Uses of Interface
de.lmu.ifi.dbs.elki.distance.distancefunction.NumberVectorDistanceFunction

Packages that use NumberVectorDistanceFunction

Package	Description
de.lmu.ifi.dbs.elki.algorithm	Algorithms suitable as a task for the KDDTask main routine.
de.lmu.ifi.dbs.elki.algorithm.clustering.em	Expectation-Maximization clustering algorithm.
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans	K-means clustering and variations
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization	Initialization strategies for k-means.
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.parallel	Parallelized implementations of k-means.
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.quality	Quality measures for k-Means results.
de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain	Clustering algorithms for uncertain data.
de.lmu.ifi.dbs.elki.algorithm.outlier.clustering	Clustering based outlier detection.
de.lmu.ifi.dbs.elki.algorithm.outlier.distance	Distance-based outlier detection algorithms, such as DBOutlier and kNN.
de.lmu.ifi.dbs.elki.algorithm.outlier.lof	LOF family of outlier detection algorithms
de.lmu.ifi.dbs.elki.algorithm.statistics	Statistical analysis algorithms.
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.correlation	Distance functions using correlations
de.lmu.ifi.dbs.elki.distance.distancefunction.geo	Geographic (earth) distance functions
de.lmu.ifi.dbs.elki.distance.distancefunction.histogram	Distance functions for one-dimensional histograms.
de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski	Minkowski space Lp norms such as the popular Euclidean and Manhattan distances.
de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic	Distance from probability theory, mostly divergences such as K-L-divergence, J-divergence, F-divergence, χ²-divergence, etc.
de.lmu.ifi.dbs.elki.distance.distancefunction.set	Distance functions for binary and set type data.
de.lmu.ifi.dbs.elki.distance.distancefunction.subspace	Distance functions based on subspaces
de.lmu.ifi.dbs.elki.distance.distancefunction.timeseries	Distance functions designed for time series Note that some regular distance functions (e.g., Euclidean) are also used on time series.
de.lmu.ifi.dbs.elki.distance.similarityfunction	Similarity functions
de.lmu.ifi.dbs.elki.evaluation.clustering.internal	Internal evaluation measures for clusterings.
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 NumberVectorDistanceFunction in de.lmu.ifi.dbs.elki.algorithm

Fields in de.lmu.ifi.dbs.elki.algorithm declared as NumberVectorDistanceFunction

Modifier and Type	Field and Description
protected NumberVectorDistanceFunction<? super O>	AbstractNumberVectorDistanceBasedAlgorithm.distanceFunction Holds the instance of the distance function specified by DistanceBasedAlgorithm.DISTANCE_FUNCTION_ID.
protected NumberVectorDistanceFunction<? super O>	AbstractNumberVectorDistanceBasedAlgorithm.Parameterizer.distanceFunction Distance function to use.

Methods in de.lmu.ifi.dbs.elki.algorithm that return NumberVectorDistanceFunction

Modifier and Type	Method and Description
NumberVectorDistanceFunction<? super O>	AbstractNumberVectorDistanceBasedAlgorithm.getDistanceFunction() Returns the distanceFunction.

Constructors in de.lmu.ifi.dbs.elki.algorithm with parameters of type NumberVectorDistanceFunction

Constructor and Description
AbstractNumberVectorDistanceBasedAlgorithm(NumberVectorDistanceFunction<? super O> distanceFunction) Constructor.
DependencyDerivator(NumberVectorDistanceFunction<? super V> distanceFunction, java.text.NumberFormat nf, PCARunner pca, EigenPairFilter filter, int sampleSize, boolean randomsample) Constructor.

Uses of NumberVectorDistanceFunction in de.lmu.ifi.dbs.elki.algorithm.clustering.em

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.em with parameters of type NumberVectorDistanceFunction

Modifier and Type	Method and Description
java.util.List<TextbookMultivariateGaussianModel>	TextbookMultivariateGaussianModelFactory.buildInitialModels(Database database, Relation<V> relation, int k, NumberVectorDistanceFunction<? super V> df)
java.util.List<DiagonalGaussianModel>	DiagonalGaussianModelFactory.buildInitialModels(Database database, Relation<V> relation, int k, NumberVectorDistanceFunction<? super V> df)
java.util.List<SphericalGaussianModel>	SphericalGaussianModelFactory.buildInitialModels(Database database, Relation<V> relation, int k, NumberVectorDistanceFunction<? super V> df)
java.util.List<MultivariateGaussianModel>	MultivariateGaussianModelFactory.buildInitialModels(Database database, Relation<V> relation, int k, NumberVectorDistanceFunction<? super V> df)
java.util.List<? extends EMClusterModel<M>>	EMClusterModelFactory.buildInitialModels(Database database, Relation<V> relation, int k, NumberVectorDistanceFunction<? super V> df) Build the initial models
java.util.List<TwoPassMultivariateGaussianModel>	TwoPassMultivariateGaussianModelFactory.buildInitialModels(Database database, Relation<V> relation, int k, NumberVectorDistanceFunction<? super V> df)

Uses of NumberVectorDistanceFunction in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans

Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans declared as NumberVectorDistanceFunction

Modifier and Type	Field and Description
private NumberVectorDistanceFunction<?>	AbstractKMeans.Instance.df Distance function.

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans with parameters of type NumberVectorDistanceFunction

Modifier and Type	Method and Description
void	BestOfMultipleKMeans.setDistanceFunction(NumberVectorDistanceFunction<? super V> distanceFunction)
void	KMeans.setDistanceFunction(NumberVectorDistanceFunction<? super V> distanceFunction) Set the distance function to use.
void	AbstractKMeans.setDistanceFunction(NumberVectorDistanceFunction<? super V> distanceFunction)
void	KMeansBisecting.setDistanceFunction(NumberVectorDistanceFunction<? super V> distanceFunction)

Constructors in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans with parameters of type NumberVectorDistanceFunction

Constructor and Description
AbstractKMeans(NumberVectorDistanceFunction<? super V> distanceFunction, int k, int maxiter, KMeansInitialization initializer) Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistanceFunction<?> df, double[][] means) Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistanceFunction<?> df, double[][] means) Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistanceFunction<?> df, double[][] means) Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistanceFunction<?> df, double[][] means)
Instance(Relation<? extends NumberVector> relation, NumberVectorDistanceFunction<?> df, double[][] means)
Instance(Relation<? extends NumberVector> relation, NumberVectorDistanceFunction<?> df, double[][] means) Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistanceFunction<?> df, double[][] means) Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistanceFunction<?> df, double[][] means) Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistanceFunction<?> df, double[][] means) Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistanceFunction<?> df, double[][] means) Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistanceFunction<?> df, double[][] means) Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistanceFunction<?> df, double[][] means) Constructor.
Instance(Relation<? extends NumberVector> relation, NumberVectorDistanceFunction<?> df, double[][] means) Constructor.
KMeansAnnulus(NumberVectorDistanceFunction<? super V> distanceFunction, int k, int maxiter, KMeansInitialization initializer, boolean varstat) Constructor.
KMeansCompare(NumberVectorDistanceFunction<? super V> distanceFunction, int k, int maxiter, KMeansInitialization initializer) Constructor.
KMeansElkan(NumberVectorDistanceFunction<? super V> distanceFunction, int k, int maxiter, KMeansInitialization initializer, boolean varstat) Constructor.
KMeansExponion(NumberVectorDistanceFunction<? super V> distanceFunction, int k, int maxiter, KMeansInitialization initializer, boolean varstat) Constructor.
KMeansHamerly(NumberVectorDistanceFunction<? super V> distanceFunction, int k, int maxiter, KMeansInitialization initializer, boolean varstat) Constructor.
KMeansLloyd(NumberVectorDistanceFunction<? super V> distanceFunction, int k, int maxiter, KMeansInitialization initializer) Constructor.
KMeansMacQueen(NumberVectorDistanceFunction<? super V> distanceFunction, int k, int maxiter, KMeansInitialization initializer) Constructor.
KMeansMinusMinus(NumberVectorDistanceFunction<? super V> distanceFunction, int k, int maxiter, KMeansInitialization initializer, double rate, boolean noiseFlag) Constructor.
KMeansSimplifiedElkan(NumberVectorDistanceFunction<? super V> distanceFunction, int k, int maxiter, KMeansInitialization initializer, boolean varstat) Constructor.
KMeansSort(NumberVectorDistanceFunction<? super V> distanceFunction, int k, int maxiter, KMeansInitialization initializer) Constructor.
KMediansLloyd(NumberVectorDistanceFunction<? super V> distanceFunction, int k, int maxiter, KMeansInitialization initializer) Constructor.
SingleAssignmentKMeans(NumberVectorDistanceFunction<? super V> distanceFunction, int k, KMeansInitialization initializer) Constructor.
XMeans(NumberVectorDistanceFunction<? super V> distanceFunction, int k_min, int k_max, int maxiter, KMeans<V,M> innerKMeans, KMeansInitialization initializer, KMeansQualityMeasure<V> informationCriterion, RandomFactory random) Constructor.

Uses of NumberVectorDistanceFunction in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization with parameters of type NumberVectorDistanceFunction

Modifier and Type	Method and Description
double[][]	PAMInitialMeans.chooseInitialMeans(Database database, Relation<? extends NumberVector> relation, int k, NumberVectorDistanceFunction<?> distanceFunction)
double[][]	FirstKInitialMeans.chooseInitialMeans(Database database, Relation<? extends NumberVector> relation, int k, NumberVectorDistanceFunction<?> distanceFunction)
double[][]	RandomUniformGeneratedInitialMeans.chooseInitialMeans(Database database, Relation<? extends NumberVector> relation, int k, NumberVectorDistanceFunction<?> distanceFunction)
double[][]	FarthestPointsInitialMeans.chooseInitialMeans(Database database, Relation<? extends NumberVector> relation, int k, NumberVectorDistanceFunction<?> distanceFunction)
double[][]	KMeansInitialization.chooseInitialMeans(Database database, Relation<? extends NumberVector> relation, int k, NumberVectorDistanceFunction<?> distanceFunction) Choose initial means
double[][]	ParkInitialMeans.chooseInitialMeans(Database database, Relation<? extends NumberVector> relation, int k, NumberVectorDistanceFunction<?> distanceFunction)
double[][]	FarthestSumPointsInitialMeans.chooseInitialMeans(Database database, Relation<? extends NumberVector> relation, int k, NumberVectorDistanceFunction<?> distanceFunction)
double[][]	PredefinedInitialMeans.chooseInitialMeans(Database database, Relation<? extends NumberVector> relation, int k, NumberVectorDistanceFunction<?> distanceFunction)
double[][]	LABInitialMeans.chooseInitialMeans(Database database, Relation<? extends NumberVector> relation, int k, NumberVectorDistanceFunction<?> distanceFunction)
double[][]	OstrovskyInitialMeans.chooseInitialMeans(Database database, Relation<? extends NumberVector> relation, int k, NumberVectorDistanceFunction<?> distanceFunction)
double[][]	KMeansPlusPlusInitialMeans.chooseInitialMeans(Database database, Relation<? extends NumberVector> relation, int k, NumberVectorDistanceFunction<?> distanceFunction)
double[][]	RandomNormalGeneratedInitialMeans.chooseInitialMeans(Database database, Relation<? extends NumberVector> relation, int k, NumberVectorDistanceFunction<?> distanceFunction)
double[][]	RandomlyChosenInitialMeans.chooseInitialMeans(Database database, Relation<? extends NumberVector> relation, int k, NumberVectorDistanceFunction<?> distanceFunction)
double[][]	SampleKMeansInitialization.chooseInitialMeans(Database database, Relation<? extends NumberVector> relation, int k, NumberVectorDistanceFunction<?> distanceFunction)

Uses of NumberVectorDistanceFunction in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.parallel

Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.parallel declared as NumberVectorDistanceFunction

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

Constructors in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.parallel with parameters of type NumberVectorDistanceFunction

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

Uses of NumberVectorDistanceFunction in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.quality

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.quality with parameters of type NumberVectorDistanceFunction

Modifier and Type	Method and Description
static <V extends NumberVector> double	AbstractKMeansQualityMeasure.logLikelihood(Relation<V> relation, Clustering<? extends MeanModel> clustering, NumberVectorDistanceFunction<? super V> distanceFunction) Computes log likelihood of an entire clustering.
static <V extends NumberVector> double	BayesianInformationCriterionZhao.logLikelihoodZhao(Relation<V> relation, Clustering<? extends MeanModel> clustering, NumberVectorDistanceFunction<? super V> distanceFunction) Computes log likelihood of an entire clustering.
<V extends NumberVector> double	BayesianInformationCriterion.quality(Clustering<? extends MeanModel> clustering, NumberVectorDistanceFunction<? super V> distanceFunction, Relation<V> relation)
<V extends NumberVector> double	BayesianInformationCriterionZhao.quality(Clustering<? extends MeanModel> clustering, NumberVectorDistanceFunction<? super V> distanceFunction, Relation<V> relation)
<V extends NumberVector> double	WithinClusterVarianceQualityMeasure.quality(Clustering<? extends MeanModel> clustering, NumberVectorDistanceFunction<? super V> distanceFunction, Relation<V> relation)
<V extends NumberVector> double	AkaikeInformationCriterion.quality(Clustering<? extends MeanModel> clustering, NumberVectorDistanceFunction<? super V> distanceFunction, Relation<V> relation)
<V extends NumberVector> double	WithinClusterMeanDistanceQualityMeasure.quality(Clustering<? extends MeanModel> clustering, NumberVectorDistanceFunction<? super V> distanceFunction, Relation<V> relation)
<V extends O> double	KMeansQualityMeasure.quality(Clustering<? extends MeanModel> clustering, NumberVectorDistanceFunction<? super V> distanceFunction, Relation<V> relation) Calculates and returns the quality measure.
static <V extends NumberVector> double	AbstractKMeansQualityMeasure.varianceOfCluster(Cluster<? extends MeanModel> cluster, NumberVectorDistanceFunction<? super V> distanceFunction, Relation<V> relation) Variance contribution of a single cluster.

Uses of NumberVectorDistanceFunction in de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain

Constructors in de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain with parameters of type NumberVectorDistanceFunction

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

Uses of NumberVectorDistanceFunction in de.lmu.ifi.dbs.elki.algorithm.outlier.clustering

Fields in de.lmu.ifi.dbs.elki.algorithm.outlier.clustering declared as NumberVectorDistanceFunction

Modifier and Type	Field and Description
protected NumberVectorDistanceFunction<? super O>	CBLOF.distance Distance function to use.
protected NumberVectorDistanceFunction<? super O>	CBLOF.Parameterizer.distance Distance function to use.

Methods in de.lmu.ifi.dbs.elki.algorithm.outlier.clustering with parameters of type NumberVectorDistanceFunction

Modifier and Type	Method and Description
private void	CBLOF.computeCBLOFs(Relation<O> relation, NumberVectorDistanceFunction<? super O> distance, WritableDoubleDataStore cblofs, DoubleMinMax cblofMinMax, java.util.List<? extends Cluster<MeanModel>> largeClusters, java.util.List<? extends Cluster<MeanModel>> smallClusters) Compute the CBLOF scores for all the data.
private double	CBLOF.computeLargeClusterCBLOF(O obj, NumberVectorDistanceFunction<? super O> distanceQuery, NumberVector clusterMean, Cluster<MeanModel> cluster)
private double	CBLOF.computeSmallClusterCBLOF(O obj, NumberVectorDistanceFunction<? super O> distance, java.util.List<NumberVector> largeClusterMeans, Cluster<MeanModel> cluster)

Constructors in de.lmu.ifi.dbs.elki.algorithm.outlier.clustering with parameters of type NumberVectorDistanceFunction

Constructor and Description
CBLOF(NumberVectorDistanceFunction<? super O> distanceFunction, ClusteringAlgorithm<Clustering<MeanModel>> clusteringAlgorithm, double alpha, double beta) Constructor.

Uses of NumberVectorDistanceFunction in de.lmu.ifi.dbs.elki.algorithm.outlier.distance

Constructors in de.lmu.ifi.dbs.elki.algorithm.outlier.distance with parameters of type NumberVectorDistanceFunction

Constructor and Description
ReferenceBasedOutlierDetection(int k, NumberVectorDistanceFunction<? super NumberVector> distanceFunction, ReferencePointsHeuristic refp) Constructor with parameters.

Uses of NumberVectorDistanceFunction in de.lmu.ifi.dbs.elki.algorithm.outlier.lof

Fields in de.lmu.ifi.dbs.elki.algorithm.outlier.lof declared as NumberVectorDistanceFunction

Modifier and Type	Field and Description
private NumberVectorDistanceFunction<?>	ALOCI.Parameterizer.distanceFunction The distance function
private NumberVectorDistanceFunction<?>	ALOCI.distFunc Distance function

Constructors in de.lmu.ifi.dbs.elki.algorithm.outlier.lof with parameters of type NumberVectorDistanceFunction

Constructor and Description
ALOCI(NumberVectorDistanceFunction<?> distanceFunction, int nmin, int alpha, int g, RandomFactory rnd) Constructor.

Uses of NumberVectorDistanceFunction in de.lmu.ifi.dbs.elki.algorithm.statistics

Constructors in de.lmu.ifi.dbs.elki.algorithm.statistics with parameters of type NumberVectorDistanceFunction

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

Uses of NumberVectorDistanceFunction in de.lmu.ifi.dbs.elki.distance.distancefunction

Subinterfaces of NumberVectorDistanceFunction in de.lmu.ifi.dbs.elki.distance.distancefunction

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

Classes in de.lmu.ifi.dbs.elki.distance.distancefunction that implement NumberVectorDistanceFunction

Modifier and Type	Class and Description
class	AbstractNumberVectorDistanceFunction Abstract base class for the most common family of distance functions: defined on number vectors and returning double values.
class	ArcCosineDistanceFunction Arcus cosine distance function for feature vectors.
class	ArcCosineUnitlengthDistanceFunction Arcus cosine distance function for feature vectors.
class	BrayCurtisDistanceFunction Bray-Curtis distance function / Sørensen–Dice coefficient for continuous vector spaces (not only binary data).
class	CanberraDistanceFunction Canberra distance function, a variation of Manhattan distance.
class	ClarkDistanceFunction Clark distance function for vector spaces.
class	CosineDistanceFunction Cosine distance function for feature vectors.
class	CosineUnitlengthDistanceFunction Cosine distance function for unit length feature vectors.
class	MahalanobisDistanceFunction Mahalanobis quadratic form distance for feature vectors.
class	MatrixWeightedQuadraticDistanceFunction Matrix weighted quadratic distance, the squared form of MahalanobisDistanceFunction.
class	WeightedCanberraDistanceFunction Weighted Canberra distance function, a variation of Manhattan distance.

Uses of NumberVectorDistanceFunction in de.lmu.ifi.dbs.elki.distance.distancefunction.colorhistogram

Classes in de.lmu.ifi.dbs.elki.distance.distancefunction.colorhistogram that implement NumberVectorDistanceFunction

Modifier and Type	Class and Description
class	HistogramIntersectionDistanceFunction Intersection distance for color histograms.
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.

Uses of NumberVectorDistanceFunction in de.lmu.ifi.dbs.elki.distance.distancefunction.correlation

Classes in de.lmu.ifi.dbs.elki.distance.distancefunction.correlation that implement NumberVectorDistanceFunction

Modifier and Type	Class and Description
class	AbsolutePearsonCorrelationDistanceFunction Absolute Pearson correlation distance function for feature vectors.
class	AbsoluteUncenteredCorrelationDistanceFunction Absolute uncentered correlation distance function for feature vectors.
class	PearsonCorrelationDistanceFunction Pearson correlation distance function for feature vectors.
class	SquaredPearsonCorrelationDistanceFunction Squared Pearson correlation distance function for feature vectors.
class	SquaredUncenteredCorrelationDistanceFunction Squared uncentered correlation distance function for feature vectors.
class	UncenteredCorrelationDistanceFunction Uncentered correlation distance.
class	WeightedPearsonCorrelationDistanceFunction Pearson correlation distance function for feature vectors.
class	WeightedSquaredPearsonCorrelationDistanceFunction Weighted squared Pearson correlation distance function for feature vectors.

Uses of NumberVectorDistanceFunction in de.lmu.ifi.dbs.elki.distance.distancefunction.geo

Classes in de.lmu.ifi.dbs.elki.distance.distancefunction.geo that implement NumberVectorDistanceFunction

Modifier and Type	Class and Description
class	DimensionSelectingLatLngDistanceFunction Distance function for 2D vectors in Latitude, Longitude form.
class	LatLngDistanceFunction Distance function for 2D vectors in Latitude, Longitude form.
class	LngLatDistanceFunction Distance function for 2D vectors in Longitude, Latitude form.

Uses of NumberVectorDistanceFunction in de.lmu.ifi.dbs.elki.distance.distancefunction.histogram

Classes in de.lmu.ifi.dbs.elki.distance.distancefunction.histogram that implement NumberVectorDistanceFunction

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

Uses of NumberVectorDistanceFunction in de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski

Classes in de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski that implement NumberVectorDistanceFunction

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	LPNormDistanceFunction Lp-Norm (Minkowski norms) are a family of distances for NumberVectors.
class	ManhattanDistanceFunction Manhattan distance for NumberVectors.
class	MaximumDistanceFunction Maximum distance for NumberVectors.
class	MinimumDistanceFunction Minimum distance for NumberVectors.
class	SquaredEuclideanDistanceFunction Squared Euclidean distance, optimized for SparseNumberVectors.
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.
class	WeightedSquaredEuclideanDistanceFunction Weighted squared Euclidean distance for NumberVectors.

Uses of NumberVectorDistanceFunction in de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic

Classes in de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic that implement NumberVectorDistanceFunction

Modifier and Type	Class and Description
class	ChiDistanceFunction χ distance function, symmetric version.
class	ChiSquaredDistanceFunction χ² distance function, symmetric version.
class	FisherRaoDistanceFunction Fisher-Rao riemannian metric for (discrete) probability distributions.
class	HellingerDistanceFunction Hellinger metric / affinity / kernel, Bhattacharyya coefficient, fidelity similarity, Matusita distance, Hellinger-Kakutani metric on a probability distribution.
class	JeffreyDivergenceDistanceFunction Jeffrey Divergence for NumberVectors is a symmetric, smoothened version of the KullbackLeiblerDivergenceAsymmetricDistanceFunction.
class	JensenShannonDivergenceDistanceFunction Jensen-Shannon Divergence for NumberVectors is a symmetric, smoothened version of the KullbackLeiblerDivergenceAsymmetricDistanceFunction.
class	KullbackLeiblerDivergenceAsymmetricDistanceFunction Kullback-Leibler divergence, also known as relative entropy, information deviation, or just KL-distance (albeit asymmetric).
class	KullbackLeiblerDivergenceReverseAsymmetricDistanceFunction Kullback-Leibler divergence, also known as relative entropy, information deviation or just KL-distance (albeit asymmetric).
class	SqrtJensenShannonDivergenceDistanceFunction The square root of Jensen-Shannon divergence is a metric.
class	TriangularDiscriminationDistanceFunction Triangular Discrimination has relatively tight upper and lower bounds to the Jensen-Shannon divergence, but is much less expensive.
class	TriangularDistanceFunction 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 NumberVectorDistanceFunction in de.lmu.ifi.dbs.elki.distance.distancefunction.set

Classes in de.lmu.ifi.dbs.elki.distance.distancefunction.set that implement NumberVectorDistanceFunction

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

Uses of NumberVectorDistanceFunction in de.lmu.ifi.dbs.elki.distance.distancefunction.subspace

Classes in de.lmu.ifi.dbs.elki.distance.distancefunction.subspace that implement NumberVectorDistanceFunction

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 NumberVectors only in specified dimensions.
class	SubspaceLPNormDistanceFunction Lp-Norm distance function between NumberVectors only in specified dimensions.
class	SubspaceManhattanDistanceFunction Manhattan distance function between NumberVectors only in specified dimensions.
class	SubspaceMaximumDistanceFunction Maximum distance function between NumberVectors only in specified dimensions.

Uses of NumberVectorDistanceFunction in de.lmu.ifi.dbs.elki.distance.distancefunction.timeseries

Classes in de.lmu.ifi.dbs.elki.distance.distancefunction.timeseries that implement NumberVectorDistanceFunction

Modifier and Type	Class and Description
class	AbstractEditDistanceFunction Edit Distance for FeatureVectors.
class	DerivativeDTWDistanceFunction Derivative Dynamic Time Warping distance for numerical vectors.
class	DTWDistanceFunction Dynamic Time Warping distance (DTW) for numerical vectors.
class	EDRDistanceFunction Edit Distance on Real Sequence distance for numerical vectors.
class	ERPDistanceFunction Edit Distance With Real Penalty distance for numerical vectors.
class	LCSSDistanceFunction Longest Common Subsequence distance for numerical vectors.

Uses of NumberVectorDistanceFunction in de.lmu.ifi.dbs.elki.distance.similarityfunction

Classes in de.lmu.ifi.dbs.elki.distance.similarityfunction that implement NumberVectorDistanceFunction

Modifier and Type	Class and Description
class	Kulczynski1SimilarityFunction Kulczynski similarity 1.

Uses of NumberVectorDistanceFunction in de.lmu.ifi.dbs.elki.evaluation.clustering.internal

Fields in de.lmu.ifi.dbs.elki.evaluation.clustering.internal declared as NumberVectorDistanceFunction

Modifier and Type	Field and Description
private NumberVectorDistanceFunction<?>	EvaluateSquaredErrors.distance Distance function to use.
private NumberVectorDistanceFunction<?>	EvaluateSquaredErrors.Parameterizer.distance Distance function to use.
private NumberVectorDistanceFunction<?>	EvaluateSimplifiedSilhouette.distance Distance function to use.
private NumberVectorDistanceFunction<?>	EvaluateSimplifiedSilhouette.Parameterizer.distance Distance function to use.
private NumberVectorDistanceFunction<?>	EvaluatePBMIndex.Parameterizer.distance Distance function to use.
private NumberVectorDistanceFunction<?>	EvaluateDaviesBouldin.Parameterizer.distance Distance function to use.
private NumberVectorDistanceFunction<?>	EvaluatePBMIndex.distanceFunction Distance function to use.
private NumberVectorDistanceFunction<?>	EvaluateDaviesBouldin.distanceFunction Distance function to use.

Constructors in de.lmu.ifi.dbs.elki.evaluation.clustering.internal with parameters of type NumberVectorDistanceFunction

Constructor and Description
EvaluateDaviesBouldin(NumberVectorDistanceFunction<?> distance, NoiseHandling noiseOpt) Constructor.
EvaluatePBMIndex(NumberVectorDistanceFunction<?> distance, NoiseHandling noiseOpt) Constructor.
EvaluateSimplifiedSilhouette(NumberVectorDistanceFunction<?> distance, NoiseHandling noiseOpt, boolean penalize) Constructor.
EvaluateSquaredErrors(NumberVectorDistanceFunction<?> distance, NoiseHandling noiseOption) Constructor.

Uses of NumberVectorDistanceFunction in tutorial.clustering

Fields in tutorial.clustering declared as NumberVectorDistanceFunction

Modifier and Type	Field and Description
protected NumberVectorDistanceFunction<? super V>	SameSizeKMeansAlgorithm.Parameterizer.distanceFunction Distance function

Methods in tutorial.clustering with parameters of type NumberVectorDistanceFunction

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

Constructors in tutorial.clustering with parameters of type NumberVectorDistanceFunction

Constructor and Description
SameSizeKMeansAlgorithm(NumberVectorDistanceFunction<? super V> distanceFunction, int k, int maxiter, KMeansInitialization initializer) Constructor.

Uses of NumberVectorDistanceFunction in tutorial.distancefunction

Classes in tutorial.distancefunction that implement NumberVectorDistanceFunction

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