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

Class LOF<O>

java.lang.Object

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

de.lmu.ifi.dbs.elki.algorithm.AbstractDistanceBasedAlgorithm<O,OutlierResult>

de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LOF<O>

Type Parameters:

O - the type of data objects handled by this algorithm

All Implemented Interfaces:

Algorithm, DistanceBasedAlgorithm<O>, OutlierAlgorithm

@Title(value="LOF: Local Outlier Factor")
@Description(value="Algorithm to compute density-based local outlier factors in a database based on the neighborhood size parameter \'k\'")
@Reference(authors="M. M. Breunig, H.-P. Kriegel, R. Ng, and J. Sander",
           title="LOF: Identifying Density-Based Local Outliers",
           booktitle="Proc. 2nd ACM SIGMOD Int. Conf. on Management of Data (SIGMOD \'00), Dallas, TX, 2000",
           url="http://dx.doi.org/10.1145/342009.335388")
@Alias(value={"de.lmu.ifi.dbs.elki.algorithm.outlier.LOF","LOF"})
public class LOF<O>
extends AbstractDistanceBasedAlgorithm<O,OutlierResult>
implements OutlierAlgorithm

Algorithm to compute density-based local outlier factors in a database based on a specified parameter LOF.Parameterizer.K_ID (-lof.k).

The original LOF parameter was called "minPts", but for consistency within ELKI we have renamed this parameter to "k".

Compatibility note: as of ELKI 0.7.0, we no longer include the query point, for consistency with other methods.

Reference:

M. M. Breunig, H.-P. Kriegel, R. Ng, J. Sander:
LOF: Identifying Density-Based Local Outliers.
In: Proc. 2nd ACM SIGMOD Int. Conf. on Management of Data (SIGMOD'00), Dallas, TX, 2000.

Author:

Erich Schubert, Elke Achtert

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	LOF.Parameterizer<O> Parameterization class.

Field Summary

Fields

Modifier and Type	Field and Description
protected int	k The number of neighbors to query (including the query point!)
private static Logging	LOG The logger for this class.

Fields inherited from interface de.lmu.ifi.dbs.elki.algorithm.DistanceBasedAlgorithm

DISTANCE_FUNCTION_ID

Constructor Summary

Constructors

Constructor and Description
LOF(int k, DistanceFunction<? super O> distanceFunction) Constructor.

Method Summary

Methods

Modifier and Type	Method and Description
protected double	computeLOFScore(KNNQuery<O> knnq, DBIDRef cur, DoubleDataStore lrds) Compute a single LOF score.
private void	computeLOFScores(KNNQuery<O> knnq, DBIDs ids, DoubleDataStore lrds, WritableDoubleDataStore lofs, DoubleMinMax lofminmax) Compute local outlier factors.
protected double	computeLRD(KNNQuery<O> knnq, DBIDIter curr) Compute a single local reachability distance.
private void	computeLRDs(KNNQuery<O> knnq, DBIDs ids, WritableDoubleDataStore lrds) Compute local reachability distances.
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) Runs the LOF algorithm on the given database.

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

getDistanceFunction

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.

k

protected int k

The number of neighbors to query (including the query point!)

Constructor Detail

LOF

public LOF(int k,
   DistanceFunction<? super O> distanceFunction)

Constructor.

Parameters:

k - the number of neighbors to use for comparison (excluding the query point)

distanceFunction - the neighborhood distance function

Method Detail

run

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

Runs the LOF algorithm on the given database.

Parameters:

database - Database to query

relation - Data to process

Returns:

LOF outlier result

computeLRDs

private void computeLRDs(KNNQuery<O> knnq,
               DBIDs ids,
               WritableDoubleDataStore lrds)

Compute local reachability distances.

Parameters:

knnq - KNN query

ids - IDs to process

lrds - Reachability storage

computeLRD

protected double computeLRD(KNNQuery<O> knnq,
                DBIDIter curr)

Compute a single local reachability distance.

Parameters:

knnq - kNN Query

curr - Current object

Returns:

Local Reachability Density

computeLOFScores

private void computeLOFScores(KNNQuery<O> knnq,
                    DBIDs ids,
                    DoubleDataStore lrds,
                    WritableDoubleDataStore lofs,
                    DoubleMinMax lofminmax)

Compute local outlier factors.

Parameters:

knnq - KNN query

ids - IDs to process

lrds - Local reachability distances

lofs - Local outlier factor storage

lofminmax - Score minimum/maximum tracker

computeLOFScore

protected double computeLOFScore(KNNQuery<O> knnq,
                     DBIDRef cur,
                     DoubleDataStore lrds)

Compute a single LOF score.

Parameters:

knnq - kNN query

cur - Current object

lrds - Stored reachability densities

Returns:

LOF score.

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