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

Class TrimmedMeanApproach<N>

java.lang.Object

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

de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.AbstractNeighborhoodOutlier<N>

de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.TrimmedMeanApproach<N>

Type Parameters:

N - Neighborhood object type

All Implemented Interfaces:

Algorithm, OutlierAlgorithm

@Title(value="A Trimmed Mean Approach to Finding Spatial Outliers")
@Description(value="A local trimmed mean approach to evaluating the spatial outlier factor which is the degree that a site is outlying compared to its neighbors")
@Reference(authors="Tianming Hu and Sam Yuan Sung",
           title="A trimmed mean approach to finding spatial outliers",
           booktitle="Intelligent Data Analysis, Volume 8, 2004",
           url="http://iospress.metapress.com/content/PLVLT6431DVNJXNK")
public class TrimmedMeanApproach<N>
extends AbstractNeighborhoodOutlier<N>

A Trimmed Mean Approach to Finding Spatial Outliers. Outliers are defined by their value deviation from a trimmed mean of the neighbors.

Reference:
Tianming Hu and Sam Yuan Sung
A Trimmed Mean Approach to finding Spatial Outliers
in Intelligent Data Analysis, Volume 8, 2004.

the contiguity Matrix is definit as
wij = 1/k if j is neighbor of i, k is the neighbors size of i.

Author:

Ahmed Hettab

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	TrimmedMeanApproach.Parameterizer<N> Parameterizer.

Field Summary

Fields

Modifier and Type	Field and Description
private static Logging	LOG The logger for this class.
private double	p the parameter p.

Fields inherited from class de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.AbstractNeighborhoodOutlier

NEIGHBORHOOD_ID

Constructor Summary

Constructors

Modifier	Constructor and Description
protected	TrimmedMeanApproach(NeighborSetPredicate.Factory<N> npredf, double p) 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<N> nrel, Relation<? extends NumberVector> relation) Run the algorithm.

Methods inherited from class de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.AbstractNeighborhoodOutlier

getNeighborSetPredicateFactory

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

The logger for this class.

p

private double p

the parameter p.

Constructor Detail

TrimmedMeanApproach

protected TrimmedMeanApproach(NeighborSetPredicate.Factory<N> npredf,
                   double p)

Constructor.

Parameters:

p - Parameter p

npredf - Neighborhood factory.

Method Detail

run

public OutlierResult run(Database database,
                Relation<N> nrel,
                Relation<? extends NumberVector> relation)

Run the algorithm.

Parameters:

database - Database

nrel - Neighborhood relation

relation - Data Relation (1 dimensional!)

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

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