Uses of Interface
elki.distance.Norm
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Packages that use Norm Package Description elki.datasource.filter.normalization.instancewise Instancewise normalization, where each instance is normalized independently.elki.distance Distance functions for use within ELKI.elki.distance.colorhistogram Distance functions for color histograms.elki.distance.minkowski Minkowski space Lp norms such as the popular Euclidean and Manhattan distances.elki.distance.subspace Distance functions based on subspaces. -
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Uses of Norm in elki.datasource.filter.normalization.instancewise
Fields in elki.datasource.filter.normalization.instancewise declared as Norm Modifier and Type Field Description (package private) Norm<? super V>
LengthNormalization. norm
Norm to use.(package private) Norm<? super V>
LengthNormalization.Par. norm
Norm to use.Constructors in elki.datasource.filter.normalization.instancewise with parameters of type Norm Constructor Description LengthNormalization(Norm<? super V> norm)
Constructor. -
Uses of Norm in elki.distance
Classes in elki.distance that implement Norm Modifier and Type Class Description class
MahalanobisDistance
Mahalanobis quadratic form distance for feature vectors.class
MatrixWeightedQuadraticDistance
Matrix weighted quadratic distance, the squared form ofMahalanobisDistance
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Uses of Norm in elki.distance.colorhistogram
Classes in elki.distance.colorhistogram that implement Norm Modifier and Type Class Description class
HSBHistogramQuadraticDistance
Distance function for HSB color histograms based on a quadratic form and color similarity.class
RGBHistogramQuadraticDistance
Distance function for RGB color histograms based on a quadratic form and color similarity. -
Uses of Norm in elki.distance.minkowski
Classes in elki.distance.minkowski that implement Norm Modifier and Type Class Description class
EuclideanDistance
Euclidean distance forNumberVector
s.class
LPIntegerNormDistance
Lp-Norm forNumberVector
s, optimized version for integer values of p.class
LPNormDistance
Lp-Norm (Minkowski norms) are a family of distances forNumberVector
s.class
ManhattanDistance
Manhattan distance forNumberVector
s.class
MaximumDistance
Maximum distance forNumberVector
s.class
MinimumDistance
Minimum distance forNumberVector
s.class
SparseEuclideanDistance
Euclidean distance function, optimized forSparseNumberVector
s.class
SparseLPNormDistance
Lp-Norm, optimized forSparseNumberVector
s.class
SparseManhattanDistance
Manhattan distance, optimized forSparseNumberVector
s.class
SparseMaximumDistance
Maximum distance, optimized forSparseNumberVector
s.class
SparseSquaredEuclideanDistance
Squared Euclidean distance function, optimized forSparseNumberVector
s.class
SquaredEuclideanDistance
Squared Euclidean distance, optimized forSparseNumberVector
s.class
WeightedEuclideanDistance
Weighted Euclidean distance forNumberVector
s.class
WeightedLPNormDistance
Weighted version of the Minkowski Lp norm distance forNumberVector
.class
WeightedManhattanDistance
Weighted version of the Manhattan (L1) metric.class
WeightedMaximumDistance
Weighted version of the maximum distance function forNumberVector
s.class
WeightedSquaredEuclideanDistance
Weighted squared Euclidean distance forNumberVector
s. -
Uses of Norm in elki.distance.subspace
Classes in elki.distance.subspace that implement Norm Modifier and Type Class Description class
OnedimensionalDistance
Distance function that computes the distance between feature vectors as the absolute difference of their values in a specified dimension only.class
SubspaceEuclideanDistance
Euclidean distance function betweenNumberVector
s only in specified dimensions.class
SubspaceLPNormDistance
Lp-Norm distance function betweenNumberVector
s only in specified dimensions.class
SubspaceManhattanDistance
Manhattan distance function betweenNumberVector
s only in specified dimensions.class
SubspaceMaximumDistance
Maximum distance function betweenNumberVector
s only in specified dimensions.
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