Package | Description |
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de.lmu.ifi.dbs.elki.algorithm.outlier.distance |
Distance-based outlier detection algorithms, such as DBOutlier and kNN.
|
de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski |
Minkowski space L_p norms such as the popular Euclidean and Manhattan distances.
|
Modifier and Type | Field and Description |
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protected LPNormDistanceFunction |
HilOut.Parameterizer.distfunc
LPNorm distance function
|
Constructor and Description |
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HilOut(LPNormDistanceFunction distfunc,
int k,
int n,
int h,
Enum<HilOut.ScoreType> tn)
Constructor.
|
Modifier and Type | Class and Description |
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class |
EuclideanDistanceFunction
Euclidean distance for
NumberVector s. |
class |
LPIntegerNormDistanceFunction
LP-Norm for
NumberVector s, optimized version for integer values of p. |
class |
ManhattanDistanceFunction
Manhattan distance for
NumberVector s. |
class |
MaximumDistanceFunction
Maximum distance for
NumberVector s. |
class |
WeightedEuclideanDistanceFunction
Weighted Euclidean distance for
NumberVector s. |
class |
WeightedLPNormDistanceFunction
Weighted version of the Minkowski L_p norm distance for
NumberVector . |
class |
WeightedManhattanDistanceFunction
Weighted version of the Minkowski L_p metrics distance function for
NumberVector s. |
class |
WeightedMaximumDistanceFunction
Weighted version of the Minkowski L_p metrics distance function for
NumberVector s. |
Modifier and Type | Method and Description |
---|---|
protected LPNormDistanceFunction |
LPNormDistanceFunction.Parameterizer.makeInstance() |
Copyright © 2015 ELKI Development Team, Lehr- und Forschungseinheit für Datenbanksysteme, Ludwig-Maximilians-Universität München. License information.