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

Class KMeansOutlierDetection<O extends NumberVector>

java.lang.Object

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

de.lmu.ifi.dbs.elki.algorithm.outlier.clustering.KMeansOutlierDetection<O>

Type Parameters:

O - Object type

All Implemented Interfaces:

Algorithm, OutlierAlgorithm

public class KMeansOutlierDetection<O extends NumberVector>
extends AbstractAlgorithm<OutlierResult>
implements OutlierAlgorithm

Outlier detection by using k-means clustering. The scores are assigned by the objects distance to the nearest center. We don't have a clear reference for this approach, but it seems to be a best practise in some areas to remove objects that have the largest distance from their center. If you need to cite this approach, please cite the ELKI version you used (use the ELKI publication list for citation information and BibTeX templates).

Author:

Erich Schubert

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	KMeansOutlierDetection.Parameterizer<O extends NumberVector> Parameterizer.

Field Summary

Fields

Modifier and Type	Field and Description
(package private) KMeans<O,?>	clusterer Clustering algorithm to use
private static Logging	LOG Class logger.

Constructor Summary

Constructors

Constructor and Description
KMeansOutlierDetection(KMeans<O,?> clusterer) 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 outlier detection algorithm.

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.

clusterer

KMeans<O extends NumberVector,?> clusterer

Clustering algorithm to use

Constructor Detail

KMeansOutlierDetection

public KMeansOutlierDetection(KMeans<O,?> clusterer)

Constructor.

Parameters:

clusterer - Clustering algorithm

Method Detail

run

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

Run the outlier detection algorithm.

Parameters:

database - Database

relation - Relation

Returns:

Outlier detection 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