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

Packages that use DistanceFunction

Package	Description
de.lmu.ifi.dbs.elki.algorithm	Algorithms suitable as a task for the KDDTask main routine.
de.lmu.ifi.dbs.elki.algorithm.benchmark	Benchmarking pseudo algorithms.
de.lmu.ifi.dbs.elki.algorithm.classification	Classification algorithms.
de.lmu.ifi.dbs.elki.algorithm.clustering	Clustering algorithms Clustering algorithms are supposed to implement the Algorithm-Interface.
de.lmu.ifi.dbs.elki.algorithm.clustering.affinitypropagation	Affinity Propagation (AP) clustering.
de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan	Generalized DBSCAN Generalized DBSCAN is an abstraction of the original DBSCAN idea, that allows the use of arbitrary "neighborhood" and "core point" predicates.
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical	Hierarchical agglomerative clustering (HAC).
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans	K-means clustering and variations
de.lmu.ifi.dbs.elki.algorithm.clustering.optics	OPTICS family of clustering algorithms.
de.lmu.ifi.dbs.elki.algorithm.outlier	Outlier detection algorithms
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.distance.parallel	Parallel implementations of distance-based outlier detectors.
de.lmu.ifi.dbs.elki.algorithm.outlier.intrinsic	Outlier detection algorithms based on intrinsic dimensionality.
de.lmu.ifi.dbs.elki.algorithm.outlier.lof	LOF family of outlier detection algorithms
de.lmu.ifi.dbs.elki.algorithm.outlier.lof.parallel	Parallelized variants of LOF.
de.lmu.ifi.dbs.elki.algorithm.outlier.spatial	Spatial outlier detection algorithms
de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood	Spatial outlier neighborhood classes
de.lmu.ifi.dbs.elki.algorithm.projection	Data projections (see also preprocessing filters for basic projections).
de.lmu.ifi.dbs.elki.algorithm.statistics	Statistical analysis algorithms.
de.lmu.ifi.dbs.elki.application.cache	Utility applications for the persistence layer such as distance cache builders.
de.lmu.ifi.dbs.elki.application.greedyensemble	Greedy ensembles for outlier detection.
de.lmu.ifi.dbs.elki.database	ELKI database layer - loading, storing, indexing and accessing data
de.lmu.ifi.dbs.elki.database.query.distance	Prepared queries for distances
de.lmu.ifi.dbs.elki.database.relation	Relations, materialized and virtual (views)
de.lmu.ifi.dbs.elki.distance.distancefunction	Distance functions for use within ELKI.
de.lmu.ifi.dbs.elki.distance.distancefunction.adapter	Distance functions deriving distances from, e.g., similarity measures
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.external	Distance functions using external data sources
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.strings	Distance functions for strings
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.distance.similarityfunction.cluster	Similarity measures for comparing clusters.
de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel	Kernel functions.
de.lmu.ifi.dbs.elki.evaluation.clustering.internal	Internal evaluation measures for clusterings.
de.lmu.ifi.dbs.elki.evaluation.similaritymatrix	Render a distance matrix to visualize a clustering-distance-combination.
de.lmu.ifi.dbs.elki.index	Index structure implementations
de.lmu.ifi.dbs.elki.index.distancematrix	Precomputed distance matrix.
de.lmu.ifi.dbs.elki.index.idistance	iDistance is a distance based indexing technique, using a reference points embedding.
de.lmu.ifi.dbs.elki.index.lsh.hashfamilies	Hash function families for LSH
de.lmu.ifi.dbs.elki.index.preprocessed.knn	Indexes providing KNN and rKNN data.
de.lmu.ifi.dbs.elki.index.preprocessed.localpca	Index using a preprocessed local PCA
de.lmu.ifi.dbs.elki.index.preprocessed.snn	Indexes providing nearest neighbor sets
de.lmu.ifi.dbs.elki.index.tree.metrical	Tree-based index structures for metrical vector spaces
de.lmu.ifi.dbs.elki.index.tree.metrical.covertree	Cover-tree variations.
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants	M-Tree and variants
tutorial.clustering	Classes from the tutorial on implementing a custom k-means variation
tutorial.distancefunction	Classes from the tutorial on implementing distance functions
tutorial.outlier	Tutorials on implementing outlier detection methods in ELKI.

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.algorithm

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

Modifier and Type	Field and Description
private DistanceFunction<? super O>	AbstractDistanceBasedAlgorithm.distanceFunction Holds the instance of the distance function specified by DistanceBasedAlgorithm.DISTANCE_FUNCTION_ID.
protected DistanceFunction<? super O>	AbstractDistanceBasedAlgorithm.Parameterizer.distanceFunction The distance function to use.

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

Modifier and Type	Method and Description
DistanceFunction<? super O>	AbstractDistanceBasedAlgorithm.getDistanceFunction() Returns the distanceFunction.
DistanceFunction<? super O>	DistanceBasedAlgorithm.getDistanceFunction() Returns the distanceFunction.

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

Constructor and Description
AbstractDistanceBasedAlgorithm(DistanceFunction<? super O> distanceFunction) Constructor.
KNNDistancesSampler(DistanceFunction<? super O> distanceFunction, int k, double sample, RandomFactory rnd) Constructor.
KNNJoin(DistanceFunction<? super V> distanceFunction, int k) Constructor.

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.algorithm.benchmark

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

Constructor and Description
KNNBenchmarkAlgorithm(DistanceFunction<? super O> distanceFunction, int k, DatabaseConnection queries, double sampling, RandomFactory random) Constructor.
RangeQueryBenchmarkAlgorithm(DistanceFunction<? super O> distanceFunction, DatabaseConnection queries, double sampling, RandomFactory random) Constructor.
ValidateApproximativeKNNIndex(DistanceFunction<? super O> distanceFunction, int k, DatabaseConnection queries, double sampling, boolean forcelinear, RandomFactory random, java.util.regex.Pattern pattern) Constructor.

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.algorithm.classification

Fields in de.lmu.ifi.dbs.elki.algorithm.classification declared as DistanceFunction

Modifier and Type	Field and Description
protected DistanceFunction<? super O>	KNNClassifier.distanceFunction Distance function
protected DistanceFunction<? super O>	KNNClassifier.Parameterizer.distanceFunction Distance function

Methods in de.lmu.ifi.dbs.elki.algorithm.classification that return DistanceFunction

Modifier and Type	Method and Description
DistanceFunction<? super O>	KNNClassifier.getDistanceFunction()

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

Constructor and Description
KNNClassifier(DistanceFunction<? super O> distanceFunction, int k) Constructor.

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.algorithm.clustering

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

Modifier and Type	Field and Description
protected DistanceFunction<? super V>	GriDBSCAN.Instance.distanceFunction Distance function used.

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

Constructor and Description
CanopyPreClustering(DistanceFunction<? super O> distanceFunction, double t1, double t2) Constructor.
DBSCAN(DistanceFunction<? super O> distanceFunction, double epsilon, int minpts) Constructor with parameters.
GriDBSCAN(DistanceFunction<? super V> distanceFunction, double epsilon, int minpts, double gridwidth) Constructor with parameters.
Instance(DistanceFunction<? super V> distanceFunction, double epsilon, int minpts, double gridwidth) Constructor.
Leader(DistanceFunction<? super O> distanceFunction, double threshold) Constructor.
NaiveMeanShiftClustering(DistanceFunction<? super V> distanceFunction, KernelDensityFunction kernel, double range) Constructor.

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.algorithm.clustering.affinitypropagation

Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.affinitypropagation declared as DistanceFunction

Modifier and Type	Field and Description
(package private) DistanceFunction<? super O>	DistanceBasedInitializationWithMedian.distance Distance function.
(package private) DistanceFunction<? super O>	DistanceBasedInitializationWithMedian.Parameterizer.distance istance function.

Constructors in de.lmu.ifi.dbs.elki.algorithm.clustering.affinitypropagation with parameters of type DistanceFunction

Constructor and Description
DistanceBasedInitializationWithMedian(DistanceFunction<? super O> distance, double quantile) Constructor.

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan

Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan declared as DistanceFunction

Modifier and Type	Field and Description
protected DistanceFunction<O>	EpsilonNeighborPredicate.Parameterizer.distfun Distance function to use
(package private) DistanceFunction<O>	AbstractRangeQueryNeighborPredicate.Parameterizer.distfun Distance function to use
protected DistanceFunction<? super O>	EpsilonNeighborPredicate.distFunc Distance function to use
protected DistanceFunction<? super O>	AbstractRangeQueryNeighborPredicate.distFunc Distance function to use.

Constructors in de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan with parameters of type DistanceFunction

Constructor and Description
AbstractRangeQueryNeighborPredicate(double epsilon, DistanceFunction<? super O> distFunc) Full constructor.
EpsilonNeighborPredicate(double epsilon, DistanceFunction<? super O> distFunc) Full constructor.
LSDBC(DistanceFunction<? super O> distanceFunction, int k, double alpha) Constructor.

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical

Constructors in de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical with parameters of type DistanceFunction

Constructor and Description
AbstractHDBSCAN(DistanceFunction<? super O> distanceFunction, int minPts) Constructor.
AGNES(DistanceFunction<? super O> distanceFunction, Linkage linkage) Constructor.
AnderbergHierarchicalClustering(DistanceFunction<? super O> distanceFunction, Linkage linkage) Constructor.
CLINK(DistanceFunction<? super O> distanceFunction) Constructor.
HDBSCANLinearMemory(DistanceFunction<? super O> distanceFunction, int minPts) Constructor.
MiniMax(DistanceFunction<? super O> distanceFunction) Constructor.
MiniMaxAnderberg(DistanceFunction<? super O> distanceFunction) Constructor.
MiniMaxNNChain(DistanceFunction<? super O> distanceFunction) Constructor.
NNChain(DistanceFunction<? super O> distanceFunction, Linkage linkage) Constructor.
SLINK(DistanceFunction<? super O> distanceFunction) Constructor.
SLINKHDBSCANLinearMemory(DistanceFunction<? super O> distanceFunction, int minPts) Constructor.

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

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans that return DistanceFunction

Modifier and Type	Method and Description
DistanceFunction<? super V>	BestOfMultipleKMeans.getDistanceFunction()
DistanceFunction<? super V>	KMeansBisecting.getDistanceFunction()
DistanceFunction<? super V>	CLARA.CachedDistanceQuery.getDistanceFunction()

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

Constructor and Description
CLARA(DistanceFunction<? super V> distanceFunction, int k, int maxiter, KMedoidsInitialization<V> initializer, int numsamples, double sampling, boolean keepmed, RandomFactory random) Constructor.
CLARANS(DistanceFunction<? super V> distanceFunction, int k, int numlocal, double maxneighbor, RandomFactory random) Constructor.
FastCLARA(DistanceFunction<? super V> distanceFunction, int k, int maxiter, KMedoidsInitialization<V> initializer, double fasttol, int numsamples, double sampling, boolean keepmed, RandomFactory random) Constructor.
FastCLARANS(DistanceFunction<? super V> distanceFunction, int k, int numlocal, double maxneighbor, RandomFactory random) Constructor.
KMedoidsFastPAM(DistanceFunction<? super V> distanceFunction, int k, int maxiter, KMedoidsInitialization<V> initializer) Constructor.
KMedoidsFastPAM(DistanceFunction<? super V> distanceFunction, int k, int maxiter, KMedoidsInitialization<V> initializer, double fasttol) Constructor.
KMedoidsFastPAM1(DistanceFunction<? super V> distanceFunction, int k, int maxiter, KMedoidsInitialization<V> initializer) Constructor.
KMedoidsPAM(DistanceFunction<? super V> distanceFunction, int k, int maxiter, KMedoidsInitialization<V> initializer) Constructor.
KMedoidsPAMReynolds(DistanceFunction<? super V> distanceFunction, int k, int maxiter, KMedoidsInitialization<V> initializer) Constructor.
KMedoidsPark(DistanceFunction<? super V> distanceFunction, int k, int maxiter, KMedoidsInitialization<V> initializer) Constructor.

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.algorithm.clustering.optics

Constructors in de.lmu.ifi.dbs.elki.algorithm.clustering.optics with parameters of type DistanceFunction

Constructor and Description
AbstractOPTICS(DistanceFunction<? super O> distanceFunction, double epsilon, int minpts) Constructor.
DeLiClu(DeLiCluTreeFactory<? super V> indexer, DistanceFunction<? super V> distanceFunction, int minpts) Constructor.
OPTICSHeap(DistanceFunction<? super O> distanceFunction, double epsilon, int minpts) Constructor.
OPTICSList(DistanceFunction<? super O> distanceFunction, double epsilon, int minpts) Constructor.

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.algorithm.outlier

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

Constructor and Description
COP(DistanceFunction<? super V> distanceFunction, int k, PCARunner pca, double expect, COP.DistanceDist dist, boolean models) Constructor.
DWOF(DistanceFunction<? super O> distanceFunction, int k, double delta) Constructor.
OPTICSOF(DistanceFunction<? super O> distanceFunction, int minpts) Constructor with parameters.
SimpleCOP(DistanceFunction<? super V> distanceFunction, int k, PCARunner pca, EigenPairFilter filter) Constructor.

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

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

Constructor and Description
SilhouetteOutlierDetection(DistanceFunction<? super O> distanceFunction, ClusteringAlgorithm<?> clusterer, NoiseHandling noiseOption) Constructor.

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

Methods in de.lmu.ifi.dbs.elki.algorithm.outlier.distance with parameters of type DistanceFunction

Modifier and Type	Method and Description
protected void	AbstractDBOutlier.Parameterizer.configD(Parameterization config, DistanceFunction<?> distanceFunction) Grab the 'd' configuration option.

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

Constructor and Description
AbstractDBOutlier(DistanceFunction<? super O> distanceFunction, double d) Constructor with actual parameters.
DBOutlierDetection(DistanceFunction<? super O> distanceFunction, double d, double p) Constructor with actual parameters.
DBOutlierScore(DistanceFunction<? super O> distanceFunction, double d) Constructor with parameters.
KNNDD(DistanceFunction<? super O> distanceFunction, int k) Constructor for a single kNN query.
KNNOutlier(DistanceFunction<? super O> distanceFunction, int k) Constructor for a single kNN query.
KNNSOS(DistanceFunction<? super O> distance, int k) Constructor.
KNNWeightOutlier(DistanceFunction<? super O> distanceFunction, int k) Constructor with parameters.
LocalIsolationCoefficient(DistanceFunction<? super O> distanceFunction, int k) Constructor with parameters.
ODIN(DistanceFunction<? super O> distanceFunction, int k) Constructor.
SOS(DistanceFunction<? super O> distance, double h) Constructor.

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

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

Constructor and Description
ParallelKNNOutlier(DistanceFunction<? super O> distanceFunction, int k) Constructor.
ParallelKNNWeightOutlier(DistanceFunction<? super O> distanceFunction, int k) Constructor.

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.algorithm.outlier.intrinsic

Constructors in de.lmu.ifi.dbs.elki.algorithm.outlier.intrinsic with parameters of type DistanceFunction

Constructor and Description
IDOS(DistanceFunction<? super O> distanceFunction, IntrinsicDimensionalityEstimator estimator, int kc, int kr) Constructor.
IntrinsicDimensionalityOutlier(DistanceFunction<? super O> distanceFunction, int k, IntrinsicDimensionalityEstimator estimator) Constructor.
ISOS(DistanceFunction<? super O> distance, int k, IntrinsicDimensionalityEstimator estimator) Constructor.

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

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

Modifier and Type	Field and Description
protected DistanceFunction<? super O>	LoOP.comparisonDistanceFunction Distance function for comparison set.
protected DistanceFunction<O>	LoOP.Parameterizer.comparisonDistanceFunction Preprocessor Step 2.
protected DistanceFunction<? super O>	FlexibleLOF.Parameterizer.neighborhoodDistanceFunction Neighborhood distance function.
protected DistanceFunction<? super O>	FlexibleLOF.reachabilityDistanceFunction Reachability distance function.
protected DistanceFunction<? super O>	FlexibleLOF.Parameterizer.reachabilityDistanceFunction Reachability distance function.
protected DistanceFunction<? super O>	LoOP.reachabilityDistanceFunction Distance function for reachability.
protected DistanceFunction<O>	LoOP.Parameterizer.reachabilityDistanceFunction Preprocessor Step 1.
protected DistanceFunction<? super O>	FlexibleLOF.referenceDistanceFunction Neighborhood distance function.

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

Constructor and Description
COF(int k, DistanceFunction<? super O> distanceFunction) Constructor.
FlexibleLOF(int krefer, int kreach, DistanceFunction<? super O> neighborhoodDistanceFunction, DistanceFunction<? super O> reachabilityDistanceFunction) Constructor.
FlexibleLOF(int krefer, int kreach, DistanceFunction<? super O> neighborhoodDistanceFunction, DistanceFunction<? super O> reachabilityDistanceFunction) Constructor.
INFLO(DistanceFunction<? super O> distanceFunction, double m, int k) Constructor with parameters.
KDEOS(DistanceFunction<? super O> distanceFunction, int kmin, int kmax, KernelDensityFunction kernel, double minBandwidth, double scale, int idim) Constructor.
LDF(int k, DistanceFunction<? super O> distance, KernelDensityFunction kernel, double h, double c) Constructor.
LDOF(DistanceFunction<? super O> distanceFunction, int k) Constructor.
LOCI(DistanceFunction<? super O> distanceFunction, double rmax, int nmin, double alpha) Constructor.
LOF(int k, DistanceFunction<? super O> distanceFunction) Constructor.
LoOP(int kreach, int kcomp, DistanceFunction<? super O> reachabilityDistanceFunction, DistanceFunction<? super O> comparisonDistanceFunction, double lambda) Constructor with parameters.
LoOP(int kreach, int kcomp, DistanceFunction<? super O> reachabilityDistanceFunction, DistanceFunction<? super O> comparisonDistanceFunction, double lambda) Constructor with parameters.
OnlineLOF(int krefer, int kreach, DistanceFunction<? super O> neighborhoodDistanceFunction, DistanceFunction<? super O> reachabilityDistanceFunction) Constructor.
OnlineLOF(int krefer, int kreach, DistanceFunction<? super O> neighborhoodDistanceFunction, DistanceFunction<? super O> reachabilityDistanceFunction) Constructor.
SimpleKernelDensityLOF(int k, DistanceFunction<? super O> distance, KernelDensityFunction kernel) Constructor.
SimplifiedLOF(int k, DistanceFunction<? super O> distance) Constructor.
VarianceOfVolume(int k, DistanceFunction<? super O> distanceFunction) Constructor.

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

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

Constructor and Description
ParallelLOF(DistanceFunction<? super O> distanceFunction, int k) Constructor.
ParallelSimplifiedLOF(DistanceFunction<? super O> distanceFunction, int k) Constructor.

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.algorithm.outlier.spatial

Fields in de.lmu.ifi.dbs.elki.algorithm.outlier.spatial declared as DistanceFunction

Modifier and Type	Field and Description
private DistanceFunction<O>	AbstractDistanceBasedSpatialOutlier.nonSpatialDistanceFunction The distance function to use

Methods in de.lmu.ifi.dbs.elki.algorithm.outlier.spatial that return DistanceFunction

Modifier and Type	Method and Description
protected DistanceFunction<O>	AbstractDistanceBasedSpatialOutlier.getNonSpatialDistanceFunction() Get the non-spatial relation

Constructors in de.lmu.ifi.dbs.elki.algorithm.outlier.spatial with parameters of type DistanceFunction

Constructor and Description
AbstractDistanceBasedSpatialOutlier(NeighborSetPredicate.Factory<N> npredf, DistanceFunction<O> nonSpatialDistanceFunction) Constructor.
CTLuGLSBackwardSearchAlgorithm(DistanceFunction<? super V> distanceFunction, int k, double alpha) Constructor.
CTLuRandomWalkEC(DistanceFunction<? super P> distanceFunction, double alpha, double c, int k) Constructor.

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood

Fields in de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood declared as DistanceFunction

Modifier and Type	Field and Description
private DistanceFunction<? super O>	PrecomputedKNearestNeighborNeighborhood.Factory.distFunc distance function to use
(package private) DistanceFunction<? super O>	PrecomputedKNearestNeighborNeighborhood.Factory.Parameterizer.distFunc Distance function

Constructors in de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood with parameters of type DistanceFunction

Constructor and Description
Factory(int k, DistanceFunction<? super O> distFunc) Factory Constructor

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.algorithm.projection

Fields in de.lmu.ifi.dbs.elki.algorithm.projection declared as DistanceFunction

Modifier and Type	Field and Description
protected DistanceFunction<? super O>	GaussianAffinityMatrixBuilder.distanceFunction Input distance function.
protected DistanceFunction<? super O>	PerplexityAffinityMatrixBuilder.distanceFunction Input distance function.

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

Constructor and Description
GaussianAffinityMatrixBuilder(DistanceFunction<? super O> distanceFunction, double sigma) Constructor.
IntrinsicNearestNeighborAffinityMatrixBuilder(DistanceFunction<? super O> distanceFunction, double perplexity, IntrinsicDimensionalityEstimator estimator) Constructor.
NearestNeighborAffinityMatrixBuilder(DistanceFunction<? super O> distanceFunction, double perplexity) Constructor.
NearestNeighborAffinityMatrixBuilder(DistanceFunction<? super O> distanceFunction, double perplexity, int neighbors) Constructor.
PerplexityAffinityMatrixBuilder(DistanceFunction<? super O> distanceFunction, double perplexity) Constructor.

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

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

Constructor and Description
AveragePrecisionAtK(DistanceFunction<? super O> distanceFunction, int k, double sampling, RandomFactory random, boolean includeSelf) Constructor.
DistanceQuantileSampler(DistanceFunction<? super O> distanceFunction, double quantile, double sampling, boolean nozeros, RandomFactory rand) Constructor.
DistanceStatisticsWithClasses(DistanceFunction<? super O> distanceFunction, int numbins, boolean exact, boolean sampling) Constructor.
EstimateIntrinsicDimensionality(DistanceFunction<? super O> distanceFunction, IntrinsicDimensionalityEstimator estimator, double krate, double samples) Constructor.
EvaluateRankingQuality(DistanceFunction<? super V> distanceFunction, int numbins) Constructor.
EvaluateRetrievalPerformance(DistanceFunction<? super O> distanceFunction, double sampling, RandomFactory random, boolean includeSelf, int maxk) Constructor.
RangeQuerySelectivity(DistanceFunction<? super V> distanceFunction, double radius, double sampling, RandomFactory random) Constructor.
RankingQualityHistogram(DistanceFunction<? super O> distanceFunction, int numbins) Constructor.

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.application.cache

Fields in de.lmu.ifi.dbs.elki.application.cache declared as DistanceFunction

Modifier and Type	Field and Description
private DistanceFunction<? super O>	CacheDoubleDistanceInOnDiskMatrix.distance Distance function that is to be cached.
private DistanceFunction<? super O>	CacheDoubleDistanceInOnDiskMatrix.Parameterizer.distance Distance function that is to be cached.
private DistanceFunction<? super O>	CacheDoubleDistanceRangeQueries.distance Distance function that is to be cached.
private DistanceFunction<? super O>	CacheDoubleDistanceRangeQueries.Parameterizer.distance Distance function that is to be cached.
private DistanceFunction<? super O>	CacheFloatDistanceInOnDiskMatrix.distance Distance function that is to be cached.
private DistanceFunction<? super O>	CacheFloatDistanceInOnDiskMatrix.Parameterizer.distance Distance function that is to be cached.
private DistanceFunction<? super O>	CacheDoubleDistanceKNNLists.distance Distance function that is to be cached.
private DistanceFunction<? super O>	CacheDoubleDistanceKNNLists.Parameterizer.distance Distance function that is to be cached.
private DistanceFunction<? super O>	PrecomputeDistancesAsciiApplication.distance Distance function that is to be cached.
private DistanceFunction<? super O>	PrecomputeDistancesAsciiApplication.Parameterizer.distance Distance function that is to be cached.

Constructors in de.lmu.ifi.dbs.elki.application.cache with parameters of type DistanceFunction

Constructor and Description
CacheDoubleDistanceInOnDiskMatrix(Database database, DistanceFunction<? super O> distance, java.io.File out) Constructor.
CacheDoubleDistanceKNNLists(Database database, DistanceFunction<? super O> distance, int k, java.io.File out) Constructor.
CacheDoubleDistanceRangeQueries(Database database, DistanceFunction<? super O> distance, double radius, java.io.File out) Constructor.
CacheFloatDistanceInOnDiskMatrix(Database database, DistanceFunction<? super O> distance, java.io.File out) Constructor.
PrecomputeDistancesAsciiApplication(Database database, DistanceFunction<? super O> distance, java.io.File out) Constructor.

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.application.greedyensemble

Fields in de.lmu.ifi.dbs.elki.application.greedyensemble declared as DistanceFunction

Modifier and Type	Field and Description
(package private) DistanceFunction<? super O>	ComputeKNNOutlierScores.distf Distance function to use
(package private) DistanceFunction<? super O>	ComputeKNNOutlierScores.Parameterizer.distf Distance function to use

Constructors in de.lmu.ifi.dbs.elki.application.greedyensemble with parameters of type DistanceFunction

Constructor and Description
ComputeKNNOutlierScores(InputStep inputstep, DistanceFunction<? super O> distf, IntGenerator krange, ByLabelOutlier bylabel, java.io.File outfile, ScalingFunction scaling, java.util.regex.Pattern disable, int ksquarestop) Constructor.

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.database

Methods in de.lmu.ifi.dbs.elki.database with parameters of type DistanceFunction

Modifier and Type	Method and Description
static <O> DistanceQuery<O>	QueryUtil.getDistanceQuery(Database database, DistanceFunction<? super O> distanceFunction, java.lang.Object... hints) Get a distance query for a given distance function, automatically choosing a relation.
<O> DistanceQuery<O>	Database.getDistanceQuery(Relation<O> relation, DistanceFunction<? super O> distanceFunction, java.lang.Object... hints) Get the distance query for a particular distance function.
<O> DistanceQuery<O>	AbstractDatabase.getDistanceQuery(Relation<O> objQuery, DistanceFunction<? super O> distanceFunction, java.lang.Object... hints)
static <O> KNNQuery<O>	QueryUtil.getKNNQuery(Database database, DistanceFunction<? super O> distanceFunction, java.lang.Object... hints) Get a KNN query object for the given distance function.
static <O> KNNQuery<O>	QueryUtil.getKNNQuery(Relation<O> relation, DistanceFunction<? super O> distanceFunction, java.lang.Object... hints) Get a KNN query object for the given distance function.
static <O> RangeQuery<O>	QueryUtil.getRangeQuery(Database database, DistanceFunction<? super O> distanceFunction, java.lang.Object... hints) Get a range query object for the given distance function for radius-based neighbor search.
static <O> RangeQuery<O>	QueryUtil.getRangeQuery(Relation<O> relation, DistanceFunction<? super O> distanceFunction, java.lang.Object... hints) Get a range query object for the given distance function for radius-based neighbor search.
static <O> RKNNQuery<O>	QueryUtil.getRKNNQuery(Relation<O> relation, DistanceFunction<? super O> distanceFunction, java.lang.Object... hints) Get a rKNN query object for the given distance function.
static <O> DistanceQuery<O>	DatabaseUtil.precomputedDistanceQuery(Database database, Relation<O> relation, DistanceFunction<? super O> distf, Logging log) Get (or create) a precomputed distance query for the database.
static <O> KNNQuery<O>	DatabaseUtil.precomputedKNNQuery(Database database, Relation<O> relation, DistanceFunction<? super O> distf, int k) Get (or create) a precomputed kNN query for the database.

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.database.query.distance

Methods in de.lmu.ifi.dbs.elki.database.query.distance that return DistanceFunction

Modifier and Type	Method and Description
DistanceFunction<? super O>	DistanceQuery.getDistanceFunction() Get the inner distance function.

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.database.relation

Methods in de.lmu.ifi.dbs.elki.database.relation with parameters of type DistanceFunction

Modifier and Type	Method and Description
DistanceQuery<O>	Relation.getDistanceQuery(DistanceFunction<? super O> distanceFunction, java.lang.Object... hints) Get the distance query for a particular distance function.
DistanceQuery<O>	AbstractRelation.getDistanceQuery(DistanceFunction<? super O> distanceFunction, java.lang.Object... hints)
default KNNQuery<O>	Relation.getKNNQuery(DistanceFunction<? super O> distanceFunction, java.lang.Object... hints) Get a KNN query object for the given distance query.
default RangeQuery<O>	Relation.getRangeQuery(DistanceFunction<? super O> distanceFunction, java.lang.Object... hints) Get a range query object for the given distance query.
default RKNNQuery<O>	Relation.getRKNNQuery(DistanceFunction<? super O> distanceFunction, java.lang.Object... hints) Get a rKNN query object for the given distance query.

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

Classes in de.lmu.ifi.dbs.elki.distance.distancefunction with type parameters of type DistanceFunction

Modifier and Type	Class and Description
static class	AbstractIndexBasedDistanceFunction.Instance<O,I extends Index,F extends DistanceFunction<? super O>> The actual instance bound to a particular database.

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

Modifier and Type	Interface and Description
interface	DBIDDistanceFunction Distance functions valid in a database context only (i.e. for DBIDs) For any "distance" that cannot be computed for arbitrary objects, only those that exist in the database and referenced by their ID.
interface	DBIDRangeDistanceFunction Distance functions valid in a static database context only (i.e. for DBIDRanges) For any "distance" that cannot be computed for arbitrary objects, only those that exist in the database and referenced by their ID.
interface	IndexBasedDistanceFunction<O> Distance function relying on an index (such as preprocessed neighborhoods).
interface	Norm<O> Abstract interface for a mathematical norm.
interface	NumberVectorDistanceFunction<O> Base interface for the common case of distance functions defined on numerical vectors.
interface	PrimitiveDistanceFunction<O> Primitive distance function that is defined on some kind of object.
interface	SpatialPrimitiveDistanceFunction<V extends SpatialComparable> API for a spatial primitive distance function.
interface	WeightedNumberVectorDistanceFunction<V> Distance functions where each dimension is assigned a weight.

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

Modifier and Type	Class and Description
class	AbstractDatabaseDistanceFunction<O> Abstract super class for distance functions needing a database context.
class	AbstractDBIDRangeDistanceFunction Abstract base class for distance functions that rely on integer offsets within a consecutive range.
class	AbstractIndexBasedDistanceFunction<O,F extends IndexFactory<O>> Abstract super class for distance functions needing a database index.
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	RandomStableDistanceFunction This is a dummy distance providing random values (obviously not metrical), useful mostly for unit tests and baseline evaluations: obviously this distance provides no benefit whatsoever.
class	SharedNearestNeighborJaccardDistanceFunction<O> SharedNearestNeighborJaccardDistanceFunction computes the Jaccard coefficient, which is a proper distance metric.
class	WeightedCanberraDistanceFunction Weighted Canberra distance function, a variation of Manhattan distance.

Fields in de.lmu.ifi.dbs.elki.distance.distancefunction declared as DistanceFunction

Modifier and Type	Field and Description
protected DistanceFunction<? super O>	AbstractDatabaseDistanceFunction.Instance.parent Parent distance
protected F	AbstractIndexBasedDistanceFunction.Instance.parent Our parent distance function

Methods in de.lmu.ifi.dbs.elki.distance.distancefunction that return DistanceFunction

Modifier and Type	Method and Description
DistanceFunction<? super O>	AbstractDatabaseDistanceFunction.Instance.getDistanceFunction()

Constructors in de.lmu.ifi.dbs.elki.distance.distancefunction with parameters of type DistanceFunction

Constructor and Description
Instance(Relation<O> relation, DistanceFunction<? super O> parent) Constructor.

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.distance.distancefunction.adapter

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

Modifier and Type	Class and Description
class	AbstractSimilarityAdapter<O> Adapter from a similarity function to a distance function.
class	ArccosSimilarityAdapter<O> Adapter from a normalized similarity function to a distance function using arccos(sim).
class	LinearAdapterLinear<O> Adapter from a normalized similarity function to a distance function using 1 - sim.
class	LnSimilarityAdapter<O> Adapter from a normalized similarity function to a distance function using -log(sim).

Constructors in de.lmu.ifi.dbs.elki.distance.distancefunction.adapter with parameters of type DistanceFunction

Constructor and Description
Instance(Relation<O> database, DistanceFunction<? super O> parent, SimilarityQuery<? super O> similarityQuery) Constructor.
Instance(Relation<O> database, DistanceFunction<? super O> parent, SimilarityQuery<? super O> similarityQuery) Constructor.
Instance(Relation<O> database, DistanceFunction<? super O> parent, SimilarityQuery<O> similarityQuery) Constructor.
Instance(Relation<O> database, DistanceFunction<? super O> parent, SimilarityQuery<O> similarityQuery) Constructor.

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

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

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 DistanceFunction in de.lmu.ifi.dbs.elki.distance.distancefunction.correlation

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

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 DistanceFunction in de.lmu.ifi.dbs.elki.distance.distancefunction.external

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

Modifier and Type	Class and Description
class	DiskCacheBasedDoubleDistanceFunction Distance function that is based on double distances given by a distance matrix of an external binary matrix file.
class	DiskCacheBasedFloatDistanceFunction Distance function that is based on float distances given by a distance matrix of an external binary matrix file.
class	FileBasedSparseDoubleDistanceFunction Distance function that is based on double distances given by a distance matrix of an external ASCII file.
class	FileBasedSparseFloatDistanceFunction Distance function that is based on float distances given by a distance matrix of an external ASCII file.

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

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

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 DistanceFunction in de.lmu.ifi.dbs.elki.distance.distancefunction.histogram

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

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 DistanceFunction in de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski

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

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	SparseEuclideanDistanceFunction Euclidean distance function, optimized for SparseNumberVectors.
class	SparseLPNormDistanceFunction Lp-Norm, optimized for SparseNumberVectors.
class	SparseManhattanDistanceFunction Manhattan distance, optimized for SparseNumberVectors.
class	SparseMaximumDistanceFunction Maximum distance, optimized for SparseNumberVectors.
class	SparseSquaredEuclideanDistanceFunction Squared Euclidean distance function, optimized for SparseNumberVectors.
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 DistanceFunction in de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic

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

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 DistanceFunction in de.lmu.ifi.dbs.elki.distance.distancefunction.set

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

Modifier and Type	Class and Description
class	AbstractSetDistanceFunction<O> Abstract base class for set distance functions.
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 DistanceFunction in de.lmu.ifi.dbs.elki.distance.distancefunction.strings

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

Modifier and Type	Class and Description
class	LevenshteinDistanceFunction Classic Levenshtein distance on strings.
class	NormalizedLevenshteinDistanceFunction Levenshtein distance on strings, normalized by string length.

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

Subinterfaces of DistanceFunction in de.lmu.ifi.dbs.elki.distance.distancefunction.subspace

Modifier and Type	Interface and Description
interface	DimensionSelectingSubspaceDistanceFunction<O> Interface for dimension selecting subspace distance functions.

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

Modifier and Type	Class and Description
class	AbstractDimensionsSelectingDistanceFunction<V extends FeatureVector<?>> Abstract base class for distances computed only in subspaces.
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 DistanceFunction in de.lmu.ifi.dbs.elki.distance.distancefunction.timeseries

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

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 DistanceFunction in de.lmu.ifi.dbs.elki.distance.similarityfunction

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

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

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.distance.similarityfunction.cluster

Subinterfaces of DistanceFunction in de.lmu.ifi.dbs.elki.distance.similarityfunction.cluster

Modifier and Type	Interface and Description
interface	ClusteringDistanceSimilarityFunction Distance and similarity measure for clusterings.

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

Modifier and Type	Class and Description
class	ClusteringAdjustedRandIndexSimilarityFunction Measure the similarity of clusters via the Adjusted Rand Index.
class	ClusteringBCubedF1SimilarityFunction Measure the similarity of clusters via the BCubed F1 Index.
class	ClusteringFowlkesMallowsSimilarityFunction Measure the similarity of clusters via the Fowlkes-Mallows Index.
class	ClusteringRandIndexSimilarityFunction Measure the similarity of clusters via the Rand Index.
class	ClusterIntersectionSimilarityFunction Measure the similarity of clusters via the intersection size.
class	ClusterJaccardSimilarityFunction Measure the similarity of clusters via the Jaccard coefficient.

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel

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

Modifier and Type	Class and Description
class	LinearKernelFunction Linear Kernel function that computes a similarity between the two feature vectors x and y defined by \(x^T\cdot y\).
class	PolynomialKernelFunction Polynomial Kernel function that computes a similarity between the two feature vectors x and y defined by \((x^T\cdot y+b)^{\text{degree}}\).

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

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

Modifier and Type	Field and Description
private DistanceFunction<? super O>	EvaluateDBCV.Parameterizer.distance Distance function to use.
private DistanceFunction<? super O>	EvaluateCIndex.distance Distance function to use.
private DistanceFunction<? super O>	EvaluateCIndex.Parameterizer.distance Distance function to use.
private DistanceFunction<? super O>	EvaluateSilhouette.distance Distance function to use.
private DistanceFunction<? super O>	EvaluateSilhouette.Parameterizer.distance Distance function to use.
private DistanceFunction<? super O>	EvaluateDBCV.distanceFunction Distance function to use.

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

Constructor and Description
EvaluateCIndex(DistanceFunction<? super O> distance, NoiseHandling noiseOpt) Constructor.
EvaluateDBCV(DistanceFunction<? super O> distance) Constructor.
EvaluateSilhouette(DistanceFunction<? super O> distance, boolean mergenoise) Constructor.
EvaluateSilhouette(DistanceFunction<? super O> distance, NoiseHandling noiseOption, boolean penalize) Constructor.

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.evaluation.similaritymatrix

Fields in de.lmu.ifi.dbs.elki.evaluation.similaritymatrix declared as DistanceFunction

Modifier and Type	Field and Description
private DistanceFunction<? super O>	ComputeSimilarityMatrixImage.distanceFunction The distance function to use
private DistanceFunction<O>	ComputeSimilarityMatrixImage.Parameterizer.distanceFunction The distance function to use

Constructors in de.lmu.ifi.dbs.elki.evaluation.similaritymatrix with parameters of type DistanceFunction

Constructor and Description
ComputeSimilarityMatrixImage(DistanceFunction<? super O> distanceFunction, ScalingFunction scaling, boolean skipzero) Constructor.

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.index

Methods in de.lmu.ifi.dbs.elki.index with parameters of type DistanceFunction

Modifier and Type	Method and Description
DistanceQuery<O>	DistanceIndex.getDistanceQuery(DistanceFunction<? super O> distanceFunction, java.lang.Object... hints) Get a KNN query object for the given distance query and k.

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.index.distancematrix

Fields in de.lmu.ifi.dbs.elki.index.distancematrix declared as DistanceFunction

Modifier and Type	Field and Description
protected DistanceFunction<? super O>	PrecomputedDistanceMatrix.distanceFunction Nested distance function.
protected DistanceFunction<? super O>	PrecomputedDistanceMatrix.Factory.distanceFunction Nested distance function.
protected DistanceFunction<? super O>	PrecomputedDistanceMatrix.Factory.Parameterizer.distanceFunction Nested distance function.

Methods in de.lmu.ifi.dbs.elki.index.distancematrix that return DistanceFunction

Modifier and Type	Method and Description
DistanceFunction<? super O>	PrecomputedDistanceMatrix.PrecomputedDistanceQuery.getDistanceFunction()

Methods in de.lmu.ifi.dbs.elki.index.distancematrix with parameters of type DistanceFunction

Modifier and Type	Method and Description
DistanceQuery<O>	PrecomputedDistanceMatrix.getDistanceQuery(DistanceFunction<? super O> distanceFunction, java.lang.Object... hints)

Constructors in de.lmu.ifi.dbs.elki.index.distancematrix with parameters of type DistanceFunction

Constructor and Description
Factory(DistanceFunction<? super O> distanceFunction) Constructor.
PrecomputedDistanceMatrix(Relation<O> relation, DBIDRange range, DistanceFunction<? super O> distanceFunction) Constructor.

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.index.idistance

Fields in de.lmu.ifi.dbs.elki.index.idistance declared as DistanceFunction

Modifier and Type	Field and Description
(package private) DistanceFunction<? super V>	InMemoryIDistanceIndex.Factory.distance Distance function to use.
(package private) DistanceFunction<? super V>	InMemoryIDistanceIndex.Factory.Parameterizer.distance Distance function to use.

Methods in de.lmu.ifi.dbs.elki.index.idistance that return DistanceFunction

Modifier and Type	Method and Description
private DistanceFunction<? super O>	InMemoryIDistanceIndex.getDistanceFunction() Distance function.

Constructors in de.lmu.ifi.dbs.elki.index.idistance with parameters of type DistanceFunction

Constructor and Description
Factory(DistanceFunction<? super V> distance, KMedoidsInitialization<V> initialization, int k) Constructor.

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.index.lsh.hashfamilies

Methods in de.lmu.ifi.dbs.elki.index.lsh.hashfamilies with parameters of type DistanceFunction

Modifier and Type	Method and Description
boolean	EuclideanHashFunctionFamily.isCompatible(DistanceFunction<?> df)
boolean	LocalitySensitiveHashFunctionFamily.isCompatible(DistanceFunction<?> df) Check whether the given distance function can be accelerated using this hash family.
boolean	CosineHashFunctionFamily.isCompatible(DistanceFunction<?> df)
boolean	ManhattanHashFunctionFamily.isCompatible(DistanceFunction<?> df)

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.index.preprocessed.knn

Fields in de.lmu.ifi.dbs.elki.index.preprocessed.knn declared as DistanceFunction

Modifier and Type	Field and Description
protected DistanceFunction<? super O>	AbstractMaterializeKNNPreprocessor.distanceFunction The distance function to be used.
protected DistanceFunction<? super O>	AbstractMaterializeKNNPreprocessor.Factory.distanceFunction Hold the distance function to be used.
protected DistanceFunction<? super O>	AbstractMaterializeKNNPreprocessor.Factory.Parameterizer.distanceFunction Hold the distance function to be used.

Methods in de.lmu.ifi.dbs.elki.index.preprocessed.knn that return DistanceFunction

Modifier and Type	Method and Description
DistanceFunction<? super O>	AbstractMaterializeKNNPreprocessor.Factory.getDistanceFunction() Get the distance function.

Constructors in de.lmu.ifi.dbs.elki.index.preprocessed.knn with parameters of type DistanceFunction

Constructor and Description
AbstractMaterializeKNNPreprocessor(Relation<O> relation, DistanceFunction<? super O> distanceFunction, int k) Constructor.
CachedDoubleDistanceKNNPreprocessor(Relation<O> relation, DistanceFunction<? super O> distanceFunction, int k, java.io.File file) Constructor.
Factory(int k, DistanceFunction<? super NumberVector> distanceFunction) Constructor.
Factory(int k, DistanceFunction<? super O> distanceFunction) Constructor.
Factory(int k, DistanceFunction<? super O> distanceFunction) Constructor.
Factory(int k, DistanceFunction<? super O> distanceFunction) Index factory.
Factory(int k, DistanceFunction<? super O> distanceFunction) Constructor.
Factory(int k, DistanceFunction<? super O> distanceFunction) Index factory.
Factory(int k, DistanceFunction<? super O> distanceFunction, double share, RandomFactory rnd) Constructor.
Factory(int k, DistanceFunction<? super O> distanceFunction, java.io.File filename) Index factory.
Factory(int k, DistanceFunction<? super O> distanceFunction, int partitions, RandomFactory rnd) Constructor.
Factory(int k, DistanceFunction<? super O> distanceFunction, RandomFactory rnd, double delta, double rho, boolean noInitialNeighbors, int iterations) Constructor.
Factory(int k, DistanceFunction<? super V> distanceFunction, java.util.List<SpatialSorter> curvegen, double window, int variants, RandomFactory random) Constructor.
KNNJoinMaterializeKNNPreprocessor(Relation<V> relation, DistanceFunction<? super V> distanceFunction, int k) Constructor.
MaterializeKNNAndRKNNPreprocessor(Relation<O> relation, DistanceFunction<? super O> distanceFunction, int k) Constructor.
MaterializeKNNPreprocessor(Relation<O> relation, DistanceFunction<? super O> distanceFunction, int k) Constructor with preprocessing step.
MetricalIndexApproximationMaterializeKNNPreprocessor(Relation<O> relation, DistanceFunction<? super O> distanceFunction, int k) Constructor
NNDescent(Relation<O> relation, DistanceFunction<? super O> distanceFunction, int k, RandomFactory rnd, double delta, double rho, boolean noInitialNeighbors, int iterations) Constructor.
PartitionApproximationMaterializeKNNPreprocessor(Relation<O> relation, DistanceFunction<? super O> distanceFunction, int k, int partitions, RandomFactory rnd) Constructor
RandomSampleKNNPreprocessor(Relation<O> relation, DistanceFunction<? super O> distanceFunction, int k, double share, RandomFactory rnd) Constructor.
SpacefillingMaterializeKNNPreprocessor(Relation<O> relation, DistanceFunction<? super O> distanceFunction, int k, java.util.List<SpatialSorter> curvegen, double window, int variants, java.util.Random random) Constructor.
SpatialApproximationMaterializeKNNPreprocessor(Relation<O> relation, DistanceFunction<? super O> distanceFunction, int k) Constructor

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.index.preprocessed.localpca

Fields in de.lmu.ifi.dbs.elki.index.preprocessed.localpca declared as DistanceFunction

Modifier and Type	Field and Description
protected DistanceFunction<NV>	AbstractFilteredPCAIndex.Factory.pcaDistanceFunction Holds the instance of the distance function specified by AbstractFilteredPCAIndex.Factory.Parameterizer.PCA_DISTANCE_ID.
protected DistanceFunction<NV>	AbstractFilteredPCAIndex.Factory.Parameterizer.pcaDistanceFunction Holds the instance of the distance function specified by AbstractFilteredPCAIndex.Factory.Parameterizer.PCA_DISTANCE_ID.

Constructors in de.lmu.ifi.dbs.elki.index.preprocessed.localpca with parameters of type DistanceFunction

Constructor and Description
Factory(DistanceFunction<NV> pcaDistanceFunction, PCARunner pca, EigenPairFilter filter) Constructor.
Factory(DistanceFunction<V> pcaDistanceFunction, PCARunner pca, EigenPairFilter filter, int k) Constructor.

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.index.preprocessed.snn

Fields in de.lmu.ifi.dbs.elki.index.preprocessed.snn declared as DistanceFunction

Modifier and Type	Field and Description
protected DistanceFunction<O>	SharedNearestNeighborPreprocessor.distanceFunction Hold the distance function to be used.
protected DistanceFunction<O>	SharedNearestNeighborPreprocessor.Factory.distanceFunction Hold the distance function to be used.
protected DistanceFunction<O>	SharedNearestNeighborPreprocessor.Factory.Parameterizer.distanceFunction Hold the distance function to be used.

Constructors in de.lmu.ifi.dbs.elki.index.preprocessed.snn with parameters of type DistanceFunction

Constructor and Description
Factory(int numberOfNeighbors, DistanceFunction<O> distanceFunction) Constructor.
SharedNearestNeighborPreprocessor(Relation<O> relation, int numberOfNeighbors, DistanceFunction<O> distanceFunction) Constructor.

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.index.tree.metrical

Methods in de.lmu.ifi.dbs.elki.index.tree.metrical that return DistanceFunction

Modifier and Type	Method and Description
abstract DistanceFunction<? super O>	MetricalIndexTree.getDistanceFunction() Returns the distance function of this metrical index.

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.index.tree.metrical.covertree

Fields in de.lmu.ifi.dbs.elki.index.tree.metrical.covertree declared as DistanceFunction

Modifier and Type	Field and Description
protected DistanceFunction<? super O>	AbstractCoverTree.distanceFunction Holds the instance of the trees distance function.
protected DistanceFunction<? super O>	AbstractCoverTree.Factory.distanceFunction Holds the instance of the trees distance function.
protected DistanceFunction<? super O>	AbstractCoverTree.Factory.Parameterizer.distanceFunction Holds the instance of the trees distance function.

Constructors in de.lmu.ifi.dbs.elki.index.tree.metrical.covertree with parameters of type DistanceFunction

Constructor and Description
AbstractCoverTree(Relation<O> relation, DistanceFunction<? super O> distanceFunction, double expansion, int truncate) Constructor.
CoverTree(Relation<O> relation, DistanceFunction<? super O> distanceFunction, double expansion, int truncate) Constructor.
Factory(DistanceFunction<? super O> distanceFunction, double expansion, int truncate) Constructor.
Factory(DistanceFunction<? super O> distanceFunction, double expansion, int truncate) Constructor.
Factory(DistanceFunction<? super O> distanceFunction, double expansion, int truncate) Constructor.
SimplifiedCoverTree(Relation<O> relation, DistanceFunction<? super O> distanceFunction, double expansion, int truncate) Constructor.

Uses of DistanceFunction in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants

Fields in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants declared as DistanceFunction

Modifier and Type	Field and Description
protected DistanceFunction<? super O>	MTreeSettings.distanceFunction Holds the instance of the trees distance function.

Methods in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants that return DistanceFunction

Modifier and Type	Method and Description
DistanceFunction<? super O>	AbstractMTree.getDistanceFunction()

Uses of DistanceFunction in tutorial.clustering

Constructors in tutorial.clustering with parameters of type DistanceFunction

Constructor and Description
NaiveAgglomerativeHierarchicalClustering1(DistanceFunction<? super O> distanceFunction, int numclusters) Constructor.
NaiveAgglomerativeHierarchicalClustering2(DistanceFunction<? super O> distanceFunction, int numclusters) Constructor.
NaiveAgglomerativeHierarchicalClustering3(DistanceFunction<? super O> distanceFunction, int numclusters, NaiveAgglomerativeHierarchicalClustering3.Linkage linkage) Constructor.
NaiveAgglomerativeHierarchicalClustering4(DistanceFunction<? super O> distanceFunction, NaiveAgglomerativeHierarchicalClustering4.Linkage linkage) Constructor.

Uses of DistanceFunction in tutorial.distancefunction

Classes in tutorial.distancefunction that implement DistanceFunction

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.

Uses of DistanceFunction in tutorial.outlier

Constructors in tutorial.outlier with parameters of type DistanceFunction

Constructor and Description
DistanceStddevOutlier(DistanceFunction<? super O> distanceFunction, int k) Constructor.
ODIN(DistanceFunction<? super O> distanceFunction, int k) Constructor.