Uses of Interface
de.lmu.ifi.dbs.elki.database.relation.Relation

Packages that use Relation

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.affinitypropagation	Affinity Propagation (AP) clustering.
de.lmu.ifi.dbs.elki.algorithm.clustering.biclustering	Biclustering algorithms.
de.lmu.ifi.dbs.elki.algorithm.clustering.correlation	Correlation clustering algorithms
de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.cash	Helper classes for the CASH algorithm.
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.clustering.kmeans.quality	Quality measures for k-Means results.
de.lmu.ifi.dbs.elki.algorithm.clustering.onedimensional	Clustering algorithms for one-dimensional data.
de.lmu.ifi.dbs.elki.algorithm.clustering.subspace	Axis-parallel subspace clustering algorithms The clustering algorithms in this package are instances of both, projected clustering algorithms or subspace clustering algorithms according to the classical but somewhat obsolete classification schema of clustering algorithms for axis-parallel subspaces.
de.lmu.ifi.dbs.elki.algorithm.clustering.trivial	Trivial clustering algorithms: all in one, no clusters, label clusterings These methods are mostly useful for providing a reference result in evaluation.
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.meta	Meta outlier detection algorithms: external scores, score rescaling.
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.outlier.spatial.neighborhood.weighted	Weighted Neighborhood definitions.
de.lmu.ifi.dbs.elki.algorithm.outlier.subspace	Subspace outlier detection methods.
de.lmu.ifi.dbs.elki.algorithm.outlier.trivial	Trivial outlier detection algorithms: no outliers, all outliers, label outliers.
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.greedyensemble	Greedy ensembles for outlier detection.
de.lmu.ifi.dbs.elki.data	Basic classes for different data types, database object types and label types.
de.lmu.ifi.dbs.elki.data.model	Cluster models classes for various algorithms.
de.lmu.ifi.dbs.elki.database	ELKI database layer - loading, storing, indexing and accessing data
de.lmu.ifi.dbs.elki.database.query	Database queries - computing distances, neighbors, similarities - API and general documentation.
de.lmu.ifi.dbs.elki.database.query.distance	Prepared queries for distances.
de.lmu.ifi.dbs.elki.database.query.knn	Prepared queries for k nearest neighbor (kNN) queries.
de.lmu.ifi.dbs.elki.database.query.range	Prepared queries for ε-range queries.
de.lmu.ifi.dbs.elki.database.query.rknn	Prepared queries for reverse k nearest neighbor (rkNN) queries.
de.lmu.ifi.dbs.elki.database.query.similarity	Prepared queries for similarity functions.
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.correlation	Distance functions using correlations.
de.lmu.ifi.dbs.elki.distance.distancefunction.subspace	Distance functions based on subspaces.
de.lmu.ifi.dbs.elki.distance.similarityfunction	Similarity functions.
de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel	Kernel functions.
de.lmu.ifi.dbs.elki.evaluation.outlier	Evaluate an outlier score using a misclassification based cost model.
de.lmu.ifi.dbs.elki.evaluation.roc	Evaluation of rankings using ROC AUC (Receiver Operation Characteristics - Area Under Curve)
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.lsh	Locality Sensitive Hashing
de.lmu.ifi.dbs.elki.index.lsh.hashfamilies	Hash function families for LSH.
de.lmu.ifi.dbs.elki.index.preprocessed	Index structure based on preprocessors
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.preference	Indexes storing preference vectors.
de.lmu.ifi.dbs.elki.index.preprocessed.snn	Indexes providing nearest neighbor sets
de.lmu.ifi.dbs.elki.index.preprocessed.subspaceproj	Index using a preprocessed local subspaces.
de.lmu.ifi.dbs.elki.index.projected	Projected indexes for data.
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.spatial.kd	K-d-tree and variants.
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.deliclu	DeLiCluTree
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rstar	RStarTree
de.lmu.ifi.dbs.elki.index.vafile	Vector Approximation File
de.lmu.ifi.dbs.elki.math.dimensionsimilarity	Functions to compute the similarity of dimensions (or the interestingness of the combination).
de.lmu.ifi.dbs.elki.math.linearalgebra	Linear Algebra package provides classes and computational methods for operations on matrices.
de.lmu.ifi.dbs.elki.math.linearalgebra.pca	Principal Component Analysis (PCA) and Eigenvector processing.
de.lmu.ifi.dbs.elki.math.scales	Scales handling for plotting.
de.lmu.ifi.dbs.elki.math.spacefillingcurves	Space filling curves.
de.lmu.ifi.dbs.elki.result	Result types, representation and handling
de.lmu.ifi.dbs.elki.result.optics	Result classes for OPTICS.
de.lmu.ifi.dbs.elki.result.outlier	Outlier result classes
de.lmu.ifi.dbs.elki.result.textwriter	Text serialization (CSV, Gnuplot, Console, ...)
de.lmu.ifi.dbs.elki.utilities	Utility and helper classes - commonly used data structures, output formatting, exceptions, ...
de.lmu.ifi.dbs.elki.utilities.referencepoints	Package containing strategies to obtain reference points Shared code for various algorithms that use reference points.
de.lmu.ifi.dbs.elki.utilities.scaling.outlier	Scaling of Outlier scores, that require a statistical analysis of the occurring values
de.lmu.ifi.dbs.elki.visualization	Visualization package of ELKI.
de.lmu.ifi.dbs.elki.visualization.gui	Package to provide a visualization GUI.
de.lmu.ifi.dbs.elki.visualization.projector	Projectors are responsible for finding appropriate projections for data relations.
de.lmu.ifi.dbs.elki.visualization.visualizers.histogram	Visualizers based on 1D projected histograms.
de.lmu.ifi.dbs.elki.visualization.visualizers.parallel	Visualizers based on parallel coordinates.
de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot	Visualizers based on scatterplots.
de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.outlier	Visualizers for outlier scores based on 2D projections.
tutorial.clustering	Classes from the tutorial on implementing a custom k-means variation.
tutorial.outlier

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

Methods in de.lmu.ifi.dbs.elki.algorithm with parameters of type Relation

Modifier and Type	Method and Description
protected BitSet[]	APRIORI.frequentItemsets(Map<BitSet,Integer> support, BitSet[] candidates, Relation<BitVector> database) Returns the frequent BitSets out of the given BitSets with respect to the given database.
CorrelationAnalysisSolution<V>	DependencyDerivator.generateModel(Relation<V> db, DBIDs ids) Runs the pca on the given set of IDs.
CorrelationAnalysisSolution<V>	DependencyDerivator.generateModel(Relation<V> db, DBIDs ids, Vector centroid) Runs the pca on the given set of IDs and for the given centroid.
AprioriResult	APRIORI.run(Database database, Relation<BitVector> relation) Performs the APRIORI algorithm on the given database.
CollectionResult<CTriple<DBID,DBID,Double>>	MaterializeDistances.run(Database database, Relation<O> relation) Iterates over all points in the database.
Result	DummyAlgorithm.run(Database database, Relation<O> relation) Run the algorithm.
KNNDistanceOrderResult<D>	KNNDistanceOrder.run(Database database, Relation<O> relation) Provides an order of the kNN-distances for all objects within the specified database.
WritableDataStore<KNNList<D>>	KNNJoin.run(Database database, Relation<V> relation) Joins in the given spatial database to each object its k-nearest neighbors.
CorrelationAnalysisSolution<V>	DependencyDerivator.run(Database database, Relation<V> relation) Computes quantitatively linear dependencies among the attributes of the given database based on a linear correlation PCA.

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

Methods in de.lmu.ifi.dbs.elki.algorithm.benchmark with parameters of type Relation

Modifier and Type	Method and Description
Result	RangeQueryBenchmarkAlgorithm.run(Database database, Relation<O> relation) Run the algorithm, with a separate query set.
Result	KNNBenchmarkAlgorithm.run(Database database, Relation<O> relation) Run the algorithm.
Result	ValidateApproximativeKNNIndex.run(Database database, Relation<O> relation) Run the algorithm.
Result	RangeQueryBenchmarkAlgorithm.run(Database database, Relation<O> relation, Relation<NumberVector<?>> radrel) Run the algorithm, with separate radius relation
Result	RangeQueryBenchmarkAlgorithm.run(Database database, Relation<O> relation, Relation<NumberVector<?>> radrel) Run the algorithm, with separate radius relation

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

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

Modifier and Type	Method and Description
static double	EM.assignProbabilitiesToInstances(Relation<? extends NumberVector<?>> relation, double[] normDistrFactor, Vector[] means, Matrix[] invCovMatr, double[] clusterWeights, WritableDataStore<double[]> probClusterIGivenX) Assigns the current probability values to the instances in the database and compute the expectation value of the current mixture of distributions.
protected void	DBSCAN.expandCluster(Relation<O> relation, RangeQuery<O,D> rangeQuery, DBIDRef startObjectID, FiniteProgress objprog, IndefiniteProgress clusprog) DBSCAN-function expandCluster.
private Clustering<OPTICSModel>	OPTICSXi.extractClusters(ClusterOrderResult<N> clusterOrderResult, Relation<?> relation, double ixi, int minpts) Extract clusters from a cluster order result.
private DBID	DeLiClu.getStartObject(Relation<NV> relation) Returns the id of the start object for the run method.
static void	EM.recomputeCovarianceMatrices(Relation<? extends NumberVector<?>> relation, WritableDataStore<double[]> probClusterIGivenX, Vector[] means, Matrix[] covarianceMatrices, int dimensionality) Recompute the covariance matrixes.
Clustering<OPTICSModel>	OPTICSXi.run(Database database, Relation<?> relation)
ClusterOrderResult<D>	DeLiClu.run(Database database, Relation<NV> relation)
ClusterOrderResult<D>	OPTICS.run(Database database, Relation<O> relation) Run OPTICS on the database.
Clustering<Model>	SNNClustering.run(Database database, Relation<O> relation) Perform SNN clustering
Clustering<ClusterModel>	CanopyPreClustering.run(Database database, Relation<O> relation) Run the algorithm
Clustering<Model>	AbstractProjectedDBSCAN.run(Database database, Relation<V> relation) Run the algorithm
Clustering<MeanModel<V>>	NaiveMeanShiftClustering.run(Database database, Relation<V> relation) Run the mean-shift clustering algorithm.
Clustering<EMModel<V>>	EM.run(Database database, Relation<V> relation) Performs the EM clustering algorithm on the given database.
Clustering<Model>	DBSCAN.run(Relation<O> relation) Performs the DBSCAN algorithm on the given database.

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

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.affinitypropagation with parameters of type Relation

Modifier and Type	Method and Description
double[][]	DistanceBasedInitializationWithMedian.getSimilarityMatrix(Database db, Relation<O> relation, ArrayDBIDs ids)
double[][]	AffinityPropagationInitialization.getSimilarityMatrix(Database db, Relation<O> relation, ArrayDBIDs ids) Compute the initial similarity matrix.
double[][]	SimilarityBasedInitializationWithMedian.getSimilarityMatrix(Database db, Relation<O> relation, ArrayDBIDs ids)
Clustering<MedoidModel>	AffinityPropagationClusteringAlgorithm.run(Database db, Relation<O> relation) Perform affinity propagation clustering.

Uses of Relation in de.lmu.ifi.dbs.elki.algorithm.clustering.biclustering

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

Modifier and Type	Field and Description
protected Relation<V>	AbstractBiclustering.relation Relation we use.

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.biclustering that return Relation

Modifier and Type	Method and Description
Relation<V>	AbstractBiclustering.getRelation() Getter for the relation.

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.biclustering with parameters of type Relation

Modifier and Type	Method and Description
Clustering<M>	AbstractBiclustering.run(Relation<V> relation) Prepares the algorithm for running on a specific database.

Uses of Relation in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation

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

Modifier and Type	Field and Description
private Relation<ParameterizationFunction>	CASH.fulldatabase The entire relation.

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation that return Relation

Modifier and Type	Method and Description
private Relation<ParameterizationFunction>	CASH.preprocess(Database db, Relation<V> vrel) Preprocess the dataset, precomputing the parameterization functions.

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation with parameters of type Relation

Modifier and Type	Method and Description
private void	ORCLUS.assign(Relation<V> database, DistanceQuery<V,DoubleDistance> distFunc, List<ORCLUS.ORCLUSCluster> clusters) Creates a partitioning of the database by assigning each object to its closest seed.
private MaterializedRelation<ParameterizationFunction>	CASH.buildDB(int dim, Matrix basis, DBIDs ids, Relation<ParameterizationFunction> relation) Builds a dim-1 dimensional database where the objects are projected into the specified subspace.
private Database	CASH.buildDerivatorDB(Relation<ParameterizationFunction> relation, CASHInterval interval) Builds a database for the derivator consisting of the ids in the specified interval.
private Database	CASH.buildDerivatorDB(Relation<ParameterizationFunction> relation, DBIDs ids) Builds a database for the derivator consisting of the ids in the specified interval.
private double[]	CASH.determineMinMaxDistance(Relation<ParameterizationFunction> relation, int dimensionality) Determines the minimum and maximum function value of all parameterization functions stored in the specified database.
private static int	CASH.dimensionality(Relation<ParameterizationFunction> relation) Get the dimensionality of a vector field.
private Clustering<Model>	CASH.doRun(Relation<ParameterizationFunction> relation, FiniteProgress progress) Runs the CASH algorithm on the specified database, this method is recursively called until only noise is left.
private List<List<Cluster<CorrelationModel<V>>>>	ERiC.extractCorrelationClusters(Clustering<Model> copacResult, Relation<V> database, int dimensionality) Extracts the correlation clusters and noise from the copac result and returns a mapping of correlation dimension to maps of clusters within this correlation dimension.
private Matrix	ORCLUS.findBasis(Relation<V> database, DistanceQuery<V,DoubleDistance> distFunc, ORCLUS.ORCLUSCluster cluster, int dim) Finds the basis of the subspace of dimensionality dim for the specified cluster.
private LMCLUS.Separation	LMCLUS.findSeparation(Relation<NumberVector<?>> relation, DBIDs currentids, int dimension, Random r) This method samples a number of linear manifolds an tries to determine which the one with the best cluster is.
private void	CASH.initHeap(ObjectHeap<IntegerPriorityObject<CASHInterval>> heap, Relation<ParameterizationFunction> relation, int dim, DBIDs ids) Initializes the heap with the root intervals.
private List<ORCLUS.ORCLUSCluster>	ORCLUS.initialSeeds(Relation<V> database, int k) Initializes the list of seeds wit a random sample of size k.
private void	ORCLUS.merge(Relation<V> database, DistanceQuery<V,DoubleDistance> distFunc, List<ORCLUS.ORCLUSCluster> clusters, int k_new, int d_new, IndefiniteProgress cprogress) Reduces the number of seeds to k_new
private Relation<ParameterizationFunction>	CASH.preprocess(Database db, Relation<V> vrel) Preprocess the dataset, precomputing the parameterization functions.
private ORCLUS.ProjectedEnergy	ORCLUS.projectedEnergy(Relation<V> database, DistanceQuery<V,DoubleDistance> distFunc, ORCLUS.ORCLUSCluster c_i, ORCLUS.ORCLUSCluster c_j, int i, int j, int dim) Computes the projected energy of the specified clusters.
Clustering<Model>	LMCLUS.run(Database database, Relation<NumberVector<?>> relation) The main LMCLUS (Linear manifold clustering algorithm) is processed in this method.
Clustering<Model>	CASH.run(Database database, Relation<V> vrel) Run CASH on the relation.
Clustering<Model>	ORCLUS.run(Database database, Relation<V> relation) Performs the ORCLUS algorithm on the given database.
Clustering<Model>	COPAC.run(Relation<V> relation) Performs the COPAC algorithm on the given database.
Clustering<CorrelationModel<V>>	ERiC.run(Relation<V> relation) Performs the ERiC algorithm on the given database.
private Matrix	CASH.runDerivator(Relation<ParameterizationFunction> relation, int dim, CASHInterval interval, ModifiableDBIDs ids) Runs the derivator on the specified interval and assigns all points having a distance less then the standard deviation of the derivator model to the model to this model.
private LinearEquationSystem	CASH.runDerivator(Relation<ParameterizationFunction> relation, int dimensionality, DBIDs ids) Runs the derivator on the specified interval and assigns all points having a distance less then the standard deviation of the derivator model to the model to this model.
private Clustering<Model>	COPAC.runPartitionAlgorithm(Relation<V> relation, Map<Integer,DBIDs> partitionMap, DistanceQuery<V,D> query) Runs the partition algorithm and creates the result.
private ORCLUS.ORCLUSCluster	ORCLUS.union(Relation<V> relation, DistanceQuery<V,DoubleDistance> distFunc, ORCLUS.ORCLUSCluster c1, ORCLUS.ORCLUSCluster c2, int dim) Returns the union of the two specified clusters.

Uses of Relation in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.cash

Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.cash declared as Relation

Modifier and Type	Field and Description
private Relation<ParameterizationFunction>	CASHIntervalSplit.database The database storing the parameterization functions.

Constructors in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.cash with parameters of type Relation

Constructor and Description
CASHIntervalSplit(Relation<ParameterizationFunction> database, int minPts) Initializes the logger and sets the debug status to the given value.

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

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical with parameters of type Relation

Modifier and Type	Method and Description
PointerHierarchyRepresentationResult<D>	SLINK.run(Database database, Relation<O> relation) Performs the SLINK algorithm on the given database.
PointerHierarchyRepresentationResult<DoubleDistance>	NaiveAgglomerativeHierarchicalClustering.run(Database db, Relation<O> relation) Run the algorithm
private void	SLINK.step2double(DBIDRef id, DBIDs processedIDs, Relation<? extends O> relation, PrimitiveDoubleDistanceFunction<? super O> distFunc, WritableDoubleDistanceDataStore m) Second step: Determine the pairwise distances from all objects in the pointer representation to the new object with the specified id.

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

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

Modifier and Type	Method and Description
protected boolean	KMeansBatchedLloyd.assignToNearestCluster(Relation<V> relation, DBIDs ids, List<? extends NumberVector<?>> oldmeans, double[][] meanshift, int[] changesize, List<? extends ModifiableDBIDs> clusters, WritableIntegerDataStore assignment) Returns a list of clusters.
protected boolean	AbstractKMeans.assignToNearestCluster(Relation<V> relation, List<? extends NumberVector<?>> means, List<? extends ModifiableDBIDs> clusters, WritableIntegerDataStore assignment) Returns a list of clusters.
List<V>	KMeansInitialization.chooseInitialMeans(Database database, Relation<V> relation, int k, PrimitiveDistanceFunction<? super NumberVector<?>,?> distanceFunction) Choose initial means
List<V>	RandomlyChosenInitialMeans.chooseInitialMeans(Database database, Relation<V> relation, int k, PrimitiveDistanceFunction<? super NumberVector<?>,?> distanceFunction)
List<V>	RandomlyGeneratedInitialMeans.chooseInitialMeans(Database database, Relation<V> relation, int k, PrimitiveDistanceFunction<? super NumberVector<?>,?> distanceFunction)
List<V>	FirstKInitialMeans.chooseInitialMeans(Database database, Relation<V> relation, int k, PrimitiveDistanceFunction<? super NumberVector<?>,?> distanceFunction)
List<V>	KMeansPlusPlusInitialMeans.chooseInitialMeans(Database database, Relation<V> relation, int k, PrimitiveDistanceFunction<? super NumberVector<?>,?> distanceFunction)
List<V>	SampleKMeansInitialization.chooseInitialMeans(Database database, Relation<V> relation, int k, PrimitiveDistanceFunction<? super NumberVector<?>,?> distanceFunction)
List<V>	PAMInitialMeans.chooseInitialMeans(Database database, Relation<V> relation, int k, PrimitiveDistanceFunction<? super NumberVector<?>,?> distanceFunction)
List<V>	FarthestPointsInitialMeans.chooseInitialMeans(Database database, Relation<V> relation, int k, PrimitiveDistanceFunction<? super NumberVector<?>,?> distanceFunction)
protected boolean	AbstractKMeans.macQueenIterate(Relation<V> relation, List<Vector> means, List<ModifiableDBIDs> clusters, WritableIntegerDataStore assignment) Perform a MacQueen style iteration.
protected List<Vector>	AbstractKMeans.means(List<? extends ModifiableDBIDs> clusters, List<? extends NumberVector<?>> means, Relation<V> database) Returns the mean vectors of the given clusters in the given database.
protected List<NumberVector<?>>	AbstractKMeans.medians(List<? extends ModifiableDBIDs> clusters, List<? extends NumberVector<?>> medians, Relation<V> database) Returns the median vectors of the given clusters in the given database.
Clustering<KMeansModel<V>>	KMeansBatchedLloyd.run(Database database, Relation<V> relation)
Clustering<MedoidModel>	KMedoidsPAM.run(Database database, Relation<V> relation) Run k-medoids
Clustering<M>	KMeansBisecting.run(Database database, Relation<V> relation)
Clustering<M>	KMeans.run(Database database, Relation<V> rel) Run the clustering algorithm.
Clustering<KMeansModel<V>>	KMeansMacQueen.run(Database database, Relation<V> relation)
Clustering<MedoidModel>	KMedoidsEM.run(Database database, Relation<V> relation) Run k-medoids
Clustering<KMeansModel<V>>	KMeansHybridLloydMacQueen.run(Database database, Relation<V> relation)
Clustering<KMeansModel<V>>	KMeansLloyd.run(Database database, Relation<V> relation)
Clustering<M>	BestOfMultipleKMeans.run(Database database, Relation<V> relation)
Clustering<MeanModel<V>>	KMediansLloyd.run(Database database, Relation<V> relation)
protected double	KMeansPlusPlusInitialMeans.updateWeights(double[] weights, ArrayDBIDs ids, DBID latest, PrimitiveDoubleDistanceFunction<V> distF, Relation<V> rel) Update the weight list.

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

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

Modifier and Type	Method and Description
<V extends O> double	KMeansQualityMeasure.calculateCost(Clustering<? extends MeanModel<V>> clustering, PrimitiveDistanceFunction<? super V,? extends D> distanceFunction, Relation<V> relation) Calculates and returns the quality measure.
<V extends NumberVector<?>> double	WithinClusterVarianceQualityMeasure.calculateCost(Clustering<? extends MeanModel<V>> clustering, PrimitiveDistanceFunction<? super V,? extends NumberDistance<?,?>> distanceFunction, Relation<V> relation)
<V extends NumberVector<?>> double	WithinClusterMeanDistanceQualityMeasure.calculateCost(Clustering<? extends MeanModel<V>> clustering, PrimitiveDistanceFunction<? super V,? extends NumberDistance<?,?>> distanceFunction, Relation<V> relation)

Uses of Relation in de.lmu.ifi.dbs.elki.algorithm.clustering.onedimensional

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.onedimensional with parameters of type Relation

Modifier and Type	Method and Description
Clustering<ClusterModel>	KNNKernelDensityMinimaClustering.run(Relation<V> relation) Run the clustering algorithm on a data relation.

Uses of Relation in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace with parameters of type Relation

Modifier and Type	Method and Description
private Map<DBID,PROCLUS.PROCLUSCluster>	PROCLUS.assignPoints(Map<DBID,gnu.trove.set.TIntSet> dimensions, Relation<V> database) Assigns the objects to the clusters.
private void	P3C.assignUnassigned(Relation<V> relation, WritableDataStore<double[]> probClusterIGivenX, Vector[] means, Matrix[] invCovMatr, double[] clusterWeights, ModifiableDBIDs unassigned) Assign unassigned objects to best candidate based on shortest Mahalanobis distance.
private double	PROCLUS.avgDistance(V centroid, DBIDs objectIDs, Relation<V> database, int dimension) Computes the average distance of the objects to the centroid along the specified dimension.
private void	DiSH.buildHierarchy(Relation<V> database, DiSHDistanceFunction.Instance<V> distFunc, Clustering<SubspaceModel<V>> clustering, List<Cluster<SubspaceModel<V>>> clusters, int dimensionality) Builds the cluster hierarchy.
private void	DiSH.checkClusters(Relation<V> database, DiSHDistanceFunction.Instance<V> distFunc, Map<BitSet,List<Pair<BitSet,ArrayModifiableDBIDs>>> clustersMap, int minpts) Removes the clusters with size < minpts from the cluster map and adds them to their parents.
private Clustering<SubspaceModel<V>>	DiSH.computeClusters(Relation<V> database, ClusterOrderResult<PreferenceVectorBasedCorrelationDistance> clusterOrder, DiSHDistanceFunction.Instance<V> distFunc) Computes the hierarchical clusters according to the cluster order.
private void	P3C.computeFuzzyMembership(Relation<V> relation, ArrayList<P3C.Signature> clusterCores, ModifiableDBIDs unassigned, WritableDataStore<double[]> probClusterIGivenX, double[] clusterWeights) Computes a fuzzy membership with the weights based on which cluster cores each data point is part of.
private boolean	DOC.dimensionIsRelevant(int dimension, Relation<V> relation, DBIDs points) Utility method to test if a given dimension is relevant as determined via a set of reference points (i.e. if the variance along the attribute is lower than the threshold).
private double	PROCLUS.evaluateClusters(Map<DBID,PROCLUS.PROCLUSCluster> clusters, Map<DBID,gnu.trove.set.TIntSet> dimensions, Relation<V> database) Evaluates the quality of the clusters.
private Map<BitSet,List<Pair<BitSet,ArrayModifiableDBIDs>>>	DiSH.extractClusters(Relation<V> database, DiSHDistanceFunction.Instance<V> distFunc, ClusterOrderResult<PreferenceVectorBasedCorrelationDistance> clusterOrder) Extracts the clusters from the cluster order.
private List<PROCLUS.PROCLUSCluster>	PROCLUS.finalAssignment(List<Pair<V,gnu.trove.set.TIntSet>> dimensions, Relation<V> database) Refinement step to assign the objects to the final clusters.
private List<CLIQUESubspace<V>>	CLIQUE.findDenseSubspaceCandidates(Relation<V> database, List<CLIQUESubspace<V>> denseSubspaces) Determines the k-dimensional dense subspace candidates from the specified (k-1)-dimensional dense subspaces.
private List<CLIQUESubspace<V>>	CLIQUE.findDenseSubspaces(Relation<V> database, List<CLIQUESubspace<V>> denseSubspaces) Determines the k-dimensional dense subspaces and performs a pruning if this option is chosen.
private Map<DBID,gnu.trove.set.TIntSet>	PROCLUS.findDimensions(DBIDs medoids, Relation<V> database, DistanceQuery<V,DoubleDistance> distFunc, RangeQuery<V,DoubleDistance> rangeQuery) Determines the set of correlated dimensions for each medoid in the specified medoid set.
private List<Pair<V,gnu.trove.set.TIntSet>>	PROCLUS.findDimensions(List<PROCLUS.PROCLUSCluster> clusters, Relation<V> database) Refinement step that determines the set of correlated dimensions for each cluster centroid.
private List<CLIQUESubspace<V>>	CLIQUE.findOneDimensionalDenseSubspaceCandidates(Relation<V> database) Determines the one-dimensional dense subspace candidates by making a pass over the database.
private List<CLIQUESubspace<V>>	CLIQUE.findOneDimensionalDenseSubspaces(Relation<V> database) Determines the one dimensional dense subspaces and performs a pruning if this option is chosen.
private void	P3C.findOutliers(Relation<V> relation, Vector[] means, Matrix[] invCovMatr, ArrayList<P3C.ClusterCandidate> clusterCandidates, int nonUniformDimensionCount, ModifiableDBIDs noise) Performs outlier detection by testing the Mahalanobis distance of each point in a cluster against the critical value of the ChiSquared distribution with as many degrees of freedom as the cluster has relevant attributes.
private Pair<BitSet,ArrayModifiableDBIDs>	DiSH.findParent(Relation<V> database, DiSHDistanceFunction.Instance<V> distFunc, Pair<BitSet,ArrayModifiableDBIDs> child, Map<BitSet,List<Pair<BitSet,ArrayModifiableDBIDs>>> clustersMap) Returns the parent of the specified cluster
private Map<DBID,DistanceDBIDList<DoubleDistance>>	PROCLUS.getLocalities(DBIDs medoids, Relation<V> database, DistanceQuery<V,DoubleDistance> distFunc, RangeQuery<V,DoubleDistance> rangeQuery) Computes the localities of the specified medoids: for each medoid m the objects in the sphere centered at m with radius minDist are determined, where minDist is the minimum distance between medoid m and any other medoid m_i.
private Collection<CLIQUEUnit<V>>	CLIQUE.initOneDimensionalUnits(Relation<V> database) Initializes and returns the one dimensional units.
private boolean	DiSH.isParent(Relation<V> database, DiSHDistanceFunction.Instance<V> distFunc, Cluster<SubspaceModel<V>> parent, Hierarchy.Iter<Cluster<SubspaceModel<V>>> iter) Returns true, if the specified parent cluster is a parent of one child of the children clusters.
private Cluster<SubspaceModel<V>>	DOC.makeCluster(Relation<V> relation, DBIDs C, BitSet D) Utility method to create a subspace cluster from a list of DBIDs and the relevant attributes.
private SetDBIDs[][]	P3C.partitionData(Relation<V> relation, int bins) Partition the data set into bins bins in each dimension independently.
Clustering<SubspaceModel<V>>	DOC.run(Database database, Relation<V> relation) Performs the DOC or FastDOC (as configured) algorithm on the given Database.
Clustering<SubspaceModel<V>>	DiSH.run(Database database, Relation<V> relation) Performs the DiSH algorithm on the given database.
Clustering<SubspaceModel<V>>	PROCLUS.run(Database database, Relation<V> relation) Performs the PROCLUS algorithm on the given database.
Clustering<SubspaceModel<V>>	P3C.run(Database database, Relation<V> relation) Performs the P3C algorithm on the given Database.
Clustering<SubspaceModel<V>>	SUBCLU.run(Relation<V> relation) Performs the SUBCLU algorithm on the given database.
Clustering<SubspaceModel<V>>	CLIQUE.run(Relation<V> relation) Performs the CLIQUE algorithm on the given database.
private List<Cluster<Model>>	SUBCLU.runDBSCAN(Relation<V> relation, DBIDs ids, Subspace subspace) Runs the DBSCAN algorithm on the specified partition of the database in the given subspace.
private Cluster<SubspaceModel<V>>	DOC.runDOC(Relation<V> relation, ArrayModifiableDBIDs S, int d, int n, int m, int r, int minClusterSize) Performs a single run of DOC, finding a single cluster.
private Cluster<SubspaceModel<V>>	DOC.runFastDOC(Relation<V> relation, ArrayModifiableDBIDs S, int d, int n, int m, int r) Performs a single run of FastDOC, finding a single cluster.
private List<Cluster<SubspaceModel<V>>>	DiSH.sortClusters(Relation<V> database, Map<BitSet,List<Pair<BitSet,ArrayModifiableDBIDs>>> clustersMap) Returns a sorted list of the clusters w.r.t. the subspace dimensionality in descending order.

Uses of Relation in de.lmu.ifi.dbs.elki.algorithm.clustering.trivial

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.trivial with parameters of type Relation

Modifier and Type	Method and Description
private HashMap<String,DBIDs>	ByLabelClustering.multipleAssignment(Relation<?> data) Assigns the objects of the database to multiple clusters according to their labels.
Clustering<Model>	ByLabelHierarchicalClustering.run(Relation<?> relation) Run the actual clustering algorithm.
Clustering<Model>	TrivialAllNoise.run(Relation<?> relation)
Clustering<Model>	ByLabelClustering.run(Relation<?> relation) Run the actual clustering algorithm.
Clustering<Model>	TrivialAllInOne.run(Relation<?> relation)
Clustering<Model>	ByModelClustering.run(Relation<Model> relation) Run the actual clustering algorithm.
private HashMap<String,DBIDs>	ByLabelClustering.singleAssignment(Relation<?> data) Assigns the objects of the database to single clusters according to their labels.

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

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

Modifier and Type	Field and Description
(package private) Relation<O>	HilOut.HilbertFeatures.relation Relation indexed

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

Modifier and Type	Method and Description
protected ArrayList<ArrayList<DBIDs>>	AbstractAggarwalYuOutlier.buildRanges(Relation<V> relation) Grid discretization of the data: Each attribute of data is divided into phi equi-depth ranges.
protected double	ABOD.computeABOF(Relation<V> relation, KernelMatrix kernelMatrix, DBIDRef pA, MeanVariance s) Compute the exact ABOF value.
protected DistanceDBIDList<D>	ReferenceBasedOutlierDetection.computeDistanceVector(V refPoint, Relation<V> database, DistanceQuery<V,D> distFunc) Computes for each object the distance to one reference point.
protected DoubleDataStore	DBOutlierScore.computeOutlierScores(Database database, Relation<O> relation, D d)
protected DoubleDataStore	DBOutlierDetection.computeOutlierScores(Database database, Relation<O> relation, D neighborhoodSize)
protected abstract DoubleDataStore	AbstractDBOutlier.computeOutlierScores(Database database, Relation<O> relation, D d) computes an outlier score for each object of the database.
private double	GaussianUniformMixture.loglikelihoodNormal(DBIDs objids, Relation<V> database) Computes the loglikelihood of all normal objects.
OutlierResult	ODIN.run(Database database, Relation<O> relation) Run the ODIN algorithm
OutlierResult	KNNOutlier.run(Database database, Relation<O> relation) Runs the algorithm in the timed evaluation part.
OutlierResult	DWOF.run(Database database, Relation<O> relation) Performs the Generalized DWOF_SCORE algorithm on the given database by calling all the other methods in the proper order.
OutlierResult	OPTICSOF.run(Database database, Relation<O> relation) Perform OPTICS-based outlier detection.
OutlierResult	AbstractDBOutlier.run(Database database, Relation<O> relation) Runs the algorithm in the timed evaluation part.
OutlierResult	HilOut.run(Database database, Relation<O> relation)
OutlierResult	KNNWeightOutlier.run(Database database, Relation<O> relation) Runs the algorithm in the timed evaluation part.
OutlierResult	FastABOD.run(Database db, Relation<V> relation) Run Fast-ABOD on the data set.
OutlierResult	LBABOD.run(Database db, Relation<V> relation) Run LB-ABOD on the data set.
OutlierResult	AggarwalYuEvolutionary.run(Database database, Relation<V> relation) Performs the evolutionary algorithm on the given database.
OutlierResult	ReferenceBasedOutlierDetection.run(Database database, Relation<V> relation) Run the algorithm on the given relation.
OutlierResult	ABOD.run(Database db, Relation<V> relation) Run ABOD on the data set.
OutlierResult	SimpleCOP.run(Database database, Relation<V> data)
OutlierResult	EMOutlier.run(Database database, Relation<V> relation) Runs the algorithm in the timed evaluation part.
OutlierResult	COP.run(Relation<V> relation) Process a single relation.
OutlierResult	GaussianUniformMixture.run(Relation<V> relation) Run the algorithm
OutlierResult	AggarwalYuNaive.run(Relation<V> relation) Run the algorithm on the given relation.
OutlierResult	GaussianModel.run(Relation<V> relation) Run the algorithm

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

Constructor and Description
AggarwalYuEvolutionary.EvolutionarySearch(Relation<V> relation, ArrayList<ArrayList<DBIDs>> ranges, int m, Random random) Constructor.
HilOut.HilbertFeatures(Relation<O> relation, double[] min, double diameter) Constructor.

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

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

Modifier and Type	Field and Description
private Relation<? extends NumberVector<?>>	ALOCI.ALOCIQuadTree.relation Relation indexed.

Methods in de.lmu.ifi.dbs.elki.algorithm.outlier.lof with parameters of type Relation

Modifier and Type	Method and Description
private Pair<Pair<KNNQuery<O,D>,KNNQuery<O,D>>,Pair<RKNNQuery<O,D>,RKNNQuery<O,D>>>	OnlineLOF.getKNNAndRkNNQueries(Database database, Relation<O> relation, StepProgress stepprog) Get the kNN and rkNN queries for the algorithm.
private Pair<KNNQuery<O,D>,KNNQuery<O,D>>	FlexibleLOF.getKNNQueries(Database database, Relation<O> relation, StepProgress stepprog) Get the kNN queries for the algorithm.
protected Pair<KNNQuery<O,D>,KNNQuery<O,D>>	LoOP.getKNNQueries(Database database, Relation<O> relation, StepProgress stepprog) Get the kNN queries for the algorithm.
OutlierResult	FlexibleLOF.run(Database database, Relation<O> relation) Performs the Generalized LOF algorithm on the given database by calling FlexibleLOF.doRunInTime(de.lmu.ifi.dbs.elki.database.ids.DBIDs, de.lmu.ifi.dbs.elki.database.query.knn.KNNQuery<O, D>, de.lmu.ifi.dbs.elki.database.query.knn.KNNQuery<O, D>, de.lmu.ifi.dbs.elki.logging.progress.StepProgress).
OutlierResult	OnlineLOF.run(Database database, Relation<O> relation) Performs the Generalized LOF_SCORE algorithm on the given database by calling #doRunInTime(Database) and adds a OnlineLOF.LOFKNNListener to the preprocessors.
OutlierResult	LOCI.run(Database database, Relation<O> relation) Run the algorithm
OutlierResult	LDF.run(Database database, Relation<O> relation) Run the naive kernel density LOF algorithm.
OutlierResult	SimplifiedLOF.run(Database database, Relation<O> relation) Run the Simple LOF algorithm.
OutlierResult	LoOP.run(Database database, Relation<O> relation) Performs the LoOP algorithm on the given database.
OutlierResult	INFLO.run(Database database, Relation<O> relation) Run the algorithm
OutlierResult	ALOCI.run(Database database, Relation<O> relation)
OutlierResult	LOF.run(Database database, Relation<O> relation) Performs the Generalized LOF_SCORE algorithm on the given database.
OutlierResult	LDOF.run(Database database, Relation<O> relation) Run the algorithm
OutlierResult	SimpleKernelDensityLOF.run(Database database, Relation<O> relation) Run the naive kernel density LOF algorithm.

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

Constructor and Description
ALOCI.ALOCIQuadTree(double[] min, double[] max, double[] shift, int nmin, Relation<? extends NumberVector<?>> relation) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.algorithm.outlier.meta

Methods in de.lmu.ifi.dbs.elki.algorithm.outlier.meta with parameters of type Relation

Modifier and Type	Method and Description
private ArrayList<ArrayDBIDs>	HiCS.buildOneDimIndexes(Relation<? extends NumberVector<?>> relation) Calculates "index structures" for every attribute, i.e. sorts a ModifiableArray of every DBID in the database for every dimension and stores them in a list
private void	HiCS.calculateContrast(Relation<? extends NumberVector<?>> relation, HiCS.HiCSSubspace subspace, ArrayList<ArrayDBIDs> subspaceIndex, Random random) Calculates the actual contrast of a given subspace.
private Set<HiCS.HiCSSubspace>	HiCS.calculateSubspaces(Relation<? extends NumberVector<?>> relation, ArrayList<ArrayDBIDs> subspaceIndex, Random random) Identifies high contrast subspaces in a given full-dimensional database.
OutlierResult	ExternalDoubleOutlierScore.run(Database database, Relation<?> relation) Run the algorithm.
OutlierResult	FeatureBagging.run(Database database, Relation<NumberVector<?>> relation) Run the algorithm on a data set.
OutlierResult	HiCS.run(Relation<V> relation) Perform HiCS on a given database.

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

Methods in de.lmu.ifi.dbs.elki.algorithm.outlier.spatial with parameters of type Relation

Modifier and Type	Method and Description
OutlierResult	TrimmedMeanApproach.run(Database database, Relation<N> nrel, Relation<? extends NumberVector<?>> relation) Run the algorithm.
OutlierResult	TrimmedMeanApproach.run(Database database, Relation<N> nrel, Relation<? extends NumberVector<?>> relation) Run the algorithm.
OutlierResult	CTLuZTestOutlier.run(Database database, Relation<N> nrel, Relation<? extends NumberVector<?>> relation) Main method.
OutlierResult	CTLuZTestOutlier.run(Database database, Relation<N> nrel, Relation<? extends NumberVector<?>> relation) Main method.
OutlierResult	SOF.run(Database database, Relation<N> spatial, Relation<O> relation) The main run method
OutlierResult	SOF.run(Database database, Relation<N> spatial, Relation<O> relation) The main run method
OutlierResult	SLOM.run(Database database, Relation<N> spatial, Relation<O> relation)
OutlierResult	SLOM.run(Database database, Relation<N> spatial, Relation<O> relation)
OutlierResult	CTLuGLSBackwardSearchAlgorithm.run(Database database, Relation<V> relationx, Relation<? extends NumberVector<?>> relationy) Run the algorithm
OutlierResult	CTLuGLSBackwardSearchAlgorithm.run(Database database, Relation<V> relationx, Relation<? extends NumberVector<?>> relationy) Run the algorithm
OutlierResult	CTLuMoranScatterplotOutlier.run(Relation<N> nrel, Relation<? extends NumberVector<?>> relation) Main method.
OutlierResult	CTLuMoranScatterplotOutlier.run(Relation<N> nrel, Relation<? extends NumberVector<?>> relation) Main method.
OutlierResult	CTLuMedianAlgorithm.run(Relation<N> nrel, Relation<? extends NumberVector<?>> relation) Main method.
OutlierResult	CTLuMedianAlgorithm.run(Relation<N> nrel, Relation<? extends NumberVector<?>> relation) Main method.
OutlierResult	CTLuRandomWalkEC.run(Relation<N> spatial, Relation<? extends NumberVector<?>> relation) Run the algorithm.
OutlierResult	CTLuRandomWalkEC.run(Relation<N> spatial, Relation<? extends NumberVector<?>> relation) Run the algorithm.
OutlierResult	CTLuScatterplotOutlier.run(Relation<N> nrel, Relation<? extends NumberVector<?>> relation) Main method.
OutlierResult	CTLuScatterplotOutlier.run(Relation<N> nrel, Relation<? extends NumberVector<?>> relation) Main method.
OutlierResult	CTLuMedianMultipleAttributes.run(Relation<N> spatial, Relation<O> attributes) Run the algorithm
OutlierResult	CTLuMedianMultipleAttributes.run(Relation<N> spatial, Relation<O> attributes) Run the algorithm
OutlierResult	CTLuMeanMultipleAttributes.run(Relation<N> spatial, Relation<O> attributes)
OutlierResult	CTLuMeanMultipleAttributes.run(Relation<N> spatial, Relation<O> attributes)
private Pair<DBIDVar,Double>	CTLuGLSBackwardSearchAlgorithm.singleIteration(Relation<V> relationx, Relation<? extends NumberVector<?>> relationy) Run a single iteration of the GLS-SOD modeling step
private Pair<DBIDVar,Double>	CTLuGLSBackwardSearchAlgorithm.singleIteration(Relation<V> relationx, Relation<? extends NumberVector<?>> relationy) Run a single iteration of the GLS-SOD modeling step

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

Methods in de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood with parameters of type Relation

Modifier and Type	Method and Description
private DataStore<DBIDs>	ExtendedNeighborhood.Factory.extendNeighborhood(Relation<? extends O> database) Method to load the external neighbors.
NeighborSetPredicate	ExternalNeighborhood.Factory.instantiate(Relation<?> relation)
NeighborSetPredicate	NeighborSetPredicate.Factory.instantiate(Relation<? extends O> relation) Instantiation method.
NeighborSetPredicate	ExtendedNeighborhood.Factory.instantiate(Relation<? extends O> database)
NeighborSetPredicate	PrecomputedKNearestNeighborNeighborhood.Factory.instantiate(Relation<? extends O> relation)
private DataStore<DBIDs>	ExternalNeighborhood.Factory.loadNeighbors(Database database, Relation<?> relation) Method to load the external neighbors.

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

Methods in de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood.weighted with parameters of type Relation

Modifier and Type	Method and Description
LinearWeightedExtendedNeighborhood	LinearWeightedExtendedNeighborhood.Factory.instantiate(Relation<? extends O> database)
UnweightedNeighborhoodAdapter	UnweightedNeighborhoodAdapter.Factory.instantiate(Relation<? extends O> relation)
WeightedNeighborSetPredicate	WeightedNeighborSetPredicate.Factory.instantiate(Relation<? extends O> relation) Instantiation method.

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

Fields in de.lmu.ifi.dbs.elki.algorithm.outlier.subspace declared as Relation

Modifier and Type	Field and Description
(package private) Relation<V>	OUTRES.KernelDensityEstimator.relation Relation to retrieve data from

Methods in de.lmu.ifi.dbs.elki.algorithm.outlier.subspace with parameters of type Relation

Modifier and Type	Method and Description
private static double[]	SOD.computePerDimensionVariances(Relation<? extends NumberVector<?>> relation, Vector center, DBIDs neighborhood) Compute the per-dimension variances for the given neighborhood and center.
private DBIDs	SOD.getNearestNeighbors(Relation<V> relation, SimilarityQuery<V,D> simQ, DBIDRef queryObject) Provides the k nearest neighbors in terms of the shared nearest neighbor distance.
OutlierResult	SOD.run(Relation<V> relation) Performs the SOD algorithm on the given database.
OutlierResult	OUTRES.run(Relation<V> relation) Main loop for OUTRES

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

Constructor and Description
OUTRES.KernelDensityEstimator(Relation<V> relation) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.algorithm.outlier.trivial

Methods in de.lmu.ifi.dbs.elki.algorithm.outlier.trivial with parameters of type Relation

Modifier and Type	Method and Description
OutlierResult	ByLabelOutlier.run(Relation<?> relation) Run the algorithm
OutlierResult	TrivialNoOutlier.run(Relation<?> relation) Run the actual algorithm.
OutlierResult	TrivialAllOutlier.run(Relation<?> relation) Run the actual algorithm.
OutlierResult	TrivialAverageCoordinateOutlier.run(Relation<? extends NumberVector<?>> relation) Run the actual algorithm.
OutlierResult	TrivialGeneratedOutlier.run(Relation<Model> models, Relation<NumberVector<?>> vecs, Relation<?> labels) Run the algorithm
OutlierResult	TrivialGeneratedOutlier.run(Relation<Model> models, Relation<NumberVector<?>> vecs, Relation<?> labels) Run the algorithm
OutlierResult	TrivialGeneratedOutlier.run(Relation<Model> models, Relation<NumberVector<?>> vecs, Relation<?> labels) Run the algorithm

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

Methods in de.lmu.ifi.dbs.elki.algorithm.statistics with parameters of type Relation

Modifier and Type	Method and Description
private DoubleMinMax	DistanceStatisticsWithClasses.exactMinMax(Relation<O> relation, DistanceQuery<O,D> distFunc) Compute the exact maximum and minimum.
HistogramResult<DoubleVector>	RankingQualityHistogram.run(Database database, Relation<O> relation) Process a database
CollectionResult<DoubleVector>	AveragePrecisionAtK.run(Database database, Relation<V> relation, Relation<ClassLabel> lrelation) Run the algorithm
CollectionResult<DoubleVector>	AveragePrecisionAtK.run(Database database, Relation<V> relation, Relation<ClassLabel> lrelation) Run the algorithm
private ScalesResult	AddSingleScale.run(Relation<? extends NumberVector<?>> rel) Add scales to a single vector relation.
private DoubleMinMax	DistanceStatisticsWithClasses.sampleMinMax(Relation<O> relation, DistanceQuery<O,D> distFunc) Estimate minimum and maximum via sampling.

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

Methods in de.lmu.ifi.dbs.elki.application.greedyensemble that return Relation

Modifier and Type	Method and Description
static Relation<NumberVector<?>>	GreedyEnsembleExperiment.applyPrescaling(ScalingFunction scaling, Relation<NumberVector<?>> relation, DBIDs skip) Prescale each vector (except when in skip) with the given scaling function.

Methods in de.lmu.ifi.dbs.elki.application.greedyensemble with parameters of type Relation

Modifier and Type	Method and Description
static Relation<NumberVector<?>>	GreedyEnsembleExperiment.applyPrescaling(ScalingFunction scaling, Relation<NumberVector<?>> relation, DBIDs skip) Prescale each vector (except when in skip) with the given scaling function.

Uses of Relation in de.lmu.ifi.dbs.elki.data

Fields in de.lmu.ifi.dbs.elki.data declared as Relation

Modifier and Type	Field and Description
private Relation<? extends NumberVector<?>>	VectorUtil.SortDBIDsBySingleDimension.data The relation to sort.

Methods in de.lmu.ifi.dbs.elki.data with parameters of type Relation

Modifier and Type	Method and Description
static Vector	VectorUtil.computeMedoid(Relation<? extends NumberVector<?>> relation, DBIDs sample) Compute medoid for a given subset.

Constructors in de.lmu.ifi.dbs.elki.data with parameters of type Relation

Constructor and Description
VectorUtil.SortDBIDsBySingleDimension(Relation<? extends NumberVector<?>> data) Constructor.
VectorUtil.SortDBIDsBySingleDimension(Relation<? extends NumberVector<?>> data, int dim) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.data.model

Constructors in de.lmu.ifi.dbs.elki.data.model with parameters of type Relation

Constructor and Description
CorrelationAnalysisSolution(LinearEquationSystem solution, Relation<V> db, Matrix strongEigenvectors, Matrix weakEigenvectors, Matrix similarityMatrix, Vector centroid) Provides a new CorrelationAnalysisSolution holding the specified matrix.
CorrelationAnalysisSolution(LinearEquationSystem solution, Relation<V> db, Matrix strongEigenvectors, Matrix weakEigenvectors, Matrix similarityMatrix, Vector centroid, NumberFormat nf) Provides a new CorrelationAnalysisSolution holding the specified matrix and number format.

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

Fields in de.lmu.ifi.dbs.elki.database with type parameters of type Relation

Modifier and Type	Field and Description
protected List<Relation<?>>	AbstractDatabase.relations The relations we manage.

Methods in de.lmu.ifi.dbs.elki.database that return Relation

Modifier and Type	Method and Description
private Relation<?>	StaticArrayDatabase.addNewRelation(SimpleTypeInformation<?> meta) Add a new representation for the given meta.
private Relation<?>	HashmapDatabase.addNewRelation(SimpleTypeInformation<?> meta) Add a new representation for the given meta.
protected Relation<?>[]	StaticArrayDatabase.alignColumns(ObjectBundle pack) Find a mapping from package columns to database columns, eventually adding new database columns when needed.
protected Relation<?>[]	HashmapDatabase.alignColumns(ObjectBundle pack) Find a mapping from package columns to database columns, eventually adding new database columns when needed.
<O> Relation<O>	Database.getRelation(TypeInformation restriction, Object... hints) Get an object representation.
<O> Relation<O>	AbstractDatabase.getRelation(TypeInformation restriction, Object... hints)

Methods in de.lmu.ifi.dbs.elki.database that return types with arguments of type Relation

Modifier and Type	Method and Description
Collection<Relation<?>>	Database.getRelations() Get all relations of a database.
Collection<Relation<?>>	AbstractDatabase.getRelations()

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

Modifier and Type	Method and Description
void	ProxyDatabase.addRelation(Relation<?> relation) Add a new representation.
<O,D extends Distance<D>> DistanceQuery<O,D>	Database.getDistanceQuery(Relation<O> relation, DistanceFunction<? super O,D> distanceFunction, Object... hints) Get the distance query for a particular distance function.
<O,D extends Distance<D>> DistanceQuery<O,D>	AbstractDatabase.getDistanceQuery(Relation<O> objQuery, DistanceFunction<? super O,D> distanceFunction, Object... hints)
static <O,D extends Distance<D>> KNNQuery<O,D>	QueryUtil.getKNNQuery(Relation<O> relation, DistanceFunction<? super O,D> distanceFunction, Object... hints) Get a KNN query object for the given distance function.
static <O,D extends Distance<D>> RangeQuery<O,D>	QueryUtil.getRangeQuery(Relation<O> relation, DistanceFunction<? super O,D> distanceFunction, Object... hints) Get a range query object for the given distance function.
static <O,D extends Distance<D>> RKNNQuery<O,D>	QueryUtil.getRKNNQuery(Relation<O> relation, DistanceFunction<? super O,D> distanceFunction, Object... hints) Get a rKNN query object for the given distance function.
<O,D extends Distance<D>> SimilarityQuery<O,D>	Database.getSimilarityQuery(Relation<O> relation, SimilarityFunction<? super O,D> similarityFunction, Object... hints) Get the similarity query for a particular similarity function.
<O,D extends Distance<D>> SimilarityQuery<O,D>	AbstractDatabase.getSimilarityQuery(Relation<O> objQuery, SimilarityFunction<? super O,D> similarityFunction, Object... hints)

Constructors in de.lmu.ifi.dbs.elki.database with parameters of type Relation

Constructor and Description
ProxyDatabase(DBIDs ids, Relation<?>... relations) Constructor.

Constructor parameters in de.lmu.ifi.dbs.elki.database with type arguments of type Relation

Constructor and Description
ProxyDatabase(DBIDs ids, Iterable<Relation<?>> relations) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.database.query

Fields in de.lmu.ifi.dbs.elki.database.query declared as Relation

Modifier and Type	Field and Description
protected Relation<? extends O>	AbstractDataBasedQuery.relation The data to use for this query

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

Modifier and Type	Method and Description
Relation<? extends O>	AbstractDataBasedQuery.getRelation() Get the queries relation.

Constructors in de.lmu.ifi.dbs.elki.database.query with parameters of type Relation

Constructor and Description
AbstractDataBasedQuery(Relation<? extends O> relation) Database this query works on.

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

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

Modifier and Type	Method and Description
Relation<? extends O>	DistanceQuery.getRelation() Access the underlying data query.

Constructors in de.lmu.ifi.dbs.elki.database.query.distance with parameters of type Relation

Constructor and Description
AbstractDatabaseDistanceQuery(Relation<? extends O> relation) Constructor.
AbstractDistanceQuery(Relation<? extends O> relation) Constructor.
DBIDDistanceQuery(Relation<DBID> relation, DBIDDistanceFunction<D> distanceFunction) Constructor.
PrimitiveDistanceQuery(Relation<? extends O> relation, PrimitiveDistanceFunction<? super O,D> distanceFunction) Constructor.
PrimitiveDistanceSimilarityQuery(Relation<? extends O> relation, PrimitiveDistanceFunction<? super O,D> distanceFunction, PrimitiveSimilarityFunction<? super O,D> similarityFunction) Constructor.
SpatialPrimitiveDistanceQuery(Relation<? extends V> relation, SpatialPrimitiveDistanceFunction<? super V,D> distanceFunction)

Uses of Relation in de.lmu.ifi.dbs.elki.database.query.knn

Methods in de.lmu.ifi.dbs.elki.database.query.knn with parameters of type Relation

Modifier and Type	Method and Description
private static <O> void	DoubleOptimizedDistanceKNNQuery.linearScan(Relation<? extends O> relation, DBIDIter iter, PrimitiveDoubleDistanceFunction<? super O> rawdist, O obj, DoubleDistanceKNNHeap heap)

Constructors in de.lmu.ifi.dbs.elki.database.query.knn with parameters of type Relation

Constructor and Description
PreprocessorKNNQuery(Relation<O> database, AbstractMaterializeKNNPreprocessor.Factory<O,D,T> preprocessor) Constructor.
PreprocessorKNNQuery(Relation<O> database, AbstractMaterializeKNNPreprocessor<O,D,T> preprocessor) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.database.query.range

Methods in de.lmu.ifi.dbs.elki.database.query.range with parameters of type Relation

Modifier and Type	Method and Description
private static <O> void	DoubleOptimizedDistanceRangeQuery.linearScan(Relation<? extends O> relation, DBIDIter iter, PrimitiveDoubleDistanceFunction<? super O> rawdist, O obj, double range, ModifiableDoubleDistanceDBIDList result)

Uses of Relation in de.lmu.ifi.dbs.elki.database.query.rknn

Constructors in de.lmu.ifi.dbs.elki.database.query.rknn with parameters of type Relation

Constructor and Description
PreprocessorRKNNQuery(Relation<O> database, MaterializeKNNAndRKNNPreprocessor.Factory<O,D> preprocessor) Constructor.
PreprocessorRKNNQuery(Relation<O> database, MaterializeKNNAndRKNNPreprocessor<O,D> preprocessor) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.database.query.similarity

Methods in de.lmu.ifi.dbs.elki.database.query.similarity that return Relation

Modifier and Type	Method and Description
Relation<? extends O>	SimilarityQuery.getRelation() Access the underlying data query.

Constructors in de.lmu.ifi.dbs.elki.database.query.similarity with parameters of type Relation

Constructor and Description
AbstractDBIDSimilarityQuery(Relation<? extends O> relation) Constructor.
AbstractSimilarityQuery(Relation<? extends O> relation) Constructor.
PrimitiveSimilarityQuery(Relation<? extends O> relation, PrimitiveSimilarityFunction<? super O,D> similarityFunction) Constructor.

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

Classes in de.lmu.ifi.dbs.elki.database.relation that implement Relation

Modifier and Type	Class and Description
class	ConvertToStringView Representation adapter that uses toString() to produce a string representation.
class	DBIDView Pseudo-representation that is the object ID itself.
class	MaterializedRelation<O> Represents a single representation.
class	ProjectedView<IN,OUT> Projected relation view (non-materialized)
class	ProxyView<O> A virtual partitioning of the database.

Fields in de.lmu.ifi.dbs.elki.database.relation declared as Relation

Modifier and Type	Field and Description
(package private) Relation<?>	ConvertToStringView.existing The database we use
private Relation<O>	ProxyView.inner The wrapped representation where we get the IDs from.
private Relation<IN>	ProjectedView.inner The wrapped representation where we get the IDs from.

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

Modifier and Type	Method and Description
static <V extends FeatureVector<?>> VectorFieldTypeInformation<V>	RelationUtil.assumeVectorField(Relation<V> relation) Get the vector field type information from a relation.
static int	RelationUtil.dimensionality(Relation<? extends FeatureVector<?>> relation) Get the dimensionality of a database relation.
static <V extends FeatureVector<?>> String	RelationUtil.getColumnLabel(Relation<? extends V> rel, int col) Get the column name or produce a generic label "Column XY".
static <V extends NumberVector<? extends N>,N extends Number> NumberVector.Factory<V,N>	RelationUtil.getNumberVectorFactory(Relation<V> relation) Get the number vector factory of a database relation.
static double[][]	RelationUtil.relationAsMatrix(Relation<? extends NumberVector<?>> relation, ArrayDBIDs ids) Copy a relation into a double matrix.
static <O> ProxyView<O>	ProxyView.wrap(Database database, DBIDs idview, Relation<O> inner) Constructor-like static method.

Constructors in de.lmu.ifi.dbs.elki.database.relation with parameters of type Relation

Constructor and Description
ConvertToStringView(Relation<?> existing) Constructor.
ProjectedView(Relation<IN> inner, Projection<IN,OUT> projection) Constructor.
ProxyView(Database database, DBIDs idview, Relation<O> inner) Constructor.

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

Methods in de.lmu.ifi.dbs.elki.distance.distancefunction with parameters of type Relation

Modifier and Type	Method and Description
<O extends DBID> DistanceQuery<O,D>	AbstractDBIDDistanceFunction.instantiate(Relation<O> database)
<T extends NumberVector<?>> SpatialPrimitiveDistanceQuery<T,DoubleDistance>	AbstractSpatialDoubleDistanceFunction.instantiate(Relation<T> relation)
<T extends NumberVector<?>> SpatialPrimitiveDistanceQuery<T,DoubleDistance>	AbstractSpatialDoubleDistanceNorm.instantiate(Relation<T> relation)
<T extends O> SharedNearestNeighborJaccardDistanceFunction.Instance<T>	SharedNearestNeighborJaccardDistanceFunction.instantiate(Relation<T> database)
<T extends O> FilteredLocalPCABasedDistanceFunction.Instance<T,?,D>	FilteredLocalPCABasedDistanceFunction.instantiate(Relation<T> database) Instantiate with a database to get the actual distance query.
<T extends O> DistanceQuery<T,D>	MinKDistance.instantiate(Relation<T> relation)
<T extends O> DistanceQuery<T,D>	AbstractPrimitiveDistanceFunction.instantiate(Relation<T> relation) Instantiate with a database to get the actual distance query.
<T extends O> DistanceQuery<T,D>	DistanceFunction.instantiate(Relation<T> relation) Instantiate with a database to get the actual distance query.
<T extends V> LocallyWeightedDistanceFunction.Instance<T>	LocallyWeightedDistanceFunction.instantiate(Relation<T> database)
<T extends V> SpatialDistanceQuery<T,D>	SpatialPrimitiveDistanceFunction.instantiate(Relation<T> relation)

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

Constructor and Description
AbstractDatabaseDistanceFunction.Instance(Relation<O> database, DistanceFunction<? super O,D> parent) Constructor.
AbstractIndexBasedDistanceFunction.Instance(Relation<O> database, I index, F parent) Constructor.
LocallyWeightedDistanceFunction.Instance(Relation<V> database, LocalProjectionIndex<V,?> index, LocallyWeightedDistanceFunction<? super V> distanceFunction) Constructor.
MinKDistance.Instance(Relation<T> relation, int k, DistanceFunction<? super O,D> parentDistance) Constructor.
SharedNearestNeighborJaccardDistanceFunction.Instance(Relation<T> database, SharedNearestNeighborIndex<T> preprocessor, SharedNearestNeighborJaccardDistanceFunction<T> parent) Constructor.

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

Methods in de.lmu.ifi.dbs.elki.distance.distancefunction.adapter with parameters of type Relation

Modifier and Type	Method and Description
<T extends O> DistanceQuery<T,DoubleDistance>	ArccosSimilarityAdapter.instantiate(Relation<T> database)
<T extends O> DistanceQuery<T,DoubleDistance>	LnSimilarityAdapter.instantiate(Relation<T> database)
<T extends O> DistanceQuery<T,DoubleDistance>	LinearAdapterLinear.instantiate(Relation<T> database)
abstract <T extends O> DistanceQuery<T,DoubleDistance>	AbstractSimilarityAdapter.instantiate(Relation<T> database)

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

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

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

Methods in de.lmu.ifi.dbs.elki.distance.distancefunction.correlation with parameters of type Relation

Modifier and Type	Method and Description
<T extends NumberVector<?>> ERiCDistanceFunction.Instance<T>	ERiCDistanceFunction.instantiate(Relation<T> database)
<T extends NumberVector<?>> PCABasedCorrelationDistanceFunction.Instance<T>	PCABasedCorrelationDistanceFunction.instantiate(Relation<T> database)

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

Constructor and Description
ERiCDistanceFunction.Instance(Relation<V> database, FilteredLocalPCAIndex<V> index, ERiCDistanceFunction parent, double delta, double tau) Constructor.
PCABasedCorrelationDistanceFunction.Instance(Relation<V> database, FilteredLocalPCAIndex<V> index, double delta, PCABasedCorrelationDistanceFunction distanceFunction) Constructor.

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

Methods in de.lmu.ifi.dbs.elki.distance.distancefunction.subspace with parameters of type Relation

Modifier and Type	Method and Description
<T extends NumberVector<?>> SpatialPrimitiveDistanceQuery<T,DoubleDistance>	SubspaceLPNormDistanceFunction.instantiate(Relation<T> database)
<T extends NumberVector<?>> DiSHDistanceFunction.Instance<T>	DiSHDistanceFunction.instantiate(Relation<T> database)
<T extends V> HiSCDistanceFunction.Instance<T>	HiSCDistanceFunction.instantiate(Relation<T> database)
<V extends NumberVector<?>> LocalSubspaceDistanceFunction.Instance<V>	LocalSubspaceDistanceFunction.instantiate(Relation<V> database)

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

Constructor and Description
AbstractPreferenceVectorBasedCorrelationDistanceFunction.Instance(Relation<V> database, P preprocessor, double epsilon, AbstractPreferenceVectorBasedCorrelationDistanceFunction<? super V,?> distanceFunction) Constructor.
DiSHDistanceFunction.Instance(Relation<V> database, DiSHPreferenceVectorIndex<V> index, double epsilon, DiSHDistanceFunction distanceFunction) Constructor.
HiSCDistanceFunction.Instance(Relation<V> database, HiSCPreferenceVectorIndex<V> index, double epsilon, HiSCDistanceFunction<? super V> distanceFunction) Constructor.
LocalSubspaceDistanceFunction.Instance(Relation<V> database, FilteredLocalPCAIndex<V> index, LocalSubspaceDistanceFunction distanceFunction)

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

Fields in de.lmu.ifi.dbs.elki.distance.similarityfunction declared as Relation

Modifier and Type	Field and Description
protected Relation<? extends DBID>	AbstractDBIDSimilarityFunction.database The database we work on

Methods in de.lmu.ifi.dbs.elki.distance.similarityfunction with parameters of type Relation

Modifier and Type	Method and Description
<T extends O> DistanceSimilarityQuery<T,DoubleDistance>	JaccardPrimitiveSimilarityFunction.instantiate(Relation<T> relation)
<T extends O> FractionalSharedNearestNeighborSimilarityFunction.Instance<T>	FractionalSharedNearestNeighborSimilarityFunction.instantiate(Relation<T> database)
<T extends O> SimilarityQuery<T,D>	AbstractPrimitiveSimilarityFunction.instantiate(Relation<T> relation)
<T extends O> IndexBasedSimilarityFunction.Instance<T,?,D>	IndexBasedSimilarityFunction.instantiate(Relation<T> database) Preprocess the database to get the actual distance function.
abstract <T extends O> AbstractIndexBasedSimilarityFunction.Instance<T,?,R,D>	AbstractIndexBasedSimilarityFunction.instantiate(Relation<T> database)
<T extends O> SharedNearestNeighborSimilarityFunction.Instance<T>	SharedNearestNeighborSimilarityFunction.instantiate(Relation<T> database)
<T extends O> SimilarityQuery<T,D>	SimilarityFunction.instantiate(Relation<T> relation) Instantiate with a representation to get the actual similarity query.

Constructors in de.lmu.ifi.dbs.elki.distance.similarityfunction with parameters of type Relation

Constructor and Description
AbstractDBIDSimilarityFunction(Relation<? extends DBID> database) Constructor.
AbstractIndexBasedSimilarityFunction.Instance(Relation<O> database, I index) Constructor.
FractionalSharedNearestNeighborSimilarityFunction.Instance(Relation<T> database, SharedNearestNeighborIndex<T> preprocessor, FractionalSharedNearestNeighborSimilarityFunction<? super T> similarityFunction) Constructor.
SharedNearestNeighborSimilarityFunction.Instance(Relation<O> database, SharedNearestNeighborIndex<O> preprocessor, SharedNearestNeighborSimilarityFunction<? super O> similarityFunction) Constructor.

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

Methods in de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel with parameters of type Relation

Modifier and Type	Method and Description
<T extends NumberVector<?>> DistanceSimilarityQuery<T,DoubleDistance>	PolynomialKernelFunction.instantiate(Relation<T> database)

Constructors in de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel with parameters of type Relation

Constructor and Description
KernelMatrix(PrimitiveSimilarityFunction<? super O,D> kernelFunction, Relation<? extends O> relation, DBIDs ids) Provides a new kernel matrix.
KernelMatrix(SimilarityQuery<? super O,D> kernelFunction, Relation<? extends O> relation, DBIDs ids) Provides a new kernel matrix.

Uses of Relation in de.lmu.ifi.dbs.elki.evaluation.outlier

Methods in de.lmu.ifi.dbs.elki.evaluation.outlier with parameters of type Relation

Modifier and Type	Method and Description
private XYCurve	OutlierPrecisionRecallCurve.computePrecisionResult(int size, SetDBIDs ids, DBIDIter iter, Relation<Double> scores)

Uses of Relation in de.lmu.ifi.dbs.elki.evaluation.roc

Fields in de.lmu.ifi.dbs.elki.evaluation.roc declared as Relation

Modifier and Type	Field and Description
private Relation<Double>	ROC.OutlierScoreAdapter.scores Outlier score.

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

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

Modifier and Type	Field and Description
(package private) Relation<?>	ComputeSimilarityMatrixImage.SimilarityMatrix.relation The database

Methods in de.lmu.ifi.dbs.elki.evaluation.similaritymatrix that return Relation

Modifier and Type	Method and Description
Relation<?>	ComputeSimilarityMatrixImage.SimilarityMatrix.getRelation() Get the relation

Methods in de.lmu.ifi.dbs.elki.evaluation.similaritymatrix with parameters of type Relation

Modifier and Type	Method and Description
private ComputeSimilarityMatrixImage.SimilarityMatrix	ComputeSimilarityMatrixImage.computeSimilarityMatrixImage(Relation<O> relation, DBIDIter iter) Compute the actual similarity image.

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

Constructor and Description
ComputeSimilarityMatrixImage.SimilarityMatrix(RenderedImage img, Relation<?> relation, ArrayDBIDs ids) Constructor

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

Fields in de.lmu.ifi.dbs.elki.index declared as Relation

Modifier and Type	Field and Description
protected Relation<O>	AbstractIndex.relation The representation we are bound to.

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

Modifier and Type	Method and Description
I	IndexFactory.instantiate(Relation<V> relation) Sets the database in the distance function of this index (if existing).

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

Constructor and Description
AbstractIndex(Relation<O> relation) Constructor.
AbstractRefiningIndex(Relation<O> relation) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.index.lsh

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

Modifier and Type	Method and Description
InMemoryLSHIndex.Instance	InMemoryLSHIndex.instantiate(Relation<V> relation)

Constructors in de.lmu.ifi.dbs.elki.index.lsh with parameters of type Relation

Constructor and Description
InMemoryLSHIndex.Instance(Relation<V> relation, ArrayList<? extends LocalitySensitiveHashFunction<? super V>> hashfunctions, int numberOfBuckets) Constructor.

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

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

Modifier and Type	Method and Description
ArrayList<? extends LocalitySensitiveHashFunction<? super NumberVector<?>>>	AbstractHashFunctionFamily.generateHashFunctions(Relation<? extends NumberVector<?>> relation, int l)
ArrayList<? extends LocalitySensitiveHashFunction<? super V>>	LocalitySensitiveHashFunctionFamily.generateHashFunctions(Relation<? extends V> relation, int k) Generate hash functions for the given relation.

Uses of Relation in de.lmu.ifi.dbs.elki.index.preprocessed

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

Modifier and Type	Method and Description
I	LocalProjectionIndex.Factory.instantiate(Relation<V> relation) Instantiate the index for a given database.

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

Constructor and Description
AbstractPreprocessorIndex(Relation<O> relation) Constructor.

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

Methods in de.lmu.ifi.dbs.elki.index.preprocessed.knn with parameters of type Relation

Modifier and Type	Method and Description
private MetricalIndexTree<O,D,N,E>	MetricalIndexApproximationMaterializeKNNPreprocessor.getMetricalIndex(Relation<O> relation) Do some (limited) type checking, then cast the database into a spatial database.
SpatialApproximationMaterializeKNNPreprocessor<NumberVector<?>,D,N,E>	SpatialApproximationMaterializeKNNPreprocessor.Factory.instantiate(Relation<NumberVector<?>> relation)
MetricalIndexApproximationMaterializeKNNPreprocessor<O,D,N,E>	MetricalIndexApproximationMaterializeKNNPreprocessor.Factory.instantiate(Relation<O> relation)
MaterializeKNNPreprocessor<O,D>	MaterializeKNNPreprocessor.Factory.instantiate(Relation<O> relation)
abstract AbstractMaterializeKNNPreprocessor<O,D,T>	AbstractMaterializeKNNPreprocessor.Factory.instantiate(Relation<O> relation)
RandomSampleKNNPreprocessor<O,D>	RandomSampleKNNPreprocessor.Factory.instantiate(Relation<O> relation)
MaterializeKNNAndRKNNPreprocessor<O,D>	MaterializeKNNAndRKNNPreprocessor.Factory.instantiate(Relation<O> relation)
CachedDoubleDistanceKNNPreprocessor<O>	CachedDoubleDistanceKNNPreprocessor.Factory.instantiate(Relation<O> relation)
PartitionApproximationMaterializeKNNPreprocessor<O,D>	PartitionApproximationMaterializeKNNPreprocessor.Factory.instantiate(Relation<O> relation)
KNNJoinMaterializeKNNPreprocessor<O,D>	KNNJoinMaterializeKNNPreprocessor.Factory.instantiate(Relation<O> relation)

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

Constructor and Description
AbstractMaterializeKNNPreprocessor(Relation<O> relation, DistanceFunction<? super O,D> distanceFunction, int k) Constructor.
CachedDoubleDistanceKNNPreprocessor(Relation<O> relation, DistanceFunction<? super O,DoubleDistance> distanceFunction, int k, File file) Constructor.
KNNJoinMaterializeKNNPreprocessor(Relation<V> relation, DistanceFunction<? super V,D> distanceFunction, int k) Constructor.
MaterializeKNNAndRKNNPreprocessor(Relation<O> relation, DistanceFunction<? super O,D> distanceFunction, int k) Constructor.
MaterializeKNNPreprocessor(Relation<O> relation, DistanceFunction<? super O,D> distanceFunction, int k) Constructor with preprocessing step.
MetricalIndexApproximationMaterializeKNNPreprocessor(Relation<O> relation, DistanceFunction<? super O,D> distanceFunction, int k) Constructor
PartitionApproximationMaterializeKNNPreprocessor(Relation<O> relation, DistanceFunction<? super O,D> distanceFunction, int k, int partitions, RandomFactory rnd) Constructor
RandomSampleKNNPreprocessor(Relation<O> relation, DistanceFunction<? super O,D> distanceFunction, int k, double share, RandomFactory rnd) Constructor.
SpatialApproximationMaterializeKNNPreprocessor(Relation<O> relation, DistanceFunction<? super O,D> distanceFunction, int k) Constructor

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

Methods in de.lmu.ifi.dbs.elki.index.preprocessed.localpca with parameters of type Relation

Modifier and Type	Method and Description
abstract I	AbstractFilteredPCAIndex.Factory.instantiate(Relation<NV> relation)
I	FilteredLocalPCAIndex.Factory.instantiate(Relation<NV> relation) Instantiate the index for a given database.
RangeQueryFilteredPCAIndex<V>	RangeQueryFilteredPCAIndex.Factory.instantiate(Relation<V> relation)
KNNQueryFilteredPCAIndex<V>	KNNQueryFilteredPCAIndex.Factory.instantiate(Relation<V> relation)

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

Constructor and Description
AbstractFilteredPCAIndex(Relation<NV> relation, PCAFilteredRunner<NV> pca) Constructor.
KNNQueryFilteredPCAIndex(Relation<NV> relation, PCAFilteredRunner<NV> pca, KNNQuery<NV,DoubleDistance> knnQuery, int k) Constructor.
RangeQueryFilteredPCAIndex(Relation<NV> database, PCAFilteredRunner<NV> pca, RangeQuery<NV,DoubleDistance> rangeQuery, DoubleDistance epsilon) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.index.preprocessed.preference

Methods in de.lmu.ifi.dbs.elki.index.preprocessed.preference with parameters of type Relation

Modifier and Type	Method and Description
private BitSet	HiSCPreferenceVectorIndex.determinePreferenceVector(Relation<V> relation, DBIDRef id, DBIDs neighborIDs, StringBuilder msg) Determines the preference vector according to the specified neighbor ids.
private BitSet	DiSHPreferenceVectorIndex.determinePreferenceVector(Relation<V> relation, ModifiableDBIDs[] neighborIDs, StringBuilder msg) Determines the preference vector according to the specified neighbor ids.
private BitSet	DiSHPreferenceVectorIndex.determinePreferenceVectorByApriori(Relation<V> relation, ModifiableDBIDs[] neighborIDs, StringBuilder msg) Determines the preference vector with the apriori strategy.
private RangeQuery<V,DoubleDistance>[]	DiSHPreferenceVectorIndex.initRangeQueries(Relation<V> relation, int dimensionality) Initializes the dimension selecting distancefunctions to determine the preference vectors.
HiSCPreferenceVectorIndex<V>	HiSCPreferenceVectorIndex.Factory.instantiate(Relation<V> relation)
abstract I	AbstractPreferenceVectorIndex.Factory.instantiate(Relation<V> relation)
DiSHPreferenceVectorIndex<V>	DiSHPreferenceVectorIndex.Factory.instantiate(Relation<V> relation)
I	PreferenceVectorIndex.Factory.instantiate(Relation<V> relation) Instantiate the index for a given database.

Constructors in de.lmu.ifi.dbs.elki.index.preprocessed.preference with parameters of type Relation

Constructor and Description
AbstractPreferenceVectorIndex(Relation<NV> relation) Constructor.
DiSHPreferenceVectorIndex(Relation<V> relation, DoubleDistance[] epsilon, int minpts, DiSHPreferenceVectorIndex.Strategy strategy) Constructor.
HiSCPreferenceVectorIndex(Relation<V> relation, double alpha, int k) Constructor.

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

Methods in de.lmu.ifi.dbs.elki.index.preprocessed.snn with parameters of type Relation

Modifier and Type	Method and Description
I	SharedNearestNeighborIndex.Factory.instantiate(Relation<O> database) Instantiate the index for a given database.
SharedNearestNeighborPreprocessor<O,D>	SharedNearestNeighborPreprocessor.Factory.instantiate(Relation<O> relation)

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

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

Uses of Relation in de.lmu.ifi.dbs.elki.index.preprocessed.subspaceproj

Methods in de.lmu.ifi.dbs.elki.index.preprocessed.subspaceproj with parameters of type Relation

Modifier and Type	Method and Description
protected abstract P	AbstractSubspaceProjectionIndex.computeProjection(DBIDRef id, DistanceDBIDList<D> neighbors, Relation<NV> relation) This method implements the type of variance analysis to be computed for a given point.
protected PCAFilteredResult	FourCSubspaceIndex.computeProjection(DBIDRef id, DistanceDBIDList<D> neighbors, Relation<V> database)
protected SubspaceProjectionResult	PreDeConSubspaceIndex.computeProjection(DBIDRef id, DistanceDBIDList<D> neighbors, Relation<V> database)
I	SubspaceProjectionIndex.Factory.instantiate(Relation<NV> relation) Instantiate the index for a given database.
abstract I	AbstractSubspaceProjectionIndex.Factory.instantiate(Relation<NV> relation)
FourCSubspaceIndex<V,D>	FourCSubspaceIndex.Factory.instantiate(Relation<V> relation)
PreDeConSubspaceIndex<V,D>	PreDeConSubspaceIndex.Factory.instantiate(Relation<V> relation)

Constructors in de.lmu.ifi.dbs.elki.index.preprocessed.subspaceproj with parameters of type Relation

Constructor and Description
AbstractSubspaceProjectionIndex(Relation<NV> relation, D epsilon, DistanceFunction<NV,D> rangeQueryDistanceFunction, int minpts) Constructor.
FourCSubspaceIndex(Relation<V> relation, D epsilon, DistanceFunction<V,D> rangeQueryDistanceFunction, int minpts, PCAFilteredRunner<V> pca) Full constructor.
PreDeConSubspaceIndex(Relation<V> relation, D epsilon, DistanceFunction<V,D> rangeQueryDistanceFunction, int minpts, double delta) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.index.projected

Fields in de.lmu.ifi.dbs.elki.index.projected declared as Relation

Modifier and Type	Field and Description
(package private) Relation<O>	ProjectedIndex.relation The relation we predend to index.
(package private) Relation<I>	ProjectedIndex.view The view that we really index.

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

Modifier and Type	Method and Description
ProjectedIndex<O,O>	LatLngAsECEFIndex.Factory.instantiate(Relation<O> relation)
ProjectedIndex<O,I>	ProjectedIndex.Factory.instantiate(Relation<O> relation)
ProjectedIndex<O,O>	LngLatAsECEFIndex.Factory.instantiate(Relation<O> relation)

Constructors in de.lmu.ifi.dbs.elki.index.projected with parameters of type Relation

Constructor and Description
LatLngAsECEFIndex(Relation<O> relation, Projection<O,O> proj, Relation<O> view, Index inner, boolean norefine) Constructor.
LatLngAsECEFIndex(Relation<O> relation, Projection<O,O> proj, Relation<O> view, Index inner, boolean norefine) Constructor.
LngLatAsECEFIndex(Relation<O> relation, Projection<O,O> proj, Relation<O> view, Index inner, boolean norefine) Constructor.
LngLatAsECEFIndex(Relation<O> relation, Projection<O,O> proj, Relation<O> view, Index inner, boolean norefine) Constructor.
ProjectedIndex(Relation<O> relation, Projection<O,I> proj, Relation<I> view, Index inner, boolean norefine, double kmulti) Constructor.
ProjectedIndex(Relation<O> relation, Projection<O,I> proj, Relation<I> view, Index inner, boolean norefine, double kmulti) Constructor.

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

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

Constructor and Description
AbstractMkTree(Relation<O> relation, PageFile<N> pagefile, S settings) Constructor.
AbstractMkTreeUnified(Relation<O> relation, PageFile<N> pagefile, S settings) Constructor.

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

Fields in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkapp declared as Relation

Modifier and Type	Field and Description
private Relation<O>	MkAppTreeIndex.relation The relation indexed

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

Modifier and Type	Method and Description
MkAppTreeIndex<O,D>	MkAppTreeFactory.instantiate(Relation<O> relation)

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

Constructor and Description
MkAppTree(Relation<O> relation, PageFile<MkAppTreeNode<O,D>> pageFile, MkAppTreeSettings<O,D> settings) Constructor.
MkAppTreeIndex(Relation<O> relation, PageFile<MkAppTreeNode<O,D>> pageFile, MkAppTreeSettings<O,D> settings) Constructor.

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

Fields in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkcop declared as Relation

Modifier and Type	Field and Description
private Relation<O>	MkCoPTreeIndex.relation Relation indexed

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

Modifier and Type	Method and Description
MkCoPTreeIndex<O,D>	MkCopTreeFactory.instantiate(Relation<O> relation)

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

Constructor and Description
MkCoPTree(Relation<O> relation, PageFile<MkCoPTreeNode<O,D>> pagefile, MkTreeSettings<O,D,MkCoPTreeNode<O,D>,MkCoPEntry> settings) Constructor.
MkCoPTreeIndex(Relation<O> relation, PageFile<MkCoPTreeNode<O,D>> pageFile, MkTreeSettings<O,D,MkCoPTreeNode<O,D>,MkCoPEntry> settings) Constructor.

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

Fields in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkmax declared as Relation

Modifier and Type	Field and Description
private Relation<O>	MkMaxTreeIndex.relation Relation indexed.

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

Modifier and Type	Method and Description
MkMaxTreeIndex<O,D>	MkMaxTreeFactory.instantiate(Relation<O> relation)

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

Constructor and Description
MkMaxTree(Relation<O> relation, PageFile<MkMaxTreeNode<O,D>> pagefile, MkTreeSettings<O,D,MkMaxTreeNode<O,D>,MkMaxEntry> settings) Constructor.
MkMaxTreeIndex(Relation<O> relation, PageFile<MkMaxTreeNode<O,D>> pagefile, MkTreeSettings<O,D,MkMaxTreeNode<O,D>,MkMaxEntry> settings) Constructor.

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

Fields in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mktab declared as Relation

Modifier and Type	Field and Description
private Relation<O>	MkTabTreeIndex.relation The relation indexed.

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

Modifier and Type	Method and Description
MkTabTreeIndex<O,D>	MkTabTreeFactory.instantiate(Relation<O> relation)

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

Constructor and Description
MkTabTree(Relation<O> relation, PageFile<MkTabTreeNode<O,D>> pagefile, MkTreeSettings<O,D,MkTabTreeNode<O,D>,MkTabEntry> settings) Constructor.
MkTabTreeIndex(Relation<O> relation, PageFile<MkTabTreeNode<O,D>> pagefile, MkTreeSettings<O,D,MkTabTreeNode<O,D>,MkTabEntry> settings) Constructor.

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

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

Modifier and Type	Field and Description
private Relation<O>	MTreeIndex.relation The relation indexed.

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

Modifier and Type	Method and Description
MTreeIndex<O,D>	MTreeFactory.instantiate(Relation<O> relation)

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

Constructor and Description
MTreeIndex(Relation<O> relation, PageFile<MTreeNode<O,D>> pagefile, MTreeSettings<O,D,MTreeNode<O,D>,MTreeEntry> settings) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.index.tree.spatial.kd

Methods in de.lmu.ifi.dbs.elki.index.tree.spatial.kd with parameters of type Relation

Modifier and Type	Method and Description
MinimalisticMemoryKDTree<O>	MinimalisticMemoryKDTree.Factory.instantiate(Relation<O> relation)

Constructors in de.lmu.ifi.dbs.elki.index.tree.spatial.kd with parameters of type Relation

Constructor and Description
MinimalisticMemoryKDTree(Relation<O> relation) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.deliclu

Fields in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.deliclu declared as Relation

Modifier and Type	Field and Description
private Relation<O>	DeLiCluTreeIndex.relation The relation we index.

Methods in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.deliclu with parameters of type Relation

Modifier and Type	Method and Description
DeLiCluTreeIndex<O>	DeLiCluTreeFactory.instantiate(Relation<O> relation)

Constructors in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.deliclu with parameters of type Relation

Constructor and Description
DeLiCluTreeIndex(Relation<O> relation, PageFile<DeLiCluNode> pagefile, AbstractRTreeSettings settings) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rstar

Fields in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rstar declared as Relation

Modifier and Type	Field and Description
private Relation<O>	RStarTreeIndex.relation Relation

Methods in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rstar with parameters of type Relation

Modifier and Type	Method and Description
RStarTreeIndex<O>	RStarTreeFactory.instantiate(Relation<O> relation)

Constructors in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rstar with parameters of type Relation

Constructor and Description
RStarTreeIndex(Relation<O> relation, PageFile<RStarTreeNode> pagefile, AbstractRTreeSettings settings) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.index.vafile

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

Modifier and Type	Method and Description
protected static BitSet	PartialVAFile.fakeSubspace(Relation<? extends NumberVector<?>> relation) Fake subspace (full-dimensional).
PartialVAFile<V>	PartialVAFile.Factory.instantiate(Relation<V> relation)
VAFile<V>	VAFile.Factory.instantiate(Relation<V> relation)
void	VAFile.setPartitions(Relation<V> relation) Initialize the data set grid by computing quantiles.

Constructors in de.lmu.ifi.dbs.elki.index.vafile with parameters of type Relation

Constructor and Description
DAFile(Relation<? extends NumberVector<?>> relation, int dimension, int partitions) Constructor.
PartialVAFile(int pageSize, Relation<V> relation, int partitions) Constructor.
VAFile(int pageSize, Relation<V> relation, int partitions) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.math.dimensionsimilarity

Methods in de.lmu.ifi.dbs.elki.math.dimensionsimilarity with parameters of type Relation

Modifier and Type	Method and Description
private ArrayList<ArrayDBIDs>	HiCSDimensionSimilarity.buildOneDimIndexes(Relation<? extends NumberVector<?>> relation, DBIDs ids, DimensionSimilarityMatrix matrix) Calculates "index structures" for every attribute, i.e. sorts a ModifiableArray of every DBID in the database for every dimension and stores them in a list
private ArrayList<ArrayList<DBIDs>>	MCEDimensionSimilarity.buildPartitions(Relation<? extends NumberVector<?>> relation, DBIDs ids, int depth, DimensionSimilarityMatrix matrix) Calculates "index structures" for every attribute, i.e. sorts a ModifiableArray of every DBID in the database for every dimension and stores them in a list.
private double	HiCSDimensionSimilarity.calculateContrast(Relation<? extends NumberVector<?>> relation, DBIDs subset, ArrayDBIDs subspaceIndex1, ArrayDBIDs subspaceIndex2, int dim1, int dim2, Random random) Calculates the actual contrast of a given subspace
void	SlopeInversionDimensionSimilarity.computeDimensionSimilarites(Database database, Relation<? extends NumberVector<?>> relation, DBIDs subset, DimensionSimilarityMatrix matrix)
void	SlopeDimensionSimilarity.computeDimensionSimilarites(Database database, Relation<? extends NumberVector<?>> relation, DBIDs subset, DimensionSimilarityMatrix matrix)
void	CovarianceDimensionSimilarity.computeDimensionSimilarites(Database database, Relation<? extends NumberVector<?>> relation, DBIDs subset, DimensionSimilarityMatrix matrix)
void	HiCSDimensionSimilarity.computeDimensionSimilarites(Database database, Relation<? extends NumberVector<?>> relation, DBIDs subset, DimensionSimilarityMatrix matrix)
void	HSMDimensionSimilarity.computeDimensionSimilarites(Database database, Relation<? extends NumberVector<?>> relation, DBIDs subset, DimensionSimilarityMatrix matrix)
void	SURFINGDimensionSimilarity.computeDimensionSimilarites(Database database, Relation<? extends NumberVector<?>> relation, DBIDs subset, DimensionSimilarityMatrix matrix)
void	MCEDimensionSimilarity.computeDimensionSimilarites(Database database, Relation<? extends NumberVector<?>> relation, DBIDs subset, DimensionSimilarityMatrix matrix)
void	DimensionSimilarity.computeDimensionSimilarites(Database database, Relation<? extends V> relation, DBIDs subset, DimensionSimilarityMatrix matrix) Compute the dimension similarity matrix

Uses of Relation in de.lmu.ifi.dbs.elki.math.linearalgebra

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

Modifier and Type	Method and Description
<F extends NumberVector<?>> F	CovarianceMatrix.getMeanVector(Relation<? extends F> relation) Get the mean as vector.
static ProjectedCentroid	ProjectedCentroid.make(BitSet dims, Relation<? extends NumberVector<?>> relation) Static Constructor from a relation.
static ProjectedCentroid	ProjectedCentroid.make(BitSet dims, Relation<? extends NumberVector<?>> relation, DBIDs ids) Static Constructor from a relation.
static Centroid	Centroid.make(Relation<? extends NumberVector<?>> relation) Static constructor from an existing relation.
static CovarianceMatrix	CovarianceMatrix.make(Relation<? extends NumberVector<?>> relation) Static Constructor from a full relation.
static Centroid	Centroid.make(Relation<? extends NumberVector<?>> relation, DBIDs ids) Static constructor from an existing relation.
static CovarianceMatrix	CovarianceMatrix.make(Relation<? extends NumberVector<?>> relation, DBIDs ids) Static Constructor from a full relation.
<F extends NumberVector<?>> F	Centroid.toVector(Relation<? extends F> relation) Get the data as vector.

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

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

Modifier and Type	Method and Description
Matrix	StandardCovarianceMatrixBuilder.processDatabase(Relation<? extends V> database) Compute Covariance Matrix for a complete database.
Matrix	AbstractCovarianceMatrixBuilder.processDatabase(Relation<? extends V> database)
Matrix	CovarianceMatrixBuilder.processDatabase(Relation<? extends V> database) Compute Covariance Matrix for a complete database.
PCAResult	PCARunner.processDatabase(Relation<? extends V> database) Run PCA on the complete database.
Matrix	StandardCovarianceMatrixBuilder.processIds(DBIDs ids, Relation<? extends V> database) Compute Covariance Matrix for a collection of database IDs.
PCAFilteredResult	PCAFilteredAutotuningRunner.processIds(DBIDs ids, Relation<? extends V> database)
abstract Matrix	AbstractCovarianceMatrixBuilder.processIds(DBIDs ids, Relation<? extends V> database)
Matrix	CovarianceMatrixBuilder.processIds(DBIDs ids, Relation<? extends V> database) Compute Covariance Matrix for a collection of database IDs.
PCAResult	PCARunner.processIds(DBIDs ids, Relation<? extends V> database) Run PCA on a collection of database IDs.
Matrix	WeightedCovarianceMatrixBuilder.processIds(DBIDs ids, Relation<? extends V> relation) Weighted Covariance Matrix for a set of IDs.
PCAFilteredResult	PCAFilteredRunner.processIds(DBIDs ids, Relation<? extends V> database) Run PCA on a collection of database IDs.
Matrix	RANSACCovarianceMatrixBuilder.processIds(DBIDs ids, Relation<? extends V> relation)
<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 Relation in de.lmu.ifi.dbs.elki.math.scales

Methods in de.lmu.ifi.dbs.elki.math.scales with parameters of type Relation

Modifier and Type	Method and Description
static <O extends NumberVector<? extends Number>> LinearScale[]	Scales.calcScales(Relation<O> db) Compute a linear scale for each dimension.

Uses of Relation in de.lmu.ifi.dbs.elki.math.spacefillingcurves

Constructors in de.lmu.ifi.dbs.elki.math.spacefillingcurves with parameters of type Relation

Constructor and Description
ZCurveTransformer(Relation<? extends NumberVector<?>> relation, DBIDs ids) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.result

Methods in de.lmu.ifi.dbs.elki.result that return types with arguments of type Relation

Modifier and Type	Method and Description
static List<Relation<?>>	ResultUtil.getRelations(Result r) Collect all Annotation results from a Result

Methods in de.lmu.ifi.dbs.elki.result with parameters of type Relation

Modifier and Type	Method and Description
static SamplingResult	ResultUtil.getSamplingResult(Relation<?> rel) Get the sampling result attached to a relation
static ScalesResult	ResultUtil.getScalesResult(Relation<? extends NumberVector<?>> rel) Get (or create) a scales result for a relation.

Method parameters in de.lmu.ifi.dbs.elki.result with type arguments of type Relation

Modifier and Type	Method and Description
private StringBuilder	KMLOutputHandler.makeDescription(Collection<Relation<?>> relations, DBIDRef id) Make an HTML description.

Constructors in de.lmu.ifi.dbs.elki.result with parameters of type Relation

Constructor and Description
SamplingResult(Relation<?> rel) Constructor.
ScalesResult(Relation<? extends NumberVector<?>> relation) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.result.optics

Classes in de.lmu.ifi.dbs.elki.result.optics that implement Relation

Modifier and Type	Class and Description
(package private) class	ClusterOrderResult.PredecessorAdapter Result containing the predecessor ID.
(package private) class	ClusterOrderResult.ReachabilityDistanceAdapter Result containing the reachability distances.

Uses of Relation in de.lmu.ifi.dbs.elki.result.outlier

Fields in de.lmu.ifi.dbs.elki.result.outlier declared as Relation

Modifier and Type	Field and Description
private Relation<Double>	OutlierResult.scores Outlier scores.
protected Relation<Double>	OrderingFromRelation.scores Outlier scores.

Methods in de.lmu.ifi.dbs.elki.result.outlier that return Relation

Modifier and Type	Method and Description
Relation<Double>	OutlierResult.getScores() Get the outlier scores association.

Constructors in de.lmu.ifi.dbs.elki.result.outlier with parameters of type Relation

Constructor and Description
OrderingFromRelation(Relation<Double> scores) Ascending constructor.
OrderingFromRelation(Relation<Double> scores, boolean ascending) Constructor for outlier orderings
OutlierResult(OutlierScoreMeta meta, Relation<Double> scores) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.result.textwriter

Method parameters in de.lmu.ifi.dbs.elki.result.textwriter with type arguments of type Relation

Modifier and Type	Method and Description
private void	TextWriter.printObject(TextWriterStream out, Database db, DBIDRef objID, List<Relation<?>> ra)
private void	TextWriter.writeClusterResult(Database db, StreamFactory streamOpener, Clustering<Model> clustering, Cluster<Model> clus, List<Relation<?>> ra, NamingScheme naming)
private void	TextWriter.writeOrderingResult(Database db, StreamFactory streamOpener, OrderingResult or, List<Relation<?>> ra)

Uses of Relation in de.lmu.ifi.dbs.elki.utilities

Fields in de.lmu.ifi.dbs.elki.utilities declared as Relation

Modifier and Type	Field and Description
(package private) Relation<? extends O>	DatabaseUtil.RelationObjectIterator.database The database we use.
(package private) Relation<? extends O>	DatabaseUtil.CollectionFromRelation.db The database we query.

Methods in de.lmu.ifi.dbs.elki.utilities that return Relation

Modifier and Type	Method and Description
static Relation<String>	DatabaseUtil.guessLabelRepresentation(Database database) Guess a potentially label-like representation, preferring class labels.
static Relation<String>	DatabaseUtil.guessObjectLabelRepresentation(Database database) Guess a potentially object label-like representation.
static <V extends NumberVector<?>,T extends NumberVector<?>> Relation<V>	DatabaseUtil.relationUglyVectorCast(Relation<T> database) An ugly vector type cast unavoidable in some situations due to Generics.

Methods in de.lmu.ifi.dbs.elki.utilities with parameters of type Relation

Modifier and Type	Method and Description
static <NV extends NumberVector<?>> Pair<NV,NV>	DatabaseUtil.computeMinMax(Relation<NV> relation) Determines the minimum and maximum values in each dimension of all objects stored in the given database.
static <V extends FeatureVector<?>> int	DatabaseUtil.dimensionality(Relation<V> relation) Deprecated. Use RelationUtil.dimensionality(Relation) instead!
static <V extends NumberVector<?>> double	DatabaseUtil.exactMedian(Relation<V> relation, DBIDs ids, int dimension) Returns the median of a data set in the given dimension.
static <O> Class<?>	DatabaseUtil.getBaseObjectClassExpensive(Relation<O> database) Do a full inspection of the database to find the base object class.
static SortedSet<ClassLabel>	DatabaseUtil.getClassLabels(Relation<? extends ClassLabel> database) Retrieves all class labels within the database.
static <O> Class<? extends O>	DatabaseUtil.guessObjectClass(Relation<O> database) Do a cheap guess at the databases object class.
static <V extends NumberVector<?>> double	DatabaseUtil.quickMedian(Relation<V> relation, ArrayDBIDs ids, int dimension, int numberOfSamples) Returns the median of a data set in the given dimension by using a sampling method.
static <V extends NumberVector<?>,T extends NumberVector<?>> Relation<V>	DatabaseUtil.relationUglyVectorCast(Relation<T> database) An ugly vector type cast unavoidable in some situations due to Generics.
static double[]	DatabaseUtil.variances(Relation<? extends NumberVector<?>> database, NumberVector<?> centroid, DBIDs ids) Determines the variances in each dimension of the specified objects stored in the given database.

Constructors in de.lmu.ifi.dbs.elki.utilities with parameters of type Relation

Constructor and Description
DatabaseUtil.CollectionFromRelation(Relation<? extends O> db) Constructor.
DatabaseUtil.RelationObjectIterator(DBIDIter iter, Relation<? extends O> database) Full Constructor.
DatabaseUtil.RelationObjectIterator(Relation<? extends O> database) Simplified constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.utilities.referencepoints

Methods in de.lmu.ifi.dbs.elki.utilities.referencepoints with parameters of type Relation

Modifier and Type	Method and Description
<T extends O> Collection<O>	ReferencePointsHeuristic.getReferencePoints(Relation<T> db) Get the reference points for the given database.
<T extends O> Collection<O>	FullDatabaseReferencePoints.getReferencePoints(Relation<T> db)
<T extends V> Collection<V>	RandomSampleReferencePoints.getReferencePoints(Relation<T> db)
<T extends V> Collection<V>	RandomGeneratedReferencePoints.getReferencePoints(Relation<T> db)
<T extends V> Collection<V>	GridBasedReferencePoints.getReferencePoints(Relation<T> db)
<T extends V> Collection<V>	StarBasedReferencePoints.getReferencePoints(Relation<T> db)
<T extends V> Collection<V>	AxisBasedReferencePoints.getReferencePoints(Relation<T> db)

Uses of Relation in de.lmu.ifi.dbs.elki.utilities.scaling.outlier

Methods in de.lmu.ifi.dbs.elki.utilities.scaling.outlier with parameters of type Relation

Modifier and Type	Method and Description
private double[]	SigmoidOutlierScalingFunction.MStepLevenbergMarquardt(double a, double b, ArrayDBIDs ids, BitSet t, Relation<Double> scores) M-Step using a modified Levenberg-Marquardt method.

Uses of Relation in de.lmu.ifi.dbs.elki.visualization

Fields in de.lmu.ifi.dbs.elki.visualization declared as Relation

Modifier and Type	Field and Description
(package private) Relation<?>	VisualizationTask.relation The main representation

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

Modifier and Type	Method and Description
<R extends Relation<?>> R	VisualizationTask.getRelation()

Constructors in de.lmu.ifi.dbs.elki.visualization with parameters of type Relation

Constructor and Description
VisualizationTask(String name, Result result, Relation<?> relation, VisFactory factory) Visualization task.
VisualizationTask(String name, VisualizerContext context, Result result, Relation<?> relation, VisFactory factory, Projection proj, SVGPlot svgp, double width, double height) Constructor

Uses of Relation in de.lmu.ifi.dbs.elki.visualization.gui

Fields in de.lmu.ifi.dbs.elki.visualization.gui declared as Relation

Modifier and Type	Field and Description
(package private) Relation<ClassLabel>	SelectionTableWindow.crep Class label representation
(package private) Relation<String>	SelectionTableWindow.orep Object label representation

Uses of Relation in de.lmu.ifi.dbs.elki.visualization.projector

Fields in de.lmu.ifi.dbs.elki.visualization.projector declared as Relation

Modifier and Type	Field and Description
(package private) Relation<V>	ScatterPlotProjector.rel Relation we project.
(package private) Relation<V>	HistogramProjector.rel Relation we project.
(package private) Relation<V>	ParallelPlotProjector.rel Relation we project.

Methods in de.lmu.ifi.dbs.elki.visualization.projector that return Relation

Modifier and Type	Method and Description
Relation<V>	ScatterPlotProjector.getRelation() The relation we project.
Relation<V>	HistogramProjector.getRelation() Get the relation we project.
Relation<V>	ParallelPlotProjector.getRelation() The relation we project.

Constructors in de.lmu.ifi.dbs.elki.visualization.projector with parameters of type Relation

Constructor and Description
HistogramProjector(Relation<V> rel, int maxdim) Constructor.
ParallelPlotProjector(Relation<V> rel) Constructor.
ScatterPlotProjector(Relation<V> rel, int maxdim) Constructor.

Uses of Relation in de.lmu.ifi.dbs.elki.visualization.visualizers.histogram

Fields in de.lmu.ifi.dbs.elki.visualization.visualizers.histogram declared as Relation

Modifier and Type	Field and Description
private Relation<NV>	ColoredHistogramVisualizer.Instance.relation The database we visualize

Uses of Relation in de.lmu.ifi.dbs.elki.visualization.visualizers.parallel

Fields in de.lmu.ifi.dbs.elki.visualization.visualizers.parallel declared as Relation

Modifier and Type	Field and Description
protected Relation<NV>	AbstractParallelVisualization.relation The representation we visualize

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

Fields in de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot declared as Relation

Modifier and Type	Field and Description
protected Relation<? extends NumberVector<?>>	AbstractScatterplotVisualization.rel The representation we visualize
protected Relation<PolygonsObject>	PolygonVisualization.Instance.rep The representation we visualize
private Relation<?>	TooltipStringVisualization.Instance.result Number value to visualize
private Relation<? extends Number>	TooltipScoreVisualization.Instance.result Number value to visualize

Uses of Relation in de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.outlier

Fields in de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.outlier declared as Relation

Modifier and Type	Field and Description
protected Relation<Vector>	COPVectorVisualization.Instance.result The outlier result to visualize

Uses of Relation in tutorial.clustering

Methods in tutorial.clustering with parameters of type Relation

Modifier and Type	Method and Description
protected WritableDataStore<SameSizeKMeansAlgorithm.Meta>	SameSizeKMeansAlgorithm.initializeMeta(Relation<V> relation, List<? extends NumberVector<?>> means) Initialize the metadata storage.
protected List<? extends NumberVector<?>>	SameSizeKMeansAlgorithm.refineResult(Relation<V> relation, List<? extends NumberVector<?>> means, List<ModifiableDBIDs> clusters, WritableDataStore<SameSizeKMeansAlgorithm.Meta> metas, ArrayModifiableDBIDs tids) Perform k-means style iterations to improve the clustering result.
PointerHierarchyRepresentationResult<DoubleDistance>	NaiveAgglomerativeHierarchicalClustering4.run(Database db, Relation<O> relation) Run the algorithm
Result	NaiveAgglomerativeHierarchicalClustering3.run(Database db, Relation<O> relation) Run the algorithm
Result	NaiveAgglomerativeHierarchicalClustering1.run(Database db, Relation<O> relation) Run the algorithm
Result	NaiveAgglomerativeHierarchicalClustering2.run(Database db, Relation<O> relation) Run the algorithm
Clustering<MeanModel<V>>	SameSizeKMeansAlgorithm.run(Database database, Relation<V> relation) Run k-means with cluster size constraints.
protected void	SameSizeKMeansAlgorithm.updateDistances(Relation<V> relation, List<? extends NumberVector<?>> means, WritableDataStore<SameSizeKMeansAlgorithm.Meta> metas, PrimitiveDoubleDistanceFunction<NumberVector<?>> df) Compute the distances of each object to all means.

Uses of Relation in tutorial.outlier

Methods in tutorial.outlier with parameters of type Relation

Modifier and Type	Method and Description
OutlierResult	ODIN.run(Database database, Relation<O> relation) Run the ODIN algorithm Tutorial note: the signature of this method depends on the types that we requested in the ODIN.getInputTypeRestriction() method.
OutlierResult	DistanceStddevOutlier.run(Database database, Relation<O> relation) Run the outlier detection algorithm