de.lmu.ifi.dbs.elki.algorithm.outlier.distance

Class KNNSOS<O>

java.lang.Object

de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm<R>

de.lmu.ifi.dbs.elki.algorithm.AbstractDistanceBasedAlgorithm<O,OutlierResult>

de.lmu.ifi.dbs.elki.algorithm.outlier.distance.KNNSOS<O>

Type Parameters:

O - Object type.

All Implemented Interfaces:

Algorithm, DistanceBasedAlgorithm<O>, OutlierAlgorithm

@Title(value="KNNSOS: k-Nearest-Neighbor 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") @Reference(authors="J. Janssens, F. Husz\u00e1r, E. Postma, J. van den Herik",title="Stochastic Outlier Selection",booktitle="TiCC TR 2012\u2013001",url="https://www.tilburguniversity.edu/upload/b7bac5b2-9b00-402a-9261-7849aa019fbb_sostr.pdf",bibkey="tr/tilburg/JanssensHPv12")
public class KNNSOS<O>
extends AbstractDistanceBasedAlgorithm<O,OutlierResult>
implements OutlierAlgorithm

kNN-based adaption of Stochastic Outlier Selection.

This is a trivial variation of Stochastic Outlier Selection to benefit from KNN indexes, but not discussed in the original publication. Instead of setting perplexity, we choose the number of neighbors k, and set perplexity simply to k/3. Objects outside of the kNN are not considered anymore.

Reference of the kNN variant:

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

Original reference:

J. Janssens, F. Huszár, E. Postma, J. van den Herik
Stochastic Outlier Selection
TiCC TR 2012–001

Since:

0.7.5

Author:

Erich Schubert

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	KNNSOS.Parameterizer<O> Parameterization class.

Field Summary

Fields

Modifier and Type	Field and Description
protected int	k Number of neighbors (not including query point).
private static Logging	LOG Class logger.
protected double	phi Expected outlier rate.

Fields inherited from class de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm

ALGORITHM_ID

Fields inherited from interface de.lmu.ifi.dbs.elki.algorithm.DistanceBasedAlgorithm

DISTANCE_FUNCTION_ID

Constructor Summary

Constructors

Constructor and Description
KNNSOS(DistanceFunction<? super O> distance, int k) Constructor.

Method Summary

Modifier and Type	Method and Description
TypeInformation[]	getInputTypeRestriction() Get the input type restriction used for negotiating the data query.
protected Logging	getLogger() Get the (STATIC) logger for this class.
OutlierResult	run(Relation<O> relation) Run the algorithm.

Methods inherited from class de.lmu.ifi.dbs.elki.algorithm.AbstractDistanceBasedAlgorithm

getDistanceFunction

Methods inherited from class de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm

run

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface de.lmu.ifi.dbs.elki.algorithm.outlier.OutlierAlgorithm

run

Field Detail

LOG

private static final Logging LOG

Class logger.

k

protected int k

Number of neighbors (not including query point).

phi

protected double phi

Expected outlier rate.

Constructor Detail

KNNSOS

public KNNSOS(DistanceFunction<? super O> distance,
              int k)

Constructor.

Parameters:

distance - Distance function

k - Number of neighbors to consider

Method Detail

getInputTypeRestriction

public TypeInformation[] getInputTypeRestriction()

Description copied from class: AbstractAlgorithm

Get the input type restriction used for negotiating the data query.

Specified by:

getInputTypeRestriction in interface Algorithm

Specified by:

getInputTypeRestriction in class AbstractAlgorithm<OutlierResult>

Returns:

Type restriction

run

public OutlierResult run(Relation<O> relation)

Run the algorithm.

Parameters:

relation - data relation

Returns:

outlier detection result

getLogger

protected Logging getLogger()

Description copied from class: AbstractAlgorithm

Get the (STATIC) logger for this class.

Specified by:

getLogger in class AbstractAlgorithm<OutlierResult>

Returns:

the static logger