Uses of Class
de.lmu.ifi.dbs.elki.distance.distancevalue.NumberDistance

Packages that use NumberDistance

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.clustering	Clustering algorithms.
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans	K-means clustering and variations.
de.lmu.ifi.dbs.elki.algorithm.outlier	Outlier detection algorithms
de.lmu.ifi.dbs.elki.algorithm.outlier.lof	LOF family of outlier detection algorithms.
de.lmu.ifi.dbs.elki.algorithm.outlier.spatial	Spatial outlier detection algorithms
de.lmu.ifi.dbs.elki.algorithm.outlier.subspace	Subspace outlier detection methods.
de.lmu.ifi.dbs.elki.algorithm.statistics	Statistical analysis algorithms The algorithms in this package perform statistical analysis of the data (e.g. compute distributions, distance distributions etc.)
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.distance.distancefunction.adapter	Distance functions deriving distances from e.g. similarity measures
de.lmu.ifi.dbs.elki.distance.distancefunction.external	Distance functions using external data sources.
de.lmu.ifi.dbs.elki.distance.distancevalue	Distance values, i.e. object storing an actual distance value along with comparison functions and value parsers.
de.lmu.ifi.dbs.elki.distance.similarityfunction	Similarity functions.
de.lmu.ifi.dbs.elki.evaluation.similaritymatrix	Render a distance matrix to visualize a clustering-distance-combination.
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants	M-Tree and variants.
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees	Metrical index structures based on the concepts of the M-Tree supporting processing of reverse k nearest neighbor queries by using the k-nn distances of the entries.
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkapp	MkAppTree
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkcop	MkCoPTree
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkmax	MkMaxTree
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mktab	MkTabTree
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mtree	MTree
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.query	Classes for performing queries (knn, range, ...) on metrical trees.
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.insert	Insertion (choose path) strategies of nodes in an M-Tree (and variants).
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.split	Splitting strategies of nodes in an M-Tree (and variants).
de.lmu.ifi.dbs.elki.math.linearalgebra.pca	Principal Component Analysis (PCA) and Eigenvector processing.
de.lmu.ifi.dbs.elki.visualization.opticsplot	Code for drawing OPTICS plots
de.lmu.ifi.dbs.elki.visualization.svg	Base SVG functionality (generation, markers, thumbnails, export, ...).
de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.index	Visualizers for index structures based on 2D projections.
de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.selection	Visualizers for object selection based on 2D projections.
tutorial.clustering	Classes from the tutorial on implementing a custom k-means variation.
tutorial.outlier

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

Classes in de.lmu.ifi.dbs.elki.algorithm with type parameters of type NumberDistance

Modifier and Type	Class and Description
class	MaterializeDistances<O,D extends NumberDistance<D,?>> Algorithm to materialize all the distances in a data set.
static class	MaterializeDistances.Parameterizer<O,D extends NumberDistance<D,?>> Parameterization class.

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

Classes in de.lmu.ifi.dbs.elki.algorithm.benchmark with type parameters of type NumberDistance

Modifier and Type	Class and Description
class	RangeQueryBenchmarkAlgorithm<O extends NumberVector<?>,D extends NumberDistance<D,?>> Benchmarking algorithm that computes a range query for each point.
static class	RangeQueryBenchmarkAlgorithm.Parameterizer<O extends NumberVector<?>,D extends NumberDistance<D,?>> Parameterization class

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

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering with type parameters of type NumberDistance

Modifier and Type	Class and Description
class	NaiveMeanShiftClustering<V extends NumberVector<?>,D extends NumberDistance<D,?>> Mean-shift based clustering algorithm.
static class	NaiveMeanShiftClustering.Parameterizer<V extends NumberVector<?>,D extends NumberDistance<D,?>> Parameterizer.
class	OPTICSXi<N extends NumberDistance<N,?>> Class to handle OPTICS Xi extraction.
static class	OPTICSXi.Parameterizer<D extends NumberDistance<D,?>> Parameterization class.
private static class	OPTICSXi.SteepScanPosition<N extends NumberDistance<N,?>> Position when scanning for steep areas

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

Modifier and Type	Field and Description
(package private) D	NaiveMeanShiftClustering.range Range of the kernel.
(package private) D	NaiveMeanShiftClustering.Parameterizer.range Kernel radius.

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

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical with type parameters of type NumberDistance

Modifier and Type	Class and Description
class	NaiveAgglomerativeHierarchicalClustering<O,D extends NumberDistance<D,?>> This tutorial will step you through implementing a well known clustering algorithm, agglomerative hierarchical clustering, in multiple steps.
static class	NaiveAgglomerativeHierarchicalClustering.Parameterizer<O,D extends NumberDistance<D,?>> Parameterization class

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

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans with type parameters of type NumberDistance

Modifier and Type	Class and Description
class	KMeansPlusPlusInitialMeans<V,D extends NumberDistance<D,?>> K-Means++ initialization for k-means.
static class	KMeansPlusPlusInitialMeans.Parameterizer<V,D extends NumberDistance<D,?>> Parameterization class.
class	KMedoidsEM<V,D extends NumberDistance<D,?>> Provides the k-medoids clustering algorithm, using a "bulk" variation of the "Partitioning Around Medoids" approach.
static class	KMedoidsEM.Parameterizer<V,D extends NumberDistance<D,?>> Parameterization class.
class	KMedoidsPAM<V,D extends NumberDistance<D,?>> Provides the k-medoids clustering algorithm, using the "Partitioning Around Medoids" approach.
static class	KMedoidsPAM.Parameterizer<V,D extends NumberDistance<D,?>> Parameterization class.
class	PAMInitialMeans<V,D extends NumberDistance<D,?>> PAM initialization for k-means (and of course, PAM).
static class	PAMInitialMeans.Parameterizer<V,D extends NumberDistance<D,?>> Parameterization class.

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

Classes in de.lmu.ifi.dbs.elki.algorithm.outlier with type parameters of type NumberDistance

Modifier and Type	Class and Description
class	COP<V extends NumberVector<?>,D extends NumberDistance<D,?>> Correlation outlier probability: Outlier Detection in Arbitrarily Oriented Subspaces Hans-Peter Kriegel, Peer Kröger, Erich Schubert, Arthur Zimek Outlier Detection in Arbitrarily Oriented Subspaces in: Proc.
static class	COP.Parameterizer<V extends NumberVector<?>,D extends NumberDistance<D,?>> Parameterization class.
class	KNNOutlier<O,D extends NumberDistance<D,?>> Outlier Detection based on the distance of an object to its k nearest neighbor.
static class	KNNOutlier.Parameterizer<O,D extends NumberDistance<D,?>> Parameterization class.
class	KNNWeightOutlier<O,D extends NumberDistance<D,?>> Outlier Detection based on the accumulated distances of a point to its k nearest neighbors.
static class	KNNWeightOutlier.Parameterizer<O,D extends NumberDistance<D,?>> Parameterization class.
class	OPTICSOF<O,D extends NumberDistance<D,?>> OPTICSOF provides the Optics-of algorithm, an algorithm to find Local Outliers in a database.
static class	OPTICSOF.Parameterizer<O,D extends NumberDistance<D,?>> Parameterization class.
class	ReferenceBasedOutlierDetection<V extends NumberVector<?>,D extends NumberDistance<D,?>> provides the Reference-Based Outlier Detection algorithm, an algorithm that computes kNN distances approximately, using reference points.
static class	ReferenceBasedOutlierDetection.Parameterizer<V extends NumberVector<?>,D extends NumberDistance<D,?>> Parameterization class.
class	SimpleCOP<V extends NumberVector<?>,D extends NumberDistance<D,?>> Algorithm to compute local correlation outlier probability.
static class	SimpleCOP.Parameterizer<V extends NumberVector<?>,D extends NumberDistance<D,?>> Parameterization class.

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

Classes in de.lmu.ifi.dbs.elki.algorithm.outlier.lof with type parameters of type NumberDistance

Modifier and Type	Class and Description
class	ALOCI<O extends NumberVector<?>,D extends NumberDistance<D,?>> Fast Outlier Detection Using the "approximate Local Correlation Integral".
static class	ALOCI.Parameterizer<O extends NumberVector<?>,D extends NumberDistance<D,?>> Parameterization class.
class	FlexibleLOF<O,D extends NumberDistance<D,?>> Flexible variant of the "Local Outlier Factor" algorithm.
static class	FlexibleLOF.LOFResult<O,D extends NumberDistance<D,?>> Encapsulates information like the neighborhood, the LRD and LOF values of the objects during a run of the FlexibleLOF algorithm.
static class	FlexibleLOF.Parameterizer<O,D extends NumberDistance<D,?>> Parameterization class.
class	INFLO<O,D extends NumberDistance<D,?>> INFLO provides the Mining Algorithms (Two-way Search Method) for Influence Outliers using Symmetric Relationship Reference: Jin, W., Tung, A., Han, J., and Wang, W. 2006 Ranking outliers using symmetric neighborhood relationship In Proc.
static class	INFLO.Parameterizer<O,D extends NumberDistance<D,?>> Parameterization class.
class	LDF<O extends NumberVector<?>,D extends NumberDistance<D,?>> Outlier Detection with Kernel Density Functions.
static class	LDF.Parameterizer<O extends NumberVector<?>,D extends NumberDistance<D,?>> Parameterization class.
class	LDOF<O,D extends NumberDistance<D,?>> Computes the LDOF (Local Distance-Based Outlier Factor) for all objects of a Database.
static class	LDOF.Parameterizer<O,D extends NumberDistance<D,?>> Parameterization class.
class	LOCI<O,D extends NumberDistance<D,?>> Fast Outlier Detection Using the "Local Correlation Integral".
static class	LOCI.Parameterizer<O,D extends NumberDistance<D,?>> Parameterization class.
class	LOF<O,D extends NumberDistance<D,?>> Algorithm to compute density-based local outlier factors in a database based on a specified parameter LOF.Parameterizer.K_ID (-lof.k).
static class	LOF.Parameterizer<O,D extends NumberDistance<D,?>> Parameterization class.
class	LoOP<O,D extends NumberDistance<D,?>> LoOP: Local Outlier Probabilities Distance/density based algorithm similar to LOF to detect outliers, but with statistical methods to achieve better result stability.
static class	LoOP.Parameterizer<O,D extends NumberDistance<D,?>> Parameterization class.
class	OnlineLOF<O,D extends NumberDistance<D,?>> Incremental version of the LOF Algorithm, supports insertions and removals.
static class	OnlineLOF.Parameterizer<O,D extends NumberDistance<D,?>> Parameterization class.
class	SimpleKernelDensityLOF<O extends NumberVector<?>,D extends NumberDistance<D,?>> A simple variant of the LOF algorithm, which uses a simple kernel density estimation instead of the local reachability density.
static class	SimpleKernelDensityLOF.Parameterizer<O extends NumberVector<?>,D extends NumberDistance<D,?>> Parameterization class.
class	SimplifiedLOF<O,D extends NumberDistance<D,?>> A simplified version of the original LOF algorithm, which does not use the reachability distance, yielding less stable results on inliers.
static class	SimplifiedLOF.Parameterizer<O,D extends NumberDistance<D,?>> Parameterization class.

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

Modifier and Type	Field and Description
private D	LOCI.rmax Holds the value of LOCI.RMAX_ID.
protected D	LOCI.Parameterizer.rmax

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

Classes in de.lmu.ifi.dbs.elki.algorithm.outlier.spatial with type parameters of type NumberDistance

Modifier and Type	Class and Description
class	AbstractDistanceBasedSpatialOutlier<N,O,D extends NumberDistance<D,?>> Abstract base class for distance-based spatial outlier detection methods.
static class	AbstractDistanceBasedSpatialOutlier.Parameterizer<N,O,D extends NumberDistance<D,?>> Parameterization class.
class	CTLuGLSBackwardSearchAlgorithm<V extends NumberVector<?>,D extends NumberDistance<D,?>> GLS-Backward Search is a statistical approach to detecting spatial outliers.
static class	CTLuGLSBackwardSearchAlgorithm.Parameterizer<V extends NumberVector<?>,D extends NumberDistance<D,?>> Parameterization class
class	CTLuRandomWalkEC<N,D extends NumberDistance<D,?>> Spatial outlier detection based on random walks.
static class	CTLuRandomWalkEC.Parameterizer<N,D extends NumberDistance<D,?>> Parameterization class.
class	SLOM<N,O,D extends NumberDistance<D,?>> SLOM: a new measure for local spatial outliers Reference: Sanjay Chawla and Pei Sun SLOM: a new measure for local spatial outliers in Knowledge and Information Systems 9(4), 412-429, 2006 This implementation works around some corner cases in SLOM, in particular when an object has none or a single neighbor only (albeit the results will still not be too useful then), which will result in divisions by zero.
static class	SLOM.Parameterizer<N,O,D extends NumberDistance<D,?>> Parameterization class.
class	SOF<N,O,D extends NumberDistance<D,?>> The Spatial Outlier Factor (SOF) is a spatial LOF variation.
static class	SOF.Parameterizer<N,O,D extends NumberDistance<D,?>> Parameterization class

Uses of NumberDistance in de.lmu.ifi.dbs.elki.algorithm.outlier.subspace

Classes in de.lmu.ifi.dbs.elki.algorithm.outlier.subspace with type parameters of type NumberDistance

Modifier and Type	Class and Description
class	SOD<V extends NumberVector<?>,D extends NumberDistance<D,?>> Subspace Outlier Degree.
static class	SOD.Parameterizer<V extends NumberVector<?>,D extends NumberDistance<D,?>> Parameterization class.

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

Classes in de.lmu.ifi.dbs.elki.algorithm.statistics with type parameters of type NumberDistance

Modifier and Type	Class and Description
class	AveragePrecisionAtK<V,D extends NumberDistance<D,?>> Evaluate a distance functions performance by computing the average precision at k, when ranking the objects by distance.
static class	AveragePrecisionAtK.Parameterizer<V extends NumberVector<?>,D extends NumberDistance<D,?>> Parameterization class.
class	DistanceStatisticsWithClasses<O,D extends NumberDistance<D,?>> Algorithm to gather statistics over the distance distribution in the data set.
static class	DistanceStatisticsWithClasses.Parameterizer<O,D extends NumberDistance<D,?>> Parameterization class.
class	EvaluateRankingQuality<V extends NumberVector<?>,D extends NumberDistance<D,?>> Evaluate a distance function with respect to kNN queries.
static class	EvaluateRankingQuality.Parameterizer<V extends NumberVector<?>,D extends NumberDistance<D,?>> Parameterization class.
class	RankingQualityHistogram<O,D extends NumberDistance<D,?>> Evaluate a distance function with respect to kNN queries.
static class	RankingQualityHistogram.Parameterizer<O,D extends NumberDistance<D,?>> Parameterization class.

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

Classes in de.lmu.ifi.dbs.elki.application.cache with type parameters of type NumberDistance

Modifier and Type	Class and Description
class	CacheDoubleDistanceInOnDiskMatrix<O,D extends NumberDistance<D,?>> Precompute an on-disk distance matrix, using double precision.
static class	CacheDoubleDistanceInOnDiskMatrix.Parameterizer<O,D extends NumberDistance<D,?>> Parameterization class.
class	CacheDoubleDistanceKNNLists<O,D extends NumberDistance<D,?>> Precompute the k nearest neighbors in a disk cache.
static class	CacheDoubleDistanceKNNLists.Parameterizer<O,D extends NumberDistance<D,?>> Parameterization class.
class	CacheFloatDistanceInOnDiskMatrix<O,D extends NumberDistance<D,?>> Precompute an on-disk distance matrix, using double precision.
static class	CacheFloatDistanceInOnDiskMatrix.Parameterizer<O,D extends NumberDistance<D,?>> Parameterization class.

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

Classes in de.lmu.ifi.dbs.elki.application.greedyensemble with type parameters of type NumberDistance

Modifier and Type	Class and Description
class	ComputeKNNOutlierScores<O,D extends NumberDistance<D,?>> Application that runs a series of kNN-based algorithms on a data set, for building an ensemble in a second step.
static class	ComputeKNNOutlierScores.Parameterizer<O,D extends NumberDistance<D,?>> Parameterization class.

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

Fields in de.lmu.ifi.dbs.elki.distance.distancefunction.adapter with type parameters of type NumberDistance

Modifier and Type	Field and Description
protected NormalizedSimilarityFunction<? super O,? extends NumberDistance<?,?>>	AbstractSimilarityAdapter.similarityFunction Holds the similarity function.
protected NormalizedSimilarityFunction<? super O,? extends NumberDistance<?,?>>	AbstractSimilarityAdapter.Parameterizer.similarityFunction Holds the similarity function.
private SimilarityQuery<? super O,? extends NumberDistance<?,?>>	AbstractSimilarityAdapter.Instance.similarityQuery The similarity query we use.

Constructor parameters in de.lmu.ifi.dbs.elki.distance.distancefunction.adapter with type arguments of type NumberDistance

Constructor and Description
AbstractSimilarityAdapter.Instance(Relation<O> database, DistanceFunction<? super O,DoubleDistance> parent, SimilarityQuery<? super O,? extends NumberDistance<?,?>> similarityQuery) Constructor.
AbstractSimilarityAdapter(NormalizedSimilarityFunction<? super O,? extends NumberDistance<?,?>> similarityFunction) Constructor.
ArccosSimilarityAdapter.Instance(Relation<O> database, DistanceFunction<? super O,DoubleDistance> parent, SimilarityQuery<O,? extends NumberDistance<?,?>> similarityQuery) Constructor.
ArccosSimilarityAdapter(NormalizedSimilarityFunction<? super O,? extends NumberDistance<?,?>> similarityFunction) Constructor.
LinearAdapterLinear.Instance(Relation<O> database, DistanceFunction<? super O,DoubleDistance> parent, SimilarityQuery<? super O,? extends NumberDistance<?,?>> similarityQuery) Constructor.
LinearAdapterLinear(NormalizedSimilarityFunction<? super O,? extends NumberDistance<?,?>> similarityFunction) Constructor.
LnSimilarityAdapter.Instance(Relation<O> database, DistanceFunction<? super O,DoubleDistance> parent, SimilarityQuery<O,? extends NumberDistance<?,?>> similarityQuery) Constructor.
LnSimilarityAdapter(NormalizedSimilarityFunction<? super O,? extends NumberDistance<?,?>> similarityFunction) Constructor.

Uses of NumberDistance in de.lmu.ifi.dbs.elki.distance.distancefunction.external

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

Modifier and Type	Class and Description
class	NumberDistanceParser<D extends NumberDistance<D,?>> Provides a parser for parsing one distance value per line.
static class	NumberDistanceParser.Parameterizer<D extends NumberDistance<D,?>> Parameterization class.

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

Modifier and Type	Field and Description
private D	NumberDistanceParser.distanceFactory The distance function.
protected D	NumberDistanceParser.Parameterizer.distanceFactory The distance function.

Uses of NumberDistance in de.lmu.ifi.dbs.elki.distance.distancevalue

Classes in de.lmu.ifi.dbs.elki.distance.distancevalue with type parameters of type NumberDistance

Modifier and Type	Class and Description
class	NumberDistance<D extends NumberDistance<D,N>,N extends Number> Provides a Distance for a number-valued distance.

Subclasses of NumberDistance in de.lmu.ifi.dbs.elki.distance.distancevalue

Modifier and Type	Class and Description
class	BitDistance Provides a Distance for a bit-valued distance.
class	DoubleDistance Provides a Distance for a double-valued distance.
class	FloatDistance Provides a Distance for a float-valued distance.
class	IntegerDistance Provides an integer distance value.

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

Fields in de.lmu.ifi.dbs.elki.distance.similarityfunction with type parameters of type NumberDistance

Modifier and Type	Field and Description
protected PrimitiveDistanceFunction<? super O,? extends NumberDistance<?,?>>	InvertedDistanceSimilarityFunction.distanceFunction Holds the similarity function.

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

Fields in de.lmu.ifi.dbs.elki.evaluation.similaritymatrix with type parameters of type NumberDistance

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

Constructor parameters in de.lmu.ifi.dbs.elki.evaluation.similaritymatrix with type arguments of type NumberDistance

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

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

Classes in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants with type parameters of type NumberDistance

Modifier and Type	Class and Description
class	AbstractMTree<O,D extends NumberDistance<D,?>,N extends AbstractMTreeNode<O,D,N,E>,E extends MTreeEntry,S extends MTreeSettings<O,D,N,E>> Abstract super class for all M-Tree variants.
class	AbstractMTreeFactory<O,D extends NumberDistance<D,?>,N extends AbstractMTreeNode<O,D,N,E>,E extends MTreeEntry,I extends AbstractMTree<O,D,N,E,S> & Index,S extends MTreeSettings<O,D,N,E>> Abstract factory for various MTrees
static class	AbstractMTreeFactory.Parameterizer<O,D extends NumberDistance<D,?>,N extends AbstractMTreeNode<O,D,N,E>,E extends MTreeEntry,S extends MTreeSettings<O,D,N,E>> Parameterization class.
class	AbstractMTreeNode<O,D extends NumberDistance<D,?>,N extends AbstractMTreeNode<O,D,N,E>,E extends MTreeEntry> Abstract super class for nodes in M-Tree variants.
class	MTreeSettings<O,D extends NumberDistance<D,?>,N extends AbstractMTreeNode<O,D,N,E>,E extends MTreeEntry> Class to store the MTree settings.

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

Classes in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees with type parameters of type NumberDistance

Modifier and Type	Class and Description
class	AbstractMkTree<O,D extends NumberDistance<D,?>,N extends AbstractMTreeNode<O,D,N,E>,E extends MTreeEntry,S extends MTreeSettings<O,D,N,E>> Abstract class for all M-Tree variants supporting processing of reverse k-nearest neighbor queries by using the k-nn distances of the entries, where k is less than or equal to the given parameter.
class	AbstractMkTreeUnified<O,D extends NumberDistance<D,?>,N extends AbstractMTreeNode<O,D,N,E>,E extends MTreeEntry,S extends MkTreeSettings<O,D,N,E>> Abstract class for all M-Tree variants supporting processing of reverse k-nearest neighbor queries by using the k-nn distances of the entries, where k is less than or equal to the given parameter.
class	AbstractMkTreeUnifiedFactory<O,D extends NumberDistance<D,?>,N extends AbstractMTreeNode<O,D,N,E>,E extends MTreeEntry,I extends AbstractMkTree<O,D,N,E,S> & Index,S extends MkTreeSettings<O,D,N,E>> Abstract factory for various Mk-Trees
static class	AbstractMkTreeUnifiedFactory.Parameterizer<O,D extends NumberDistance<D,?>,N extends AbstractMTreeNode<O,D,N,E>,E extends MTreeEntry,S extends MkTreeSettings<O,D,N,E>> Parameterization class.
class	MkTreeSettings<O,D extends NumberDistance<D,?>,N extends AbstractMTreeNode<O,D,N,E>,E extends MTreeEntry> Class with settings for MkTrees.

Uses of NumberDistance in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkapp

Classes in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkapp with type parameters of type NumberDistance

Modifier and Type	Class and Description
class	MkAppTree<O,D extends NumberDistance<D,?>> MkAppTree is a metrical index structure based on the concepts of the M-Tree supporting efficient processing of reverse k nearest neighbor queries for parameter k < kmax.
class	MkAppTreeFactory<O,D extends NumberDistance<D,?>> Factory for a MkApp-Tree
static class	MkAppTreeFactory.Parameterizer<O,D extends NumberDistance<D,?>> Parameterization class.
class	MkAppTreeIndex<O,D extends NumberDistance<D,?>> MkAppTree used as database index.
(package private) class	MkAppTreeNode<O,D extends NumberDistance<D,?>> Represents a node in an MkApp-Tree.
class	MkAppTreeSettings<O,D extends NumberDistance<D,?>> Settings class for the MkApp Tree.

Uses of NumberDistance in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkcop

Classes in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkcop with type parameters of type NumberDistance

Modifier and Type	Class and Description
class	MkCoPTree<O,D extends NumberDistance<D,?>> MkCopTree is a metrical index structure based on the concepts of the M-Tree supporting efficient processing of reverse k nearest neighbor queries for parameter k < kmax.
class	MkCopTreeFactory<O,D extends NumberDistance<D,?>> Factory for a MkCoPTree-Tree
static class	MkCopTreeFactory.Parameterizer<O,D extends NumberDistance<D,?>> Parameterization class.
class	MkCoPTreeIndex<O,D extends NumberDistance<D,?>> MkCoPTree used as database index.
(package private) class	MkCoPTreeNode<O,D extends NumberDistance<D,?>> Represents a node in an MkCop-Tree.

Uses of NumberDistance in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkmax

Classes in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkmax with type parameters of type NumberDistance

Modifier and Type	Class and Description
class	MkMaxTree<O,D extends NumberDistance<D,?>> MkMaxTree is a metrical index structure based on the concepts of the M-Tree supporting efficient processing of reverse k nearest neighbor queries for parameter k <= k_max.
class	MkMaxTreeFactory<O,D extends NumberDistance<D,?>> Factory for MkMaxTrees
static class	MkMaxTreeFactory.Parameterizer<O,D extends NumberDistance<D,?>> Parameterization class.
class	MkMaxTreeIndex<O,D extends NumberDistance<D,?>> MkMax tree
(package private) class	MkMaxTreeNode<O,D extends NumberDistance<D,?>> Represents a node in an MkMaxTree.

Uses of NumberDistance in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mktab

Classes in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mktab with type parameters of type NumberDistance

Modifier and Type	Class and Description
class	MkTabTree<O,D extends NumberDistance<D,?>> MkTabTree is a metrical index structure based on the concepts of the M-Tree supporting efficient processing of reverse k nearest neighbor queries for parameter k < kmax.
class	MkTabTreeFactory<O,D extends NumberDistance<D,?>> Factory for MkTabTrees
static class	MkTabTreeFactory.Parameterizer<O,D extends NumberDistance<D,?>> Parameterization class.
class	MkTabTreeIndex<O,D extends NumberDistance<D,?>> MkTabTree used as database index.
(package private) class	MkTabTreeNode<O,D extends NumberDistance<D,?>> Represents a node in a MkMax-Tree.

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

Classes in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mtree with type parameters of type NumberDistance

Modifier and Type	Class and Description
class	MTree<O,D extends NumberDistance<D,?>> MTree is a metrical index structure based on the concepts of the M-Tree.
class	MTreeFactory<O,D extends NumberDistance<D,?>> Factory for a M-Tree
static class	MTreeFactory.Parameterizer<O,D extends NumberDistance<D,?>> Parameterization class.
class	MTreeIndex<O,D extends NumberDistance<D,?>> Class for using an m-tree as database index.
class	MTreeNode<O,D extends NumberDistance<D,?>> Represents a node in an M-Tree.

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

Classes in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.query with type parameters of type NumberDistance

Modifier and Type	Class and Description
class	MetricalIndexKNNQuery<O,D extends NumberDistance<D,?>> Instance of a KNN query for a particular spatial index.
class	MetricalIndexRangeQuery<O,D extends NumberDistance<D,?>> Instance of a range query for a particular spatial index.
class	MkTreeRKNNQuery<O,D extends NumberDistance<D,?>> Instance of a rKNN query for a particular spatial index.

Methods in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.query with type parameters of type NumberDistance

Modifier and Type	Method and Description
static <O,D extends NumberDistance<D,?>> KNNQuery<O,D>	MTreeQueryUtil.getKNNQuery(AbstractMTree<O,D,?,?,?> tree, DistanceQuery<O,D> distanceQuery, Object... hints) Get an RTree knn query, using an optimized double implementation when possible.
static <O,D extends NumberDistance<D,?>> RangeQuery<O,D>	MTreeQueryUtil.getRangeQuery(AbstractMTree<O,D,?,?,?> tree, DistanceQuery<O,D> distanceQuery, Object... hints) Get an RTree knn query, using an optimized double implementation when possible.

Uses of NumberDistance in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.insert

Classes in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.insert with type parameters of type NumberDistance

Modifier and Type	Class and Description
class	MinimumEnlargementInsert<O,D extends NumberDistance<D,?>,N extends AbstractMTreeNode<O,D,N,E>,E extends MTreeEntry> Default insertion strategy for the M-tree.
interface	MTreeInsert<O,D extends NumberDistance<D,?>,N extends AbstractMTreeNode<O,D,N,E>,E extends MTreeEntry> Default insertion strategy for the M-tree.

Uses of NumberDistance in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.split

Classes in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.split with type parameters of type NumberDistance

Modifier and Type	Class and Description
class	MLBDistSplit<O,D extends NumberDistance<D,?>,N extends AbstractMTreeNode<O,D,N,E>,E extends MTreeEntry> Encapsulates the required methods for a split of a node in an M-Tree.
class	MMRadSplit<O,D extends NumberDistance<D,?>,N extends AbstractMTreeNode<O,D,N,E>,E extends MTreeEntry> Encapsulates the required methods for a split of a node in an M-Tree.
class	MRadSplit<O,D extends NumberDistance<D,?>,N extends AbstractMTreeNode<O,D,N,E>,E extends MTreeEntry> Encapsulates the required methods for a split of a node in an M-Tree.
class	MTreeSplit<O,D extends NumberDistance<D,?>,N extends AbstractMTreeNode<O,D,N,E>,E extends MTreeEntry> Abstract super class for splitting a node in an M-Tree.
class	RandomSplit<O,D extends NumberDistance<D,?>,N extends AbstractMTreeNode<O,D,N,E>,E extends MTreeEntry> Encapsulates the required methods for a split of a node in an M-Tree.
static class	RandomSplit.Parameterizer<O,D extends NumberDistance<D,?>,N extends AbstractMTreeNode<O,D,N,E>,E extends MTreeEntry> Parameterization class.

Uses of NumberDistance in de.lmu.ifi.dbs.elki.math.linearalgebra.pca

Methods in de.lmu.ifi.dbs.elki.math.linearalgebra.pca with type parameters of type NumberDistance

Modifier and Type	Method and Description
private <D extends NumberDistance<D,?>> void	PCAFilteredAutotuningRunner.assertSortedByDistance(DistanceDBIDList<D> results) Ensure that the results are sorted by distance.
<D extends NumberDistance<D,?>> PCAFilteredResult	PCAFilteredAutotuningRunner.processQueryResult(DistanceDBIDList<D> results, Relation<? extends V> database)
<D extends NumberDistance<D,?>> PCAResult	PCARunner.processQueryResult(DistanceDBIDList<D> results, Relation<? extends V> database) Run PCA on a QueryResult Collection.
<D extends NumberDistance<D,?>> PCAFilteredResult	PCAFilteredRunner.processQueryResult(DistanceDBIDList<D> results, Relation<? extends V> database) Run PCA on a QueryResult Collection.
<D extends NumberDistance<D,?>> Matrix	AbstractCovarianceMatrixBuilder.processQueryResults(DistanceDBIDList<D> results, Relation<? extends V> database)
<D extends NumberDistance<D,?>> Matrix	CovarianceMatrixBuilder.processQueryResults(DistanceDBIDList<D> results, Relation<? extends V> database) Compute Covariance Matrix for a QueryResult Collection.
<D extends NumberDistance<D,?>> Matrix	AbstractCovarianceMatrixBuilder.processQueryResults(DistanceDBIDList<D> results, Relation<? extends V> database, int k)
<D extends NumberDistance<D,?>> Matrix	CovarianceMatrixBuilder.processQueryResults(DistanceDBIDList<D> results, Relation<? extends V> database, int k) Compute Covariance Matrix for a QueryResult Collection.
<D extends NumberDistance<D,?>> Matrix	WeightedCovarianceMatrixBuilder.processQueryResults(DistanceDBIDList<D> results, Relation<? extends V> database, int k) Compute Covariance Matrix for a QueryResult Collection.

Uses of NumberDistance in de.lmu.ifi.dbs.elki.visualization.opticsplot

Classes in de.lmu.ifi.dbs.elki.visualization.opticsplot with type parameters of type NumberDistance

Modifier and Type	Class and Description
class	OPTICSNumberDistance<D extends NumberDistance<D,?>> Adapter that will map a regular number distance to its double value.

Uses of NumberDistance in de.lmu.ifi.dbs.elki.visualization.svg

Methods in de.lmu.ifi.dbs.elki.visualization.svg with type parameters of type NumberDistance

Modifier and Type	Method and Description
static <D extends NumberDistance<?,?>> Element	SVGHyperSphere.drawCross(SVGPlot svgp, Projection2D proj, NumberVector<?> mid, D rad) Wireframe "cross" hypersphere

Uses of NumberDistance in de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.index

Classes in de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.index with type parameters of type NumberDistance

Modifier and Type	Class and Description
class	TreeSphereVisualization.Instance<D extends NumberDistance<D,?>,N extends AbstractMTreeNode<?,D,N,E>,E extends MTreeEntry> Instance for a particular tree.

Uses of NumberDistance in de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.selection

Classes in de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.selection with type parameters of type NumberDistance

Modifier and Type	Class and Description
class	DistanceFunctionVisualization.Instance<D extends NumberDistance<D,?>> Instance, visualizing a particular set of kNNs

Uses of NumberDistance in tutorial.clustering

Classes in tutorial.clustering with type parameters of type NumberDistance

Modifier and Type	Class and Description
class	NaiveAgglomerativeHierarchicalClustering1<O,D extends NumberDistance<D,?>> This tutorial will step you through implementing a well known clustering algorithm, agglomerative hierarchical clustering, in multiple steps.
static class	NaiveAgglomerativeHierarchicalClustering1.Parameterizer<O,D extends NumberDistance<D,?>> Parameterization class
class	NaiveAgglomerativeHierarchicalClustering2<O,D extends NumberDistance<D,?>> This tutorial will step you through implementing a well known clustering algorithm, agglomerative hierarchical clustering, in multiple steps.
static class	NaiveAgglomerativeHierarchicalClustering2.Parameterizer<O,D extends NumberDistance<D,?>> Parameterization class
class	NaiveAgglomerativeHierarchicalClustering3<O,D extends NumberDistance<D,?>> This tutorial will step you through implementing a well known clustering algorithm, agglomerative hierarchical clustering, in multiple steps.
static class	NaiveAgglomerativeHierarchicalClustering3.Parameterizer<O,D extends NumberDistance<D,?>> Parameterization class
class	NaiveAgglomerativeHierarchicalClustering4<O,D extends NumberDistance<D,?>> This tutorial will step you through implementing a well known clustering algorithm, agglomerative hierarchical clustering, in multiple steps.
static class	NaiveAgglomerativeHierarchicalClustering4.Parameterizer<O,D extends NumberDistance<D,?>> Parameterization class

Uses of NumberDistance in tutorial.outlier

Classes in tutorial.outlier with type parameters of type NumberDistance

Modifier and Type	Class and Description
class	DistanceStddevOutlier<O,D extends NumberDistance<D,?>> A simple outlier detection algorithm that computes the standard deviation of the kNN distances.
static class	DistanceStddevOutlier.Parameterizer<O,D extends NumberDistance<D,?>> Parameterization class