Uses of Interface
elki.distance.NumberVectorDistance

Packages that use NumberVectorDistance

Package	Description
elki.algorithm	Miscellaneous algorithms.
elki.algorithm.statistics	Statistical analysis algorithms.
elki.clustering	Clustering algorithms.
elki.clustering.kmeans	K-means clustering and variations.
elki.clustering.kmeans.initialization	Initialization strategies for k-means.
elki.clustering.kmeans.parallel	Parallelized implementations of k-means.
elki.clustering.kmeans.quality	Quality measures for k-Means results.
elki.clustering.kmedoids.initialization
elki.clustering.uncertain	Clustering algorithms for uncertain data.
elki.distance	Distance functions for use within ELKI.
elki.distance.colorhistogram	Distance functions for color histograms.
elki.distance.correlation	Distance functions using correlations.
elki.distance.geo	Geographic (earth) distance functions.
elki.distance.histogram	Distance functions for one-dimensional histograms.
elki.distance.minkowski	Minkowski space Lp norms such as the popular Euclidean and Manhattan distances.
elki.distance.probabilistic	Distance from probability theory, mostly divergences such as K-L-divergence, J-divergence, F-divergence, χ²-divergence, etc.
elki.distance.set	Distance functions for binary and set type data.
elki.distance.subspace	Distance functions based on subspaces.
elki.distance.timeseries	Distance functions designed for time series.
elki.evaluation.clustering.internal	Internal evaluation measures for clusterings.
elki.outlier.clustering	Clustering based outlier detection.
elki.outlier.distance	Distance-based outlier detection algorithms, such as DBOutlier and kNN.
elki.outlier.lof	LOF family of outlier detection algorithms.
elki.similarity	Similarity functions.
tutorial.clustering	Classes from the tutorial on implementing a custom k-means variation.
tutorial.distancefunction	Classes from the tutorial on implementing distance functions.

Uses of NumberVectorDistance in elki.algorithm

Fields in elki.algorithm declared as NumberVectorDistance

Modifier and Type	Field	Description
private NumberVectorDistance<? super V>	DependencyDerivator.distance	Distance function used.
protected NumberVectorDistance<? super V>	DependencyDerivator.Par.distance	The distance function to use.

Constructors in elki.algorithm with parameters of type NumberVectorDistance

Constructor	Description
DependencyDerivator(NumberVectorDistance<? super V> distance, java.text.NumberFormat nf, PCARunner pca, EigenPairFilter filter, int sampleSize, boolean randomsample)	Constructor.

Uses of NumberVectorDistance in elki.algorithm.statistics

Fields in elki.algorithm.statistics declared as NumberVectorDistance

Modifier and Type	Field	Description
protected NumberVectorDistance<? super NumberVector>	HopkinsStatisticClusteringTendency.distance	Distance function used.
protected NumberVectorDistance<? super NumberVector>	HopkinsStatisticClusteringTendency.Par.distance	The distance function to use.

Constructors in elki.algorithm.statistics with parameters of type NumberVectorDistance

Constructor	Description
HopkinsStatisticClusteringTendency(NumberVectorDistance<? super NumberVector> distance, int samplesize, RandomFactory random, int rep, int k, double[] minima, double[] maxima)	Constructor.

Uses of NumberVectorDistance in elki.clustering

Fields in elki.clustering declared as NumberVectorDistance

Modifier and Type	Field	Description
protected NumberVectorDistance<? super V>	NaiveMeanShiftClustering.distance	Distance function used.
protected NumberVectorDistance<? super V>	NaiveMeanShiftClustering.Par.distance	The distance function to use.

Constructors in elki.clustering with parameters of type NumberVectorDistance

Constructor	Description
NaiveMeanShiftClustering(NumberVectorDistance<? super V> distance, KernelDensityFunction kernel, double range)	Constructor.

Uses of NumberVectorDistance in elki.clustering.kmeans

Fields in elki.clustering.kmeans declared as NumberVectorDistance

Modifier and Type	Field	Description
private NumberVectorDistance<?>	AbstractKMeans.Instance.df	Distance function.
protected NumberVectorDistance<? super V>	AbstractKMeans.distance	Distance function used.
protected NumberVectorDistance<? super V>	AbstractKMeans.Par.distance	The distance function to use.

Methods in elki.clustering.kmeans that return NumberVectorDistance

Modifier and Type	Method	Description
NumberVectorDistance<? super V>	AbstractKMeans.getDistance()
NumberVectorDistance<? super V>	BestOfMultipleKMeans.getDistance()
NumberVectorDistance<? super V>	BisectingKMeans.getDistance()
NumberVectorDistance<? super V>	KMeans.getDistance()	Returns the distance.

Methods in elki.clustering.kmeans with parameters of type NumberVectorDistance

Modifier and Type	Method	Description
void	AbstractKMeans.setDistance(NumberVectorDistance<? super V> distance)
void	BestOfMultipleKMeans.setDistance(NumberVectorDistance<? super V> distance)
void	BisectingKMeans.setDistance(NumberVectorDistance<? super V> distance)
void	KMeans.setDistance(NumberVectorDistance<? super V> distance)	Set the distance function to use.

Constructors in elki.clustering.kmeans with parameters of type NumberVectorDistance

Constructor	Description
AbstractKMeans(NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer)	Constructor.
AnnulusKMeans(NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer, boolean varstat)	Constructor.
CompareMeans(NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer)	Constructor.
ElkanKMeans(NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer, boolean varstat)	Constructor.
ExponionKMeans(NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer, boolean varstat)	Constructor.
GMeans(NumberVectorDistance<? super V> distance, double critical, int k_min, int k_max, int maxiter, KMeans<V,M> innerKMeans, KMeansInitialization initializer, RandomFactory random)	Constructor.
HamerlyKMeans(NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer, boolean varstat)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> df, double[][] means, int t)	Constructor.
KDTreeFilteringKMeans(NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer, KDTreePruningKMeans.Split split, int leafsize)	Constructor.
KDTreePruningKMeans(NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer, KDTreePruningKMeans.Split split, int leafsize)	Constructor.
KMeansMinusMinus(NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer, double rate, boolean noiseFlag)	Constructor.
KMediansLloyd(NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer)	Constructor.
LloydKMeans(NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer)	Constructor.
MacQueenKMeans(NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer)	Constructor.
ShallotKMeans(NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer, boolean varstat)	Constructor.
SimplifiedElkanKMeans(NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer, boolean varstat)	Constructor.
SingleAssignmentKMeans(NumberVectorDistance<? super V> distance, int k, KMeansInitialization initializer)	Constructor.
SortMeans(NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer)	Constructor.
XMeans(NumberVectorDistance<? super V> distance, int k_min, int k_max, int maxiter, KMeans<V,M> innerKMeans, KMeansInitialization initializer, KMeansQualityMeasure<V> informationCriterion, RandomFactory random)	Constructor.

Uses of NumberVectorDistance in elki.clustering.kmeans.initialization

Fields in elki.clustering.kmeans.initialization declared as NumberVectorDistance

Modifier and Type	Field	Description
protected NumberVectorDistance<?>	KMC2.Instance.distance	Distance function
protected NumberVectorDistance<?>	KMeansPlusPlus.NumberVectorInstance.distance	Distance function

Methods in elki.clustering.kmeans.initialization with parameters of type NumberVectorDistance

Modifier and Type	Method	Description
double[][]	AFKMC2.chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]	FarthestPoints.chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]	FarthestSumPoints.chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]	FirstK.chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]	KMC2.chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]	KMeansInitialization.chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)	Choose initial means
double[][]	KMeansPlusPlus.chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]	Ostrovsky.chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]	Predefined.chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]	RandomlyChosen.chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]	RandomNormalGenerated.chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]	RandomUniformGenerated.chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]	SampleKMeans.chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]	SphericalAFKMC2.chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]	SphericalKMeansPlusPlus.chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)

Constructors in elki.clustering.kmeans.initialization with parameters of type NumberVectorDistance

Constructor	Description
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> distance, int m, RandomFactory rnd)	Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> distance, int m, RandomFactory rnd)	Constructor.
NumberVectorInstance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> distance, RandomFactory rnd)	Constructor.
NumberVectorInstance(Relation<? extends NumberVector> relation, NumberVectorDistance<?> distance, RandomFactory rnd)	Constructor.

Uses of NumberVectorDistance in elki.clustering.kmeans.parallel

Fields in elki.clustering.kmeans.parallel declared as NumberVectorDistance

Modifier and Type	Field	Description
(package private) NumberVectorDistance<? super V>	KMeansProcessor.distance	Distance function.
private NumberVectorDistance<? super V>	KMeansProcessor.Instance.distance	Distance function.

Constructors in elki.clustering.kmeans.parallel with parameters of type NumberVectorDistance

Constructor	Description
Instance(Relation<V> relation, NumberVectorDistance<? super V> distance, WritableIntegerDataStore assignment, double[][] means)	Constructor.
KMeansProcessor(Relation<V> relation, NumberVectorDistance<? super V> distance, WritableIntegerDataStore assignment, double[] varsum)	Constructor.
ParallelLloydKMeans(NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer)	Constructor.

Uses of NumberVectorDistance in elki.clustering.kmeans.quality

Methods in elki.clustering.kmeans.quality with parameters of type NumberVectorDistance

Modifier and Type	Method	Description
static double	AbstractKMeansQualityMeasure.logLikelihood(Relation<? extends NumberVector> relation, Clustering<? extends MeanModel> clustering, NumberVectorDistance<?> distance)	Computes log likelihood of an entire clustering.
static double	BayesianInformationCriterionXMeans.logLikelihoodXMeans(Relation<? extends NumberVector> relation, Clustering<? extends MeanModel> clustering, NumberVectorDistance<?> distance)	Computes log likelihood of an entire clustering.
static double	BayesianInformationCriterionZhao.logLikelihoodZhao(Relation<? extends NumberVector> relation, Clustering<? extends MeanModel> clustering, NumberVectorDistance<?> distance)	Computes log likelihood of an entire clustering.
<V extends NumberVector> double	AkaikeInformationCriterion.quality(Clustering<? extends MeanModel> clustering, NumberVectorDistance<? super V> distance, Relation<V> relation)
<V extends NumberVector> double	AkaikeInformationCriterionXMeans.quality(Clustering<? extends MeanModel> clustering, NumberVectorDistance<? super V> distance, Relation<V> relation)
<V extends NumberVector> double	BayesianInformationCriterion.quality(Clustering<? extends MeanModel> clustering, NumberVectorDistance<? super V> distance, Relation<V> relation)
<V extends NumberVector> double	BayesianInformationCriterionXMeans.quality(Clustering<? extends MeanModel> clustering, NumberVectorDistance<? super V> distance, Relation<V> relation)
<V extends NumberVector> double	BayesianInformationCriterionZhao.quality(Clustering<? extends MeanModel> clustering, NumberVectorDistance<? super V> distance, Relation<V> relation)
<V extends O> double	KMeansQualityMeasure.quality(Clustering<? extends MeanModel> clustering, NumberVectorDistance<? super V> distance, Relation<V> relation)	Calculates and returns the quality measure.
<V extends NumberVector> double	WithinClusterMeanDistance.quality(Clustering<? extends MeanModel> clustering, NumberVectorDistance<? super V> distance, Relation<V> relation)
<V extends NumberVector> double	WithinClusterVariance.quality(Clustering<? extends MeanModel> clustering, NumberVectorDistance<? super V> distance, Relation<V> relation)
static double	AbstractKMeansQualityMeasure.varianceContributionOfCluster(Cluster<? extends MeanModel> cluster, NumberVectorDistance<?> distance, Relation<? extends NumberVector> relation)	Variance contribution of a single cluster.

Uses of NumberVectorDistance in elki.clustering.kmedoids.initialization

Methods in elki.clustering.kmedoids.initialization with parameters of type NumberVectorDistance

Modifier and Type	Method	Description
double[][]	BUILD.chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]	LAB.chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)
double[][]	ParkJun.chooseInitialMeans(Relation<? extends NumberVector> relation, int k, NumberVectorDistance<?> distance)

Uses of NumberVectorDistance in elki.clustering.uncertain

Constructors in elki.clustering.uncertain with parameters of type NumberVectorDistance

Constructor	Description
CKMeans(NumberVectorDistance<? super NumberVector> distance, int k, int maxiter, KMeansInitialization initializer)	Constructor that uses Lloyd's k-means algorithm.

Uses of NumberVectorDistance in elki.distance

Subinterfaces of NumberVectorDistance in elki.distance

Modifier and Type	Interface	Description
interface	WeightedNumberVectorDistance<V>	Distance functions where each dimension is assigned a weight.

Classes in elki.distance that implement NumberVectorDistance

Modifier and Type	Class	Description
class	AbstractNumberVectorDistance	Abstract base class for the most common family of distance functions: defined on number vectors and returning double values.
class	ArcCosineDistance	Arcus cosine distance function for feature vectors.
class	ArcCosineUnitlengthDistance	Arcus cosine distance function for feature vectors.
class	BrayCurtisDistance	Bray-Curtis distance function / Sørensen–Dice coefficient for continuous vector spaces (not only binary data).
class	CanberraDistance	Canberra distance function, a variation of Manhattan distance.
class	ClarkDistance	Clark distance function for vector spaces.
class	CosineDistance	Cosine distance function for feature vectors.
class	CosineUnitlengthDistance	Cosine distance function for unit length feature vectors.
class	MahalanobisDistance	Mahalanobis quadratic form distance for feature vectors.
class	MatrixWeightedQuadraticDistance	Matrix weighted quadratic distance, the squared form of MahalanobisDistance.
class	SqrtCosineDistance	Cosine distance function for feature vectors using the square root.
class	SqrtCosineUnitlengthDistance	Cosine distance function for unit length feature vectors using the square root.
class	WeightedCanberraDistance	Weighted Canberra distance function, a variation of Manhattan distance.

Uses of NumberVectorDistance in elki.distance.colorhistogram

Classes in elki.distance.colorhistogram that implement NumberVectorDistance

Modifier and Type	Class	Description
class	HistogramIntersectionDistance	Intersection distance for color histograms.
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 NumberVectorDistance in elki.distance.correlation

Classes in elki.distance.correlation that implement NumberVectorDistance

Modifier and Type	Class	Description
class	AbsolutePearsonCorrelationDistance	Absolute Pearson correlation distance function for feature vectors.
class	AbsoluteUncenteredCorrelationDistance	Absolute uncentered correlation distance function for feature vectors.
class	PearsonCorrelationDistance	Pearson correlation distance function for feature vectors.
class	SquaredPearsonCorrelationDistance	Squared Pearson correlation distance function for feature vectors.
class	SquaredUncenteredCorrelationDistance	Squared uncentered correlation distance function for feature vectors.
class	UncenteredCorrelationDistance	Uncentered correlation distance.
class	WeightedPearsonCorrelationDistance	Pearson correlation distance function for feature vectors.
class	WeightedSquaredPearsonCorrelationDistance	Weighted squared Pearson correlation distance function for feature vectors.

Uses of NumberVectorDistance in elki.distance.geo

Classes in elki.distance.geo that implement NumberVectorDistance

Modifier and Type	Class	Description
class	DimensionSelectingLatLngDistance	Distance function for 2D vectors in Latitude, Longitude form.
class	LatLngDistance	Distance function for 2D vectors in Latitude, Longitude form.
class	LngLatDistance	Distance function for 2D vectors in Longitude, Latitude form.

Uses of NumberVectorDistance in elki.distance.histogram

Classes in elki.distance.histogram that implement NumberVectorDistance

Modifier and Type	Class	Description
class	HistogramMatchDistance	Distance function based on histogram matching, i.e., Manhattan distance on the cumulative density function.
class	KolmogorovSmirnovDistance	Distance function based on the Kolmogorov-Smirnov goodness of fit test.

Uses of NumberVectorDistance in elki.distance.minkowski

Classes in elki.distance.minkowski that implement NumberVectorDistance

Modifier and Type	Class	Description
class	EuclideanDistance	Euclidean distance for NumberVectors.
class	LPIntegerNormDistance	Lp-Norm for NumberVectors, optimized version for integer values of p.
class	LPNormDistance	Lp-Norm (Minkowski norms) are a family of distances for NumberVectors.
class	ManhattanDistance	Manhattan distance for NumberVectors.
class	MaximumDistance	Maximum distance for NumberVectors.
class	MinimumDistance	Minimum distance for NumberVectors.
class	SquaredEuclideanDistance	Squared Euclidean distance, optimized for SparseNumberVectors.
class	WeightedEuclideanDistance	Weighted Euclidean distance for NumberVectors.
class	WeightedLPNormDistance	Weighted version of the Minkowski Lp norm distance for NumberVector.
class	WeightedManhattanDistance	Weighted version of the Manhattan (L1) metric.
class	WeightedMaximumDistance	Weighted version of the maximum distance function for NumberVectors.
class	WeightedSquaredEuclideanDistance	Weighted squared Euclidean distance for NumberVectors.

Uses of NumberVectorDistance in elki.distance.probabilistic

Classes in elki.distance.probabilistic that implement NumberVectorDistance

Modifier and Type	Class	Description
class	ChiDistance	χ distance function, symmetric version.
class	ChiSquaredDistance	χ² distance function, symmetric version.
class	FisherRaoDistance	Fisher-Rao riemannian metric for (discrete) probability distributions.
class	HellingerDistance	Hellinger metric / affinity / kernel, Bhattacharyya coefficient, fidelity similarity, Matusita distance, Hellinger-Kakutani metric on a probability distribution.
class	JeffreyDivergenceDistance	Jeffrey Divergence for NumberVectors is a symmetric, smoothened version of the KullbackLeiblerDivergenceAsymmetricDistance.
class	JensenShannonDivergenceDistance	Jensen-Shannon Divergence for NumberVectors is a symmetric, smoothened version of the KullbackLeiblerDivergenceAsymmetricDistance.
class	KullbackLeiblerDivergenceAsymmetricDistance	Kullback-Leibler divergence, also known as relative entropy, information deviation, or just KL-distance (albeit asymmetric).
class	KullbackLeiblerDivergenceReverseAsymmetricDistance	Kullback-Leibler divergence, also known as relative entropy, information deviation or just KL-distance (albeit asymmetric).
class	SqrtJensenShannonDivergenceDistance	The square root of Jensen-Shannon divergence is a metric.
class	TriangularDiscriminationDistance	Triangular Discrimination has relatively tight upper and lower bounds to the Jensen-Shannon divergence, but is much less expensive.
class	TriangularDistance	Triangular Distance has relatively tight upper and lower bounds to the (square root of the) Jensen-Shannon divergence, but is much less expensive.

Uses of NumberVectorDistance in elki.distance.set

Classes in elki.distance.set that implement NumberVectorDistance

Modifier and Type	Class	Description
class	HammingDistance	Computes the Hamming distance of arbitrary vectors - i.e. counting, on how many places they differ.
class	JaccardSimilarityDistance	A flexible extension of Jaccard similarity to non-binary vectors.

Uses of NumberVectorDistance in elki.distance.subspace

Classes in elki.distance.subspace that implement NumberVectorDistance

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 between NumberVectors only in specified dimensions.
class	SubspaceLPNormDistance	Lp-Norm distance function between NumberVectors only in specified dimensions.
class	SubspaceManhattanDistance	Manhattan distance function between NumberVectors only in specified dimensions.
class	SubspaceMaximumDistance	Maximum distance function between NumberVectors only in specified dimensions.

Uses of NumberVectorDistance in elki.distance.timeseries

Classes in elki.distance.timeseries that implement NumberVectorDistance

Modifier and Type	Class	Description
class	AbstractEditDistance	Edit Distance for FeatureVectors.
class	DerivativeDTWDistance	Derivative Dynamic Time Warping distance for numerical vectors.
class	DTWDistance	Dynamic Time Warping distance (DTW) for numerical vectors.
class	EDRDistance	Edit Distance on Real Sequence distance for numerical vectors.
class	ERPDistance	Edit Distance With Real Penalty distance for numerical vectors.
class	LCSSDistance	Longest Common Subsequence distance for numerical vectors.

Uses of NumberVectorDistance in elki.evaluation.clustering.internal

Fields in elki.evaluation.clustering.internal declared as NumberVectorDistance

Modifier and Type	Field	Description
private NumberVectorDistance<?>	ClusterRadius.distance	Distance function to use.
private NumberVectorDistance<?>	ClusterRadius.Par.distance	Distance function to use.
private NumberVectorDistance<?>	DaviesBouldinIndex.distance	Distance function to use.
private NumberVectorDistance<?>	DaviesBouldinIndex.Par.distance	Distance function to use.
private NumberVectorDistance<?>	PBMIndex.distance	Distance function to use.
private NumberVectorDistance<?>	PBMIndex.Par.distance	Distance function to use.
private NumberVectorDistance<?>	SimplifiedSilhouette.distance	Distance function to use.
private NumberVectorDistance<?>	SimplifiedSilhouette.Par.distance	Distance function to use.
private NumberVectorDistance<?>	SquaredErrors.distance	Distance function to use.
private NumberVectorDistance<?>	SquaredErrors.Par.distance	Distance function to use.

Constructors in elki.evaluation.clustering.internal with parameters of type NumberVectorDistance

Constructor	Description
ClusterRadius(NumberVectorDistance<?> distance, NoiseHandling noiseOption)	Constructor.
DaviesBouldinIndex(NumberVectorDistance<?> distance, NoiseHandling noiseOpt, double p)	Constructor.
PBMIndex(NumberVectorDistance<?> distance, NoiseHandling noiseOpt)	Constructor.
SimplifiedSilhouette(NumberVectorDistance<?> distance, NoiseHandling noiseOpt, boolean penalize)	Constructor.
SquaredErrors(NumberVectorDistance<?> distance, NoiseHandling noiseOption)	Constructor.

Uses of NumberVectorDistance in elki.outlier.clustering

Fields in elki.outlier.clustering declared as NumberVectorDistance

Modifier and Type	Field	Description
protected NumberVectorDistance<? super O>	CBLOF.distance	Distance function used.
protected NumberVectorDistance<? super O>	CBLOF.Par.distance	Distance function to use.

Methods in elki.outlier.clustering with parameters of type NumberVectorDistance

Modifier and Type	Method	Description
private double	CBLOF.computeLargeClusterCBLOF(O obj, NumberVectorDistance<? super O> distance, NumberVector clusterMean, Cluster<MeanModel> cluster)
private double	CBLOF.computeSmallClusterCBLOF(O obj, NumberVectorDistance<? super O> distance, java.util.List<NumberVector> largeClusterMeans, Cluster<MeanModel> cluster)
private void	KMeansOutlierDetection.distanceScoring(Clustering<?> c, Relation<O> relation, NumberVectorDistance<? super O> distfunc, WritableDoubleDataStore scores, DoubleMinMax mm)	Simple distance-based scoring function.
private void	KMeansOutlierDetection.singletonsScoring(Clustering<?> c, Relation<O> relation, NumberVectorDistance<? super O> distfunc, WritableDoubleDataStore scores, DoubleMinMax mm)	Distance-based scoring that takes singletons into account.
private void	KMeansOutlierDetection.varianceScoring(Clustering<?> c, Relation<O> relation, NumberVectorDistance<? super O> distfunc, WritableDoubleDataStore scores, DoubleMinMax mm)	Variance-based scoring function.

Constructors in elki.outlier.clustering with parameters of type NumberVectorDistance

Constructor	Description
CBLOF(NumberVectorDistance<? super O> distance, ClusteringAlgorithm<Clustering<MeanModel>> clusteringAlgorithm, double alpha, double beta)	Constructor.

Uses of NumberVectorDistance in elki.outlier.distance

Fields in elki.outlier.distance declared as NumberVectorDistance

Modifier and Type	Field	Description
protected NumberVectorDistance<? super NumberVector>	ReferenceBasedOutlierDetection.distance	Distance function used.
protected NumberVectorDistance<? super NumberVector>	ReferenceBasedOutlierDetection.Par.distance	The distance function to use.

Constructors in elki.outlier.distance with parameters of type NumberVectorDistance

Constructor	Description
ReferenceBasedOutlierDetection(int k, NumberVectorDistance<? super NumberVector> distance, ReferencePointsHeuristic refp)	Constructor with parameters.

Uses of NumberVectorDistance in elki.outlier.lof

Fields in elki.outlier.lof declared as NumberVectorDistance

Modifier and Type	Field	Description
private NumberVectorDistance<? super V>	ALOCI.distance	Distance function used.
protected NumberVectorDistance<? super O>	ALOCI.Par.distance	The distance function to use.

Constructors in elki.outlier.lof with parameters of type NumberVectorDistance

Constructor	Description
ALOCI(NumberVectorDistance<? super V> distance, int nmin, int alpha, int g, RandomFactory rnd)	Constructor.

Uses of NumberVectorDistance in elki.similarity

Classes in elki.similarity that implement NumberVectorDistance

Modifier and Type	Class	Description
class	Kulczynski1Similarity	Kulczynski similarity 1.

Uses of NumberVectorDistance in tutorial.clustering

Fields in tutorial.clustering declared as NumberVectorDistance

Modifier and Type	Field	Description
protected NumberVectorDistance<? super V>	SameSizeKMeans.Par.distance	Distance function

Methods in tutorial.clustering with parameters of type NumberVectorDistance

Modifier and Type	Method	Description
protected void	SameSizeKMeans.updateDistances(Relation<V> relation, double[][] means, WritableDataStore<SameSizeKMeans.Meta> metas, NumberVectorDistance<? super V> df)	Compute the distances of each object to all means.

Constructors in tutorial.clustering with parameters of type NumberVectorDistance

Constructor	Description
SameSizeKMeans(NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer)	Constructor.

Uses of NumberVectorDistance in tutorial.distancefunction

Classes in tutorial.distancefunction that implement NumberVectorDistance

Modifier and Type	Class	Description
class	MultiLPNorm	Tutorial example Minowski-distance variation with different exponents for different dimensions for ELKI.
class	TutorialDistance	Tutorial distance function example for ELKI.