| Package | Description | 
|---|---|
| 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 Lp norms such as the popular Euclidean and
 Manhattan distances. 
 | 
| Modifier and Type | Field and Description | 
|---|---|
protected LPNormDistanceFunction | 
HilOut.Parameterizer.distfunc
LPNorm distance function 
 | 
| Constructor and Description | 
|---|
HilOut(LPNormDistanceFunction distfunc,
      int k,
      int n,
      int h,
      java.lang.Enum<HilOut.ScoreType> tn)
Constructor. 
 | 
| Modifier and Type | Class and Description | 
|---|---|
class  | 
EuclideanDistanceFunction
Euclidean distance for  
NumberVectors. | 
class  | 
LPIntegerNormDistanceFunction
Lp-Norm for  
NumberVectors, optimized version for integer
 values of p. | 
class  | 
ManhattanDistanceFunction
Manhattan distance for  
NumberVectors. | 
class  | 
MaximumDistanceFunction
Maximum distance for  
NumberVectors. | 
class  | 
WeightedEuclideanDistanceFunction
Weighted Euclidean distance for  
NumberVectors. | 
class  | 
WeightedLPNormDistanceFunction
Weighted version of the Minkowski Lp norm distance for
  
NumberVector. | 
class  | 
WeightedManhattanDistanceFunction
Weighted version of the Manhattan (L1) metric. 
 | 
class  | 
WeightedMaximumDistanceFunction
Weighted version of the maximum distance function for
  
NumberVectors. | 
| Modifier and Type | Method and Description | 
|---|---|
protected LPNormDistanceFunction | 
LPNormDistanceFunction.Parameterizer.makeInstance()  | 
Copyright © 2019 ELKI Development Team. License information.