Package elki.outlier.clustering

Class EMOutlier<V extends NumberVector>

java.lang.Object

elki.outlier.clustering.EMOutlier<V>

Type Parameters:

V - Vector type

All Implemented Interfaces:

Algorithm, OutlierAlgorithm

@Title("EM Outlier: Outlier Detection based on the generic EM clustering")
@Description("The outlier score assigned is based on the highest cluster probability obtained from EM clustering.")
public class EMOutlier<V extends NumberVector>
extends java.lang.Object
implements OutlierAlgorithm

Outlier detection algorithm using EM Clustering.

If an object does not belong to any cluster it is supposed to be an outlier. We use the log likelihood sum that the object is explained by the clustering as anomaly score in this approach. If you use this implementation as reference, please cite the latest ELKI release.

Since:

0.3

Author:

Erich Schubert

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static class	EMOutlier.Par<V extends NumberVector>	Parameterization class.

Nested classes/interfaces inherited from interface elki.Algorithm

Algorithm.Utils

Field Summary

Fields

Modifier and Type	Field	Description
protected double	delta	Delta parameter
protected int	k	Number of clusters
private static Logging	LOG	The logger for this class.
protected int	maxiter	Maximum number of iterations to allow
protected EMClusterModelFactory<? super V,?>	mfactory	Factory for producing the initial cluster model.
protected static double	MIN_LOGLIKELIHOOD	Minimum loglikelihood to avoid -infinity.
protected int	miniter	Minimum number of iterations to do
protected double	prior	Prior to enable MAP estimation (use 0 for MLE)

Constructor Summary

Constructors

Constructor	Description
EMOutlier(int k, double delta, EMClusterModelFactory<? super V,?> mfactory, int miniter, int maxiter, double prior)	Constructor.

Method Summary

Modifier and Type	Method	Description
TypeInformation[]	getInputTypeRestriction()	Get the input type restriction used for negotiating the data query.
OutlierResult	run(Relation<V> relation)	Runs the algorithm in the timed evaluation part.

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 Logging LOG

The logger for this class.

k

protected int k

Number of clusters

delta

protected double delta

Delta parameter

mfactory

protected EMClusterModelFactory<? super V extends NumberVector,?> mfactory

Factory for producing the initial cluster model.

miniter

protected int miniter

Minimum number of iterations to do

maxiter

protected int maxiter

Maximum number of iterations to allow

prior

protected double prior

Prior to enable MAP estimation (use 0 for MLE)

MIN_LOGLIKELIHOOD

protected static final double MIN_LOGLIKELIHOOD

Minimum loglikelihood to avoid -infinity.

See Also:

Constant Field Values

Constructor Detail

EMOutlier

public EMOutlier(int k,
                 double delta,
                 EMClusterModelFactory<? super V,?> mfactory,
                 int miniter,
                 int maxiter,
                 double prior)

Constructor.

Parameters:

k - k parameter

delta - delta parameter

mfactory - EM cluster model factory

miniter - Minimum number of iterations

maxiter - Maximum number of iterations

prior - MAP prior

Method Detail

getInputTypeRestriction

public TypeInformation[] getInputTypeRestriction()

Description copied from interface: Algorithm

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

Specified by:

getInputTypeRestriction in interface Algorithm

Returns:

Type restriction

run

public OutlierResult run(Relation<V> relation)

Runs the algorithm in the timed evaluation part.

Parameters:

relation - Relation to process

Returns:

Outlier result