Package elki.outlier.lof
Class LDOF<O>
- java.lang.Object
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- elki.outlier.lof.LDOF<O>
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- Type Parameters:
O- the type of objects handled by this algorithm
- All Implemented Interfaces:
elki.Algorithm,OutlierAlgorithm
@Title("LDOF: Local Distance-Based Outlier Factor") @Description("Local outlier detection appraoch suitable for scattered data by averaging the kNN distance over all k nearest neighbors") @Reference(authors="K. Zhang, M. Hutter, H. Jin", title="A New Local Distance-Based Outlier Detection Approach for Scattered Real-World Data", booktitle="Proc. 13th Pacific-Asia Conf. Adv. Knowledge Discovery and Data Mining (PAKDD 2009)", url="https://doi.org/10.1007/978-3-642-01307-2_84", bibkey="DBLP:conf/pakdd/ZhangHJ09") public class LDOF<O> extends java.lang.Object implements OutlierAlgorithmComputes the LDOF (Local Distance-Based Outlier Factor) for all objects of a Database.Reference:
K. Zhang, M. Hutter, H. Jin
A New Local Distance-Based Outlier Detection Approach for Scattered Real-World Data.
Proc. 13th Pacific-Asia Conf. Adv. Knowledge Discovery and Data Mining (PAKDD 2009)- Since:
- 0.3
- Author:
- Arthur Zimek
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Field Summary
Fields Modifier and Type Field Description protected elki.distance.Distance<? super O>distanceDistance function used.protected intkplusNumber of neighbors to query + query point itself.private static doubleLDOF_BASELINEThe baseline for LDOF values.private static elki.logging.LoggingLOGThe logger for this class.
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description elki.data.type.TypeInformation[]getInputTypeRestriction()OutlierResultrun(elki.database.relation.Relation<O> relation)Run the algorithm-
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
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Methods inherited from interface elki.outlier.OutlierAlgorithm
autorun
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Field Detail
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LOG
private static final elki.logging.Logging LOG
The logger for this class.
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LDOF_BASELINE
private static final double LDOF_BASELINE
The baseline for LDOF values. The paper gives 0.5 for uniform distributions, although one might also discuss using 1.0 as baseline.- See Also:
- Constant Field Values
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distance
protected elki.distance.Distance<? super O> distance
Distance function used.
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kplus
protected int kplus
Number of neighbors to query + query point itself.
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Constructor Detail
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LDOF
public LDOF(elki.distance.Distance<? super O> distance, int k)
Constructor.- Parameters:
distance- distance functionk- k Parameter
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Method Detail
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getInputTypeRestriction
public elki.data.type.TypeInformation[] getInputTypeRestriction()
- Specified by:
getInputTypeRestrictionin interfaceelki.Algorithm
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run
public OutlierResult run(elki.database.relation.Relation<O> relation)
Run the algorithm- Parameters:
relation- Relation to process- Returns:
- Outlier result
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