Environment for
DeveLoping
KDD-Applications
Supported by Index-Structures

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

java.lang.Object
  extended by de.lmu.ifi.dbs.elki.logging.AbstractLoggable
      extended by de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm<O,R>
          extended by de.lmu.ifi.dbs.elki.algorithm.DistanceBasedAlgorithm<O,DoubleDistance,OutlierResult>
              extended by de.lmu.ifi.dbs.elki.algorithm.outlier.KNNWeightOutlier<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="KNNWeight outlier detection")
@Description(value="Outlier Detection based on the distances of an object to its k nearest neighbors.")
@Reference(authors="F. Angiulli, C. Pizzuti",
           title="Fast Outlier Detection in High Dimensional Spaces",
           booktitle="Proc. European Conference on Principles of Knowledge Discovery and Data Mining (PKDD\'02), Helsinki, Finland, 2002",
           url="http://dx.doi.org/10.1007/3-540-45681-3_2")
public class KNNWeightOutlier<O extends DatabaseObject,D extends DoubleDistance>
extends DistanceBasedAlgorithm<O,DoubleDistance,OutlierResult>

Outlier Detection based on the accumulated distances of a point to its k nearest neighbors. Based on: F. Angiulli, C. Pizzuti: Fast Outlier Detection in High Dimensional Spaces. In: Proc. European Conference on Principles of Knowledge Discovery and Data Mining (PKDD'02), Helsinki, Finland, 2002.

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: -knnwod.k
static AssociationID<Double> KNNWOD_WEIGHT
          Association ID for the KNN Weight Outlier Detection
 
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
KNNWeightOutlier(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

K_ID

public static final OptionID K_ID
OptionID for K_PARAM


KNNWOD_WEIGHT

public static final AssociationID<Double> KNNWOD_WEIGHT
Association ID for the KNN Weight Outlier Detection


K_PARAM

private final IntParameter K_PARAM
Parameter to specify the k nearest neighbor,

Key: -knnwod.k


k

private int k
Holds the value of K_PARAM.

Constructor Detail

KNNWeightOutlier

public KNNWeightOutlier(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).

Release 0.3 (2010-03-31_1612)