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

Class SOD<V extends NumberVector<?>,D extends NumberDistance<D,?>>

java.lang.Object

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

de.lmu.ifi.dbs.elki.algorithm.outlier.subspace.SOD<V,D>

Type Parameters:

V - the type of NumberVector handled by this Algorithm

D - distance type

All Implemented Interfaces:

Algorithm, OutlierAlgorithm, InspectionUtilFrequentlyScanned, Parameterizable

@Title(value="SOD: Subspace outlier degree")
@Description(value="Outlier Detection in Axis-Parallel Subspaces of High Dimensional Data")
@Reference(authors="H.-P. Kriegel, P. Kr\u00f6ger, E. Schubert, A. Zimek",
           title="Outlier Detection in Axis-Parallel Subspaces of High Dimensional Data",
           booktitle="Proceedings of the 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Bangkok, Thailand, 2009",
           url="http://dx.doi.org/10.1007/978-3-642-01307-2")
public class SOD<V extends NumberVector<?>,D extends NumberDistance<D,?>>
extends AbstractAlgorithm<OutlierResult>
implements OutlierAlgorithm

Subspace Outlier Degree. Outlier detection method for axis-parallel subspaces. Reference:

* H.-P. Kriegel, P. Kröger, E. Schubert, A. Zimek:
Outlier Detection in Axis-Parallel Subspaces of High Dimensional Data
In: Proceedings of the 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Bangkok, Thailand, 2009

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	SOD.Parameterizer<V extends NumberVector<?>,D extends NumberDistance<D,?>> Parameterization class.
static class	SOD.SODModel<V extends NumberVector<?>> SOD Model class
protected static class	SOD.SODProxyScoreResult Proxy class that converts a model result to an actual SOD score result.

Field Summary

Fields

Modifier and Type	Field and Description
private double	alpha Holds the value of ALPHA_ID.
static OptionID	ALPHA_ID Parameter to indicate the multiplier for the discriminance value for discerning small from large variances.
private int	knn Holds the value of KNN_ID.
static OptionID	KNN_ID Parameter to specify the number of shared nearest neighbors to be considered for learning the subspace properties., must be an integer greater than 0.
private static Logging	LOG The logger for this class.
static OptionID	SIM_ID Parameter for the similarity function.
private SimilarityFunction<V,D>	similarityFunction The similarity function SIM_ID.

Constructor Summary

Constructors

Constructor and Description
SOD(int knn, double alpha, SimilarityFunction<V,D> similarityFunction) Constructor with parameters.

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.
private DBIDs	getNearestNeighbors(Relation<V> relation, SimilarityQuery<V,D> simQ, DBIDRef queryObject) Provides the k nearest neighbors in terms of the shared nearest neighbor distance.
OutlierResult	run(Relation<V> relation) Performs the SOD algorithm on the given database.

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.

KNN_ID

public static final OptionID KNN_ID

Parameter to specify the number of shared nearest neighbors to be considered for learning the subspace properties., must be an integer greater than 0.

ALPHA_ID

public static final OptionID ALPHA_ID

Parameter to indicate the multiplier for the discriminance value for discerning small from large variances.

SIM_ID

public static final OptionID SIM_ID

Parameter for the similarity function.

knn

private int knn

Holds the value of KNN_ID.

alpha

private double alpha

Holds the value of ALPHA_ID.

similarityFunction

private SimilarityFunction<V extends NumberVector<?>,D extends NumberDistance<D,?>> similarityFunction

The similarity function SIM_ID.

Constructor Detail

SOD

public SOD(int knn,
   double alpha,
   SimilarityFunction<V,D> similarityFunction)

Constructor with parameters.

Parameters:

knn - knn value

alpha - Alpha parameter

similarityFunction - Shared nearest neighbor similarity function

Method Detail

run

public OutlierResult run(Relation<V> relation)

Performs the SOD algorithm on the given database.

Parameters:

relation - Data relation to process

Returns:

Outlier result

getNearestNeighbors

private DBIDs getNearestNeighbors(Relation<V> relation,
                        SimilarityQuery<V,D> simQ,
                        DBIDRef queryObject)

Provides the k nearest neighbors in terms of the shared nearest neighbor distance.

The query object is excluded from the knn list.

Parameters:

relation - the database holding the objects

simQ - similarity function

queryObject - the query object for which the kNNs should be determined

Returns:

the k nearest neighbors in terms of the shared nearest neighbor distance without the query object

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