Package  Description 

de.lmu.ifi.dbs.elki.datasource.filter.normalization.instancewise 
Instancewise normalization, where each instance is normalized independently.

de.lmu.ifi.dbs.elki.distance.distancefunction 
Distance functions for use within ELKI.

de.lmu.ifi.dbs.elki.distance.distancefunction.colorhistogram 
Distance functions using correlations

de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski 
Minkowski space L_{p} norms such as the popular Euclidean and
Manhattan distances.

de.lmu.ifi.dbs.elki.distance.distancefunction.subspace 
Distance functions based on subspaces

de.lmu.ifi.dbs.elki.index.tree.spatial.kd 
Kdtree and variants

Modifier and Type  Field and Description 

(package private) Norm<? super V> 
LengthNormalization.norm
Norm to use.

(package private) Norm<? super V> 
LengthNormalization.Parameterizer.norm
Norm to use.

Constructor and Description 

LengthNormalization(Norm<? super V> norm)
Constructor.

Modifier and Type  Class and Description 

class 
MahalanobisDistanceFunction
Mahalanobis quadratic form distance for feature vectors.

class 
MatrixWeightedQuadraticDistanceFunction
Matrix weighted quadratic distance, the squared form of
MahalanobisDistanceFunction . 
Modifier and Type  Class and Description 

class 
HSBHistogramQuadraticDistanceFunction
Distance function for HSB color histograms based on a quadratic form and
color similarity.

class 
RGBHistogramQuadraticDistanceFunction
Distance function for RGB color histograms based on a quadratic form and
color similarity.

Modifier and Type  Class and Description 

class 
EuclideanDistanceFunction
Euclidean distance for
NumberVector s. 
class 
LPIntegerNormDistanceFunction
L_{p}Norm for
NumberVector s, optimized version for integer
values of p. 
class 
LPNormDistanceFunction
L_{p}Norm (Minkowski norms) are a family of distances for
NumberVector s. 
class 
ManhattanDistanceFunction
Manhattan distance for
NumberVector s. 
class 
MaximumDistanceFunction
Maximum distance for
NumberVector s. 
class 
MinimumDistanceFunction
Minimum distance for
NumberVector s. 
class 
SparseEuclideanDistanceFunction
Euclidean distance function, optimized for
SparseNumberVector s. 
class 
SparseLPNormDistanceFunction
L_{p}Norm, optimized for
SparseNumberVector s. 
class 
SparseManhattanDistanceFunction
Manhattan distance, optimized for
SparseNumberVector s. 
class 
SparseMaximumDistanceFunction
Maximum distance, optimized for
SparseNumberVector s. 
class 
SparseSquaredEuclideanDistanceFunction
Squared Euclidean distance function, optimized for
SparseNumberVector s. 
class 
SquaredEuclideanDistanceFunction
Squared Euclidean distance, optimized for
SparseNumberVector 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 Manhattan (L_{1}) metric.

class 
WeightedMaximumDistanceFunction
Weighted version of the maximum distance function for
NumberVector s. 
class 
WeightedSquaredEuclideanDistanceFunction
Weighted squared Euclidean distance for
NumberVector s. 
Modifier and Type  Class and Description 

class 
OnedimensionalDistanceFunction
Distance function that computes the distance between feature vectors as the
absolute difference of their values in a specified dimension only.

class 
SubspaceEuclideanDistanceFunction
Euclidean distance function between
NumberVector s only in specified
dimensions. 
class 
SubspaceLPNormDistanceFunction
L_{p}Norm distance function between
NumberVector s only in
specified dimensions. 
class 
SubspaceManhattanDistanceFunction
Manhattan distance function between
NumberVector s only in specified
dimensions. 
class 
SubspaceMaximumDistanceFunction
Maximum distance function between
NumberVector s only in specified
dimensions. 
Modifier and Type  Field and Description 

private Norm<? super O> 
MinimalisticMemoryKDTree.KDTreeKNNQuery.norm
Norm to use.

private Norm<? super O> 
MinimalisticMemoryKDTree.KDTreeRangeQuery.norm
Norm to use.

private Norm<? super O> 
SmallMemoryKDTree.KDTreeKNNQuery.norm
Norm to use.

private Norm<? super O> 
SmallMemoryKDTree.KDTreeRangeQuery.norm
Norm to use.

Constructor and Description 

KDTreeKNNQuery(DistanceQuery<O> distanceQuery,
Norm<? super O> norm)
Constructor.

KDTreeKNNQuery(DistanceQuery<O> distanceQuery,
Norm<? super O> norm)
Constructor.

KDTreeRangeQuery(DistanceQuery<O> distanceQuery,
Norm<? super O> norm)
Constructor.

KDTreeRangeQuery(DistanceQuery<O> distanceQuery,
Norm<? super O> norm)
Constructor.

Copyright © 2019 ELKI Development Team. License information.