java.lang.Object
- elki.outlier.intrinsic.ISOS<O>

Type Parameters:: O - Object type.

All Implemented Interfaces:: elki.Algorithm, OutlierAlgorithm

@Title("ISOS: Intrinsic Stochastic Outlier Selection")
@Reference(authors="Erich Schubert, Michael Gertz",
           title="Intrinsic t-Stochastic Neighbor Embedding for Visualization and Outlier Detection: A Remedy Against the Curse of Dimensionality?",
           booktitle="Proc. Int. Conf. Similarity Search and Applications, SISAP 2017",
           url="https://doi.org/10.1007/978-3-319-68474-1_13",
           bibkey="DBLP:conf/sisap/SchubertG17")
public class ISOS<O>
extends java.lang.Object
implements OutlierAlgorithm

Intrinsic Stochastic Outlier Selection.

Reference:

Erich Schubert, Michael Gertz
Intrinsic t-Stochastic Neighbor Embedding for Visualization and Outlier Detection: A Remedy Against the Curse of Dimensionality?
Proc. Int. Conf. Similarity Search and Applications, SISAP 2017

Since:: 0.7.5
Author:: Erich Schubert

Nested Class Summary
- Nested classes/interfaces inherited from interface elki.Algorithm
  elki.Algorithm.Utils

Field Summary

Fields
Modifier and Type	Field	Description
`protected elki.distance.Distance<? super O>`	`distance`	Distance function used.
`protected elki.math.statistics.intrinsicdimensionality.DistanceBasedIntrinsicDimensionalityEstimator`	`estimator`	Estimator of intrinsic dimensionality.
`protected int`	`k`	Number of neighbors (not including query point).
`private static elki.logging.Logging`	`LOG`	Class logger.
`protected double`	`phi`	Expected outlier rate.

Constructor Summary

Constructors
Constructor	Description
`ISOS(elki.distance.Distance<? super O> distance, int k, elki.math.statistics.intrinsicdimensionality.DistanceBasedIntrinsicDimensionalityEstimator estimator)`	Constructor.

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`protected static void`	`adjustDistances(elki.database.ids.DBIDRef ignore, elki.database.ids.DoubleDBIDListIter ki, double max, double id, elki.database.ids.ModifiableDoubleDBIDList dists)`
`protected double`	`estimateID(elki.database.ids.DBIDRef ignore, elki.database.ids.DoubleDBIDListIter it, double[] p)`	Estimate the local intrinsic dimensionality.
`elki.data.type.TypeInformation[]`	`getInputTypeRestriction()`
`static void`	`nominateNeighbors(elki.database.ids.DBIDIter ignore, elki.database.ids.DBIDArrayIter di, double[] p, double norm, elki.database.datastore.WritableDoubleDataStore scores)`	Vote for neighbors not being outliers.
`OutlierResult`	`run(elki.database.relation.Relation<O> relation)`	Run the algorithm.
`static elki.math.DoubleMinMax`	`transformScores(elki.database.datastore.WritableDoubleDataStore scores, elki.database.ids.DBIDs ids, double logPerp, double phi)`	Transform scores

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface elki.outlier.OutlierAlgorithm
autorun

Field Detail

LOG

private static final elki.logging.Logging LOG

Class logger.

distance

protected elki.distance.Distance<? super O> distance

Distance function used.

k
```
protected int k
```
Number of neighbors (not including query point).

estimator

protected elki.math.statistics.intrinsicdimensionality.DistanceBasedIntrinsicDimensionalityEstimator estimator

Estimator of intrinsic dimensionality.

phi
```
protected double phi
```
Expected outlier rate.

Constructor Detail

ISOS

public ISOS(elki.distance.Distance<? super O> distance,
            int k,
            elki.math.statistics.intrinsicdimensionality.DistanceBasedIntrinsicDimensionalityEstimator estimator)

Constructor.

Parameters:: distance - Distance function; k - Number of neighbors to consider; estimator - Estimator of intrinsic dimensionality.

Method Detail

getInputTypeRestriction
```
public elki.data.type.TypeInformation[] getInputTypeRestriction()
```
Specified by:

getInputTypeRestriction in interface elki.Algorithm

run
```
public OutlierResult run(elki.database.relation.Relation<O> relation)
```
Run the algorithm.

Parameters:

relation - data relation.

Returns:

outlier detection result

adjustDistances

protected static void adjustDistances(elki.database.ids.DBIDRef ignore,
                                      elki.database.ids.DoubleDBIDListIter ki,
                                      double max,
                                      double id,
                                      elki.database.ids.ModifiableDoubleDBIDList dists)

estimateID

protected double estimateID(elki.database.ids.DBIDRef ignore,
                            elki.database.ids.DoubleDBIDListIter it,
                            double[] p)

Estimate the local intrinsic dimensionality.

Parameters:: ignore - Object to ignore; it - Iterator; p - Scratch array
Returns:: ID estimate

nominateNeighbors

public static void nominateNeighbors(elki.database.ids.DBIDIter ignore,
                                     elki.database.ids.DBIDArrayIter di,
                                     double[] p,
                                     double norm,
                                     elki.database.datastore.WritableDoubleDataStore scores)

Vote for neighbors not being outliers. The key method of SOS.

Parameters:: ignore - Object to ignore; di - Neighbor object IDs.; p - Probabilities; norm - Normalization factor (1/sum); scores - Output score storage

transformScores

public static elki.math.DoubleMinMax transformScores(elki.database.datastore.WritableDoubleDataStore scores,
                                                     elki.database.ids.DBIDs ids,
                                                     double logPerp,
                                                     double phi)

Transform scores

Parameters:: scores - Scores to transform; ids - DBIDs to process; logPerp - log perplexity; phi - Expected outlier rate
Returns:: Minimum and maximum scores

Class ISOS<O>

Nested Class Summary

Nested classes/interfaces inherited from interface elki.Algorithm