de.lmu.ifi.dbs.elki.algorithm.outlier
Class SOD<V extends NumberVector<V,?>,D extends Distance<D>>

java.lang.Object
  de.lmu.ifi.dbs.elki.logging.AbstractLoggable
      de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm<V,OutlierResult>
          de.lmu.ifi.dbs.elki.algorithm.outlier.SOD<V,D>

Type Parameters:

V - the type of NumberVector handled by this Algorithm

D - the type of Distance used by this Algorithm

All Implemented Interfaces:

Algorithm<V,OutlierResult>, 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<V,?>,D extends Distance<D>>
extends AbstractAlgorithm<V,OutlierResult>

Author:

Arthur Zimek

Nested Class Summary

static class	SOD.SODModel<O extends NumberVector<O,?>>
protected static class	SOD.SODProxyScoreResult Proxy class that converts a model result to an actual SOD score result.

Field Summary

private double	alpha Holds the value of ALPHA_PARAM.
static OptionID	ALPHA_ID OptionID for ALPHA_PARAM
DoubleParameter	ALPHA_PARAM Parameter to indicate the multiplier for the discriminance value for discerning small from large variances.
private int	knn Holds the value of KNN_PARAM.
static OptionID	KNN_ID OptionID for KNN_PARAM
private IntParameter	KNN_PARAM 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 SharedNearestNeighborSimilarityFunction<V,D>	similarityFunction The similarity function.
static AssociationID<SOD.SODModel<?>>	SOD_MODEL The association id to associate a subspace outlier degree.
static AssociationID<Double>	SOD_SCORE The association id for the raw scores.

Fields inherited from class de.lmu.ifi.dbs.elki.logging.AbstractLoggable

debug, logger

Constructor Summary

SOD(Parameterization config)
Constructor, adhering to Parameterizable

Method Summary

private KNNList<DoubleDistance>	getKNN(Database<V> database, Integer queryObject) Provides the k nearest neighbors in terms of the shared nearest neighbor distance.
protected OutlierResult	runInTime(Database<V> database) Performs the SOD algorithm on the given database.

Methods inherited from class de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm

isTime, isVerbose, run, setTime, setVerbose

Methods inherited from class de.lmu.ifi.dbs.elki.logging.AbstractLoggable

debugFine, debugFiner, debugFinest, exception, progress, verbose, warning

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

SOD_MODEL

public static final AssociationID<SOD.SODModel<?>> SOD_MODEL

The association id to associate a subspace outlier degree.

SOD_SCORE

public static final AssociationID<Double> SOD_SCORE

The association id for the raw scores.

KNN_ID

public static final OptionID KNN_ID

OptionID for KNN_PARAM

KNN_PARAM

private final IntParameter KNN_PARAM

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

Default value: 1

Key: -sod.knn

knn

private int knn

Holds the value of KNN_PARAM.

ALPHA_ID

public static final OptionID ALPHA_ID

OptionID for ALPHA_PARAM

ALPHA_PARAM

public final DoubleParameter ALPHA_PARAM

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

Default value: 1.1

Key: -sod.alpha

alpha

private double alpha

Holds the value of ALPHA_PARAM.

similarityFunction

private SharedNearestNeighborSimilarityFunction<V extends NumberVector<V,?>,D extends Distance<D>> similarityFunction

The similarity function.

Constructor Detail

SOD

public SOD(Parameterization config)

Constructor, adhering to Parameterizable

Parameters:

config - Parameterization

Method Detail

runInTime

protected OutlierResult runInTime(Database<V> database)
                           throws IllegalStateException

Performs the SOD algorithm on the given database.

Specified by:

runInTime in class AbstractAlgorithm<V extends NumberVector<V,?>,OutlierResult>

Parameters:

database - the database to run the algorithm on

Returns:

the Result computed by this algorithm

Throws:

IllegalStateException - if the algorithm has not been initialized properly (e.g. the setParameters(String[]) method has been failed to be called).

getKNN

private KNNList<DoubleDistance> getKNN(Database<V> database,
                                       Integer queryObject)

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

The query object is excluded from the knn list.

Parameters:

database - the database holding the objects

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