ExternalDoubleOutlierScore.Par |
Parameterization class
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| ExternalNeighborhood |
A precomputed neighborhood, loaded from an external file.
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| ExternalNeighborhood.Factory |
Factory class.
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| ExternalNeighborhood.Factory.Par |
Parameterization class.
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| FastABOD<V extends elki.data.NumberVector> |
Fast-ABOD (approximateABOF) version of
Angle-Based Outlier Detection / Angle-Based Outlier Factor.
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| FastABOD.Par<V extends elki.data.NumberVector> |
Parameterization class.
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| FeatureBagging |
A simple ensemble method called "Feature bagging" for outlier detection.
|
| FeatureBagging.Par |
Parameterization class.
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| FlexibleLOF<O> |
Flexible variant of the "Local Outlier Factor" algorithm.
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| FlexibleLOF.LOFResult<O> |
Encapsulates information like the neighborhood, the LRD and LOF values of
the objects during a run of the FlexibleLOF algorithm. |
| FlexibleLOF.Par<O> |
Parameterization class.
|
| GaussianModel |
Outlier detection based on the probability density of the single normal
distribution.
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| GaussianModel.Par |
Parameterization class.
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| GaussianUniformMixture |
Outlier detection algorithm using a mixture model approach.
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| GaussianUniformMixture.Par |
Parameterization class.
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| HeDESNormalizationOutlierScaling |
Normalization used by HeDES
|
| HiCS |
Algorithm to compute High Contrast Subspaces for Density-Based Outlier
Ranking.
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| HiCS.HiCSSubspace |
BitSet that holds a contrast value as field.
|
| HilOut<O extends elki.data.NumberVector> |
Fast Outlier Detection in High Dimensional Spaces
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| HilOut.HilFeature |
Hilbert representation of a single object.
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| HilOut.ScoreType |
Type of output: all scores (upper bounds) or top n only
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| HySortOD |
Hypercube-Based Outlier Detection.
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| HySortOD.DensityStrategy |
Strategy for compute density.
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| HySortOD.Hypercube |
Bounded regions of the space where at least one instance exists.
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| HySortOD.NaiveStrategy |
Naive strategy for computing density.
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| HySortOD.TreeStrategy |
Tree strategy for computing density.
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| HySortOD.TreeStrategy.Node |
Tree node.
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| IDOS<O> |
Intrinsic Dimensional Outlier Detection in High-Dimensional Data.
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| IDOS.Par<O> |
Parameterization class.
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| INFLO<O> |
Influence Outliers using Symmetric Relationship (INFLO) using two-way search,
is an outlier detection method based on LOF; but also using the reverse kNN.
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| INFLO.Par<O> |
Parameterization class.
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| InvertedOutlierScoreMeta |
Class to signal a value-inverted outlier score, i.e. low values are outliers.
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| IsolationForest |
Isolation-Based Anomaly Detection.
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| IsolationForest.ForestBuilder |
Class to build the forest
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| IsolationForest.Node |
Minimalistic tree node for the isolation forest.
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| IsolationForest.Par |
Parameterization class
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| ISOS<O> |
Intrinsic Stochastic Outlier Selection.
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| JudgeOutlierScores |
Compute a Histogram to evaluate a ranking algorithm.
|
| JudgeOutlierScores.Par |
Parameterization class.
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| JudgeOutlierScores.ScoreResult |
Result object for outlier score judgements.
|
| KDEOS<O> |
Generalized Outlier Detection with Flexible Kernel Density Estimates.
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| KNNDD<O> |
Nearest Neighbor Data Description.
|
| KNNDD.Par<O> |
Parameterization class.
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| KNNOutlier<O> |
Outlier Detection based on the distance of an object to its k nearest
neighbor.
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| KNNOutlier.Par<O> |
Parameterization class.
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| KNNSOS<O> |
kNN-based adaption of Stochastic Outlier Selection.
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| KNNWeightOutlier<O> |
Outlier Detection based on the accumulated distances of a point to its k
nearest neighbors.
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| KNNWeightOutlier.Par<O> |
Parameterization class.
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| KNNWeightProcessor |
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| KNNWeightProcessor.Instance |
Instance for precomputing the kNN.
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| LBABOD<V extends elki.data.NumberVector> |
LB-ABOD (lower-bound) version of
Angle-Based Outlier Detection / Angle-Based Outlier Factor.
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| LBABOD.Par<V extends elki.data.NumberVector> |
Parameterization class.
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| LDF<O extends elki.data.NumberVector> |
Outlier Detection with Kernel Density Functions.
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| LDOF<O> |
Computes the LDOF (Local Distance-Based Outlier Factor) for all objects of a
Database.
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| LDOF.Par<O> |
Parameterization class.
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| LID<O> |
Use intrinsic dimensionality for outlier detection.
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| LID.Par<O> |
Parameterization class.
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| LinearWeightedExtendedNeighborhood |
Neighborhood obtained by computing the k-fold closure of an existing
neighborhood.
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| LinearWeightedExtendedNeighborhood.Factory<O> |
Factory class.
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| LinearWeightedExtendedNeighborhood.Factory.Par<O> |
Parameterization class.
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| LocalIsolationCoefficient<O> |
The Local Isolation Coefficient is the sum of the kNN distance and the
average distance to its k nearest neighbors.
|
| LocalIsolationCoefficient.Par<O> |
Parameterization class.
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| LOCI<O> |
Fast Outlier Detection Using the "Local Correlation Integral".
|
| LOCI.DoubleIntArrayList |
Array of double-int values.
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| LOF<O> |
Algorithm to compute density-based local outlier factors in a database based
on a specified parameter -lof.k.
|
| LOFProcessor |
Processor for computing the LOF.
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| LogRankingPseudoOutlierScaling |
This is a pseudo outlier scoring obtained by only considering the ranks of
the objects.
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| LoOP<O> |
LoOP: Local Outlier Probabilities
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| LRDProcessor |
Processor for the "local reachability density" of LOF.
|
| MinusLogGammaScaling |
Scaling that can map arbitrary values to a probability in the range of [0:1],
by assuming a Gamma distribution on the data and evaluating the Gamma CDF.
|
| MinusLogGammaScaling.Par |
Parameterization class.
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| MinusLogStandardDeviationScaling |
Scaling that can map arbitrary values to a probability in the range of [0:1].
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| MinusLogStandardDeviationScaling.Par |
Parameterization class.
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| MixtureModelOutlierScaling |
Tries to fit a mixture model (exponential for inliers and gaussian for
outliers) to the outlier score distribution.
|
| MultiplicativeInverseScaling |
Scaling function to invert values by computing 1/x, but in a variation that
maps the values to the [0:1] interval and avoiding division by 0.
|
| NeighborSetPredicate |
Predicate to obtain the neighbors of a reference object as set.
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| NeighborSetPredicate.Factory<O> |
Factory interface to produce instances.
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| ODIN<O> |
Outlier detection based on the in-degree of the kNN graph.
|
| OnlineLOF<O> |
Incremental version of the LOF Algorithm, supports insertions and
removals. |
| OnlineLOF.Par<O> |
Parameterization class.
|
| OrderingFromRelation |
Ordering obtained from an outlier score.
|
| OutlierAlgorithm |
Generic super interface for outlier detection algorithms.
|
| OutlierGammaScaling |
Scaling that can map arbitrary values to a probability in the range of [0:1]
by assuming a Gamma distribution on the values.
|
| OutlierGammaScaling.Par |
Parameterization class.
|
| OutlierLinearScaling |
Scaling that can map arbitrary values to a value in the range of [0:1].
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| OutlierLinearScaling.Par |
Parameterization class.
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| OutlierMinusLogScaling |
Scaling function to invert values by computing -log(x)
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| OutlierPrecisionAtKCurve |
Compute a curve containing the precision values for an outlier detection
method.
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| OutlierPrecisionAtKCurve.Par |
Parameterization class.
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| OutlierPrecisionAtKCurve.PrecisionAtKCurve |
Precision at K curve.
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| OutlierPrecisionRecallCurve |
Compute a curve containing the precision values for an outlier detection
method.
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| OutlierPrecisionRecallCurve.Par |
Parameterization class.
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| OutlierPrecisionRecallGainCurve |
Compute a curve containing the precision gain and revall gain values for an
outlier detection method.
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| OutlierPrecisionRecallGainCurve.Par |
Parameterization class.
|
| OutlierRankingEvaluation |
Evaluate outlier scores by their ranking
|
| OutlierRankingEvaluation.Par |
Parameterization class.
|
| OutlierResult |
Wrap a typical Outlier result, keeping direct references to the main result
parts.
|
| OutlierROCCurve |
Compute a ROC curve to evaluate a ranking algorithm and compute the
corresponding AUROC value.
|
| OutlierROCCurve.Par |
Parameterization class.
|
| OutlierScaling |
Interface for scaling functions used by Outlier evaluation such as Histograms
and visualization.
|
| OutlierScoreAdapter |
This adapter can be used for an arbitrary collection of Integers, and uses
that id1.compareTo(id2) !
|
| OutlierScoreMeta |
Generic meta information about the value range of an outlier score.
|
| OutlierSmROCCurve |
Smooth ROC curves are a variation of classic ROC curves that takes the scores
into account.
|
| OutlierSmROCCurve.Par |
Parameterization class.
|
| OutlierSmROCCurve.SmROCResult |
Result object for Smooth ROC curves.
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| OutlierSqrtScaling |
Scaling that can map arbitrary positive values to a value in the range of
[0:1].
|
| OutlierSqrtScaling.Par |
Parameterization class.
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| OUTRES |
Adaptive outlierness for subspace outlier ranking (OUTRES).
|
| OUTRES.KernelDensityEstimator |
Kernel density estimation and utility class.
|
| OUTRES.Par |
Parameterization class.
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| ParallelKNNOutlier<O> |
Parallel implementation of KNN Outlier detection.
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| ParallelKNNWeightOutlier<O> |
Parallel implementation of KNN Weight Outlier detection.
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| ParallelLOF<O> |
Parallel implementation of Local Outlier Factor using processors.
|
| ParallelSimplifiedLOF<O> |
Parallel implementation of Simplified-LOF Outlier detection using processors.
|
| PrecomputedKNearestNeighborNeighborhood |
Neighborhoods based on k nearest neighbors.
|
| PrecomputedKNearestNeighborNeighborhood.Factory<O> |
Factory class to instantiate for a particular relation.
|
| ProbabilisticOutlierScore |
Outlier score that is a probability value in the range 0.0 - 1.0
But the baseline may be different from 0.0!
|
| QuotientOutlierScoreMeta |
Score for outlier values generated by a quotient.
|
| RankingPseudoOutlierScaling |
This is a pseudo outlier scoring obtained by only considering the ranks of
the objects.
|
| ReferenceBasedOutlierDetection |
Reference-Based Outlier Detection algorithm, an algorithm that computes kNN
distances approximately, using reference points.
|
| ReferenceBasedOutlierDetection.Par |
Parameterization class.
|
| RescaleMetaOutlierAlgorithm |
Scale another outlier score using the given scaling function.
|
| RescaleMetaOutlierAlgorithm.Par |
Parameterization class
|
| SigmoidOutlierScaling |
Tries to fit a sigmoid to the outlier scores and use it to convert the values
to probability estimates in the range of 0.0 to 1.0
|
| SimpleKernelDensityLOF<O extends elki.data.NumberVector> |
A simple variant of the LOF algorithm, which uses a simple kernel density
estimation instead of the local reachability density.
|
| SimpleOutlierEnsemble |
Simple outlier ensemble method.
|
| SimpleOutlierEnsemble.Par |
Parameterization class.
|
| SimplifiedLOF<O> |
A simplified version of the original LOF algorithm, which does not use the
reachability distance, yielding less stable results on inliers.
|
| SimplifiedLRDProcessor |
Processor for the "local reachability density" of LOF.
|
| SLOM<N,O> |
SLOM: a new measure for local spatial outliers
|
| SOD<V extends elki.data.NumberVector> |
Subspace Outlier Degree: Outlier Detection in Axis-Parallel Subspaces of High
Dimensional Data.
|
| SOD.Par<V extends elki.data.NumberVector> |
Parameterization class.
|
| SOD.SODModel |
SOD Model class
|
| SOF<N,O> |
The Spatial Outlier Factor (SOF) is a spatial
LOF variation. |
| SOS<O> |
Stochastic Outlier Selection.
|
| SqrtStandardDeviationScaling |
Scaling that can map arbitrary values to a probability in the range of [0:1].
|
| SqrtStandardDeviationScaling.Par |
Parameterization class.
|
| StandardDeviationScaling |
Scaling that can map arbitrary values to a probability in the range of [0:1].
|
| StandardDeviationScaling.Par |
Parameterization class.
|
| TopKOutlierScaling |
Outlier scaling function that only keeps the top k outliers.
|
| TopKOutlierScaling.Par |
Parameterization class.
|
| TrimmedMeanApproach<N> |
A Trimmed Mean Approach to Finding Spatial Outliers.
|
| TrivialAllOutlier |
Trivial method that claims all objects to be outliers.
|
| TrivialAverageCoordinateOutlier |
Trivial method that takes the average of all dimensions (for one-dimensional
data that is just the actual value!)
|
| TrivialNoOutlier |
Trivial method that claims to find no outliers.
|
| UnweightedNeighborhoodAdapter |
Adapter to use unweighted neighborhoods in an algorithm that requires
weighted neighborhoods.
|
| UnweightedNeighborhoodAdapter.Factory<O> |
Factory class
|
| VarianceOfVolume<O extends elki.data.spatial.SpatialComparable> |
Variance of Volume for outlier detection.
|
| WeightedNeighborSetPredicate |
Neighbor predicate with weight support.
|
| WeightedNeighborSetPredicate.Factory<O> |
Factory interface to produce instances.
|