de.lmu.ifi.dbs.elki.algorithm.outlier
Class KNNOutlier<O extends DatabaseObject,D extends DoubleDistance>

java.lang.Object
  de.lmu.ifi.dbs.elki.logging.AbstractLoggable
      de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm<O,R>
          de.lmu.ifi.dbs.elki.algorithm.DistanceBasedAlgorithm<O,DoubleDistance,OutlierResult>
              de.lmu.ifi.dbs.elki.algorithm.outlier.KNNOutlier<O,D>

Type Parameters:

O - the type of DatabaseObjects handled by this Algorithm

D - the type of Distance used by this Algorithm

All Implemented Interfaces:

Algorithm<O,OutlierResult>, Parameterizable

@Title(value="KNN outlier: Efficient Algorithms for Mining Outliers from Large Data Sets")
@Description(value="Outlier Detection based on the distance of an object to its k nearest neighbor.")
@Reference(authors="S. Ramaswamy, R. Rastogi, K. Shim",
           title="Efficient Algorithms for Mining Outliers from Large Data Sets",
           booktitle="Proc. of the Int. Conf. on Management of Data, Dallas, Texas, 2000",
           url="http://dx.doi.org/10.1145/342009.335437")
public class KNNOutlier<O extends DatabaseObject,D extends DoubleDistance>
extends DistanceBasedAlgorithm<O,DoubleDistance,OutlierResult>

Outlier Detection based on the distance of an object to its k nearest neighbor.

Reference:
S. Ramaswamy, R. Rastogi, K. Shim: Efficient Algorithms for Mining Outliers from Large Data Sets.
In: Proc. of the Int. Conf. on Management of Data, Dallas, Texas, 2000.

Author:

Lisa Reichert

Field Summary

private int	k Holds the value of K_PARAM.
static OptionID	K_ID OptionID for K_PARAM
private IntParameter	K_PARAM Parameter to specify the k nearest neighbor, Key: -knno.k
static AssociationID<Double>	KNNO_KNNDISTANCE The association id to associate the KNNO_KNNDISTANCE of an object for the KNN outlier detection algorithm.

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

DISTANCE_FUNCTION_ID, DISTANCE_FUNCTION_PARAM

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

debug, logger

Constructor Summary

KNNOutlier(Parameterization config)
Constructor, adhering to Parameterizable

Method Summary

protected OutlierResult

runInTime(Database<O> database) Runs the algorithm in the timed evaluation part.

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

getDistanceFactory, getDistanceFunction

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

KNNO_KNNDISTANCE

public static final AssociationID<Double> KNNO_KNNDISTANCE

The association id to associate the KNNO_KNNDISTANCE of an object for the KNN outlier detection algorithm.

K_ID

public static final OptionID K_ID

OptionID for K_PARAM

K_PARAM

private final IntParameter K_PARAM

Parameter to specify the k nearest neighbor,

Key: -knno.k

k

private int k

Holds the value of K_PARAM.

Constructor Detail

KNNOutlier

public KNNOutlier(Parameterization config)

Constructor, adhering to Parameterizable

Parameters:

config - Parameterization

Method Detail

runInTime

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

Runs the algorithm in the timed evaluation part.

Specified by:

runInTime in class AbstractAlgorithm<O extends DatabaseObject,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).