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

Class IntrinsicDimensionalityOutlier<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.intrinsic.IntrinsicDimensionalityOutlier<O>

Type Parameters:

O - Object type

All Implemented Interfaces:

Algorithm, DistanceBasedAlgorithm<O>, OutlierAlgorithm

public class IntrinsicDimensionalityOutlier<O>
extends AbstractDistanceBasedAlgorithm<O,OutlierResult>
implements OutlierAlgorithm

Use intrinsic dimensionality for outlier detection. This idea was first explored by Michael Houle, Arthur Zimek, Jonathan von Brünken, et al., and is provided for completeness and for visualization purposes. It turns out that ID provides some insight into outlierness, but cannot be simply used as is. Please see their upcoming publications for an improved solution.

Since:

0.3

Author:

Erich Schubert

Nested Class Summary

Nested Classes

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

Field Summary

Fields

Modifier and Type	Field and Description
protected IntrinsicDimensionalityEstimator	estimator Estimator for intrinsic dimensionality.
protected int	k Number of neighbors to use.
private static Logging	LOG Class logger.

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

DISTANCE_FUNCTION_ID

Constructor Summary

Constructors

Constructor and Description
IntrinsicDimensionalityOutlier(DistanceFunction<? super O> distanceFunction, int k, IntrinsicDimensionalityEstimator estimator) Constructor.

Method Summary

Methods

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(Database database, 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

makeParameterDistanceFunction, 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 to use.

estimator

protected IntrinsicDimensionalityEstimator estimator

Estimator for intrinsic dimensionality.

Constructor Detail

IntrinsicDimensionalityOutlier

public IntrinsicDimensionalityOutlier(DistanceFunction<? super O> distanceFunction,
                              int k,
                              IntrinsicDimensionalityEstimator estimator)

Constructor.

Parameters:

distanceFunction - Distance function

k - Neighborhood size

estimator - Estimator for intrinsic dimensionality

Method Detail

run

public OutlierResult run(Database database,
                Relation<O> relation)

Run the algorithm

Parameters:

database - Database

relation - Data relation

Returns:

Outlier result

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

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