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

Class COF<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.COF<O>

Type Parameters:

O - Object type

All Implemented Interfaces:

Algorithm, DistanceBasedAlgorithm<O>, OutlierAlgorithm

@Reference(authors="J. Tang, Z. Chen, A. W. C. Fu, D. W. Cheung",
           title="Enhancing effectiveness of outlier detections for low density patterns",
           booktitle="In Advances in Knowledge Discovery and Data Mining",
           url="http://dx.doi.org/10.1007/3-540-47887-6_53")
public class COF<O>
extends AbstractDistanceBasedAlgorithm<O,OutlierResult>
implements OutlierAlgorithm

Connectivity-based outlier factor (COF). Reference:

J. Tang, Z. Chen, A. W. C. Fu, D. W. Cheung
Enhancing effectiveness of outlier detections for low density patterns.
In Advances in Knowledge Discovery and Data Mining.

Author:

Erich Schubert

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	COF.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
COF(int k, DistanceFunction<? super O> distanceFunction) Constructor.

Method Summary

Methods

Modifier and Type	Method and Description
protected void	computeAverageChainingDistances(KNNQuery<O> knnq, DistanceQuery<O> dq, DBIDs ids, WritableDoubleDataStore acds) Computes the average chaining distance, the average length of a path through the given set of points to each target.
private void	computeCOFScores(KNNQuery<O> knnq, DBIDs ids, DoubleDataStore acds, WritableDoubleDataStore cofs, DoubleMinMax cofminmax) Compute Connectivity outlier factors.
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 COF 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

COF

public COF(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 COF algorithm on the given database.

Parameters:

database - Database to query

relation - Data to process

Returns:

COF outlier result

computeAverageChainingDistances

protected void computeAverageChainingDistances(KNNQuery<O> knnq,
                                   DistanceQuery<O> dq,
                                   DBIDs ids,
                                   WritableDoubleDataStore acds)

Computes the average chaining distance, the average length of a path through the given set of points to each target. The authors of COF decided to approximate this value using a weighted mean that assumes every object is reached from the previous point (but actually every point could be best reachable from the first, in which case this does not make much sense.) TODO: can we accelerate this by using the kNN of the neighbors?

Parameters:

knnq - KNN query

dq - Distance query

ids - IDs to process

acds - Storage for average chaining distances

computeCOFScores

private void computeCOFScores(KNNQuery<O> knnq,
                    DBIDs ids,
                    DoubleDataStore acds,
                    WritableDoubleDataStore cofs,
                    DoubleMinMax cofminmax)

Compute Connectivity outlier factors.

Parameters:

knnq - KNN query

ids - IDs to process

acds - Average chaining distances

cofs - Connectivity outlier factor storage

cofminmax - Score minimum/maximum tracker

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