Uses of Interface
de.lmu.ifi.dbs.elki.database.Database

Packages that use Database

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.gdbscan	Generalized DBSCAN.
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.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.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.jsmap	JavaScript based map client - server architecture.
de.lmu.ifi.dbs.elki.database	ELKI database layer - loading, storing, indexing and accessing data
de.lmu.ifi.dbs.elki.database.relation	Relations, materialized and virtual (views).
de.lmu.ifi.dbs.elki.evaluation.histogram	Functionality for the evaluation of algorithms using histograms.
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.result	Result types, representation and handling
de.lmu.ifi.dbs.elki.result.optics	Result classes for OPTICS.
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.visualization	Visualization package of ELKI.
de.lmu.ifi.dbs.elki.visualization.gui	Package to provide a visualization GUI.
de.lmu.ifi.dbs.elki.workflow	Work flow packages, e.g. following the usual KDD model, closely related to CRISP-DM
tutorial.clustering	Classes from the tutorial on implementing a custom k-means variation.
tutorial.outlier

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

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

Modifier and Type	Method and Description
R	AbstractAlgorithm.run(Database database)
Result	NullAlgorithm.run(Database database)
Result	Algorithm.run(Database database) Runs the algorithm.
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 Database in de.lmu.ifi.dbs.elki.algorithm.benchmark

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

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

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

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

Modifier and Type	Method and Description
protected void	OPTICS.expandClusterOrder(ClusterOrderResult<D> clusterOrder, Database database, RangeQuery<O,D> rangeQuery, DBID objectID, D epsilon, FiniteProgress progress) OPTICS-function expandClusterOrder.
protected void	OPTICS.expandClusterOrderDouble(ClusterOrderResult<DoubleDistance> clusterOrder, Database database, RangeQuery<O,DoubleDistance> rangeQuery, DBID objectID, DoubleDistance epsilon, FiniteProgress progress) OPTICS-function expandClusterOrder.
protected DistanceQuery<V,DoubleDistance>	AbstractProjectedClustering.getDistanceQuery(Database database) Returns the distance function.
C	ClusteringAlgorithm.run(Database database)
ClusterOrderResult<D>	OPTICSTypeAlgorithm.run(Database database)
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.

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

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

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 Database in de.lmu.ifi.dbs.elki.algorithm.clustering.biclustering

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

Modifier and Type	Field and Description
private Database	AbstractBiclustering.database Keeps the currently set database.

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

Modifier and Type	Method and Description
Database	AbstractBiclustering.getDatabase() Getter for database.

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

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

Modifier and Type	Method and Description
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.

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

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

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

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

Modifier and Type	Method and Description
<T> CorePredicate.Instance<T>	CorePredicate.instantiate(Database database, SimpleTypeInformation<?> type) Instantiate for a database.
<T> NeighborPredicate.Instance<T>	NeighborPredicate.instantiate(Database database, SimpleTypeInformation<?> type) Instantiate for a database.
<T> NeighborPredicate.Instance<T>	EpsilonNeighborPredicate.instantiate(Database database, SimpleTypeInformation<?> type)
<T> CorePredicate.Instance<T>	MinPtsCorePredicate.instantiate(Database database, SimpleTypeInformation<?> type)
Clustering<Model>	GeneralizedDBSCAN.run(Database database)

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

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

Modifier and Type	Method and Description
Clustering<DendrogramModel<D>>	ExtractFlatClusteringFromHierarchy.run(Database database)
PointerHierarchyRepresentationResult<D>	HierarchicalClusteringAlgorithm.run(Database db)
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

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

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

Modifier and Type	Method and Description
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)
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)

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

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

Modifier and Type	Method and Description
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.

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

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

Modifier and Type	Method and Description
Clustering<Model>	ByLabelHierarchicalClustering.run(Database database)
Clustering<Model>	ByLabelClustering.run(Database database)
Clustering<Model>	ByLabelOrAllInOneClustering.run(Database database)

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

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

Modifier and Type	Method and Description
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.
OutlierResult	OutlierAlgorithm.run(Database database)
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.

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

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

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.

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

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

Modifier and Type	Method and Description
OutlierResult	SimpleOutlierEnsemble.run(Database database)
OutlierResult	RescaleMetaOutlierAlgorithm.run(Database 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.

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

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

Modifier and Type	Method and Description
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	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	CTLuGLSBackwardSearchAlgorithm.run(Database database, Relation<V> relationx, Relation<? extends NumberVector<?>> relationy) Run the algorithm

Uses of Database 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 Database

Modifier and Type	Method and Description
private DataStore<DBIDs>	ExternalNeighborhood.Factory.loadNeighbors(Database database, Relation<?> relation) Method to load the external neighbors.

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

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

Modifier and Type	Method and Description
OutlierResult	OutRankS1.run(Database database)

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

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

Modifier and Type	Method and Description
OutlierResult	ByLabelOutlier.run(Database database)
OutlierResult	TrivialGeneratedOutlier.run(Database database)

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

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

Modifier and Type	Method and Description
HistogramResult<DoubleVector>	DistanceStatisticsWithClasses.run(Database database)
HistogramResult<DoubleVector>	EvaluateRankingQuality.run(Database database)
Result	AddSingleScale.run(Database database)
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

Uses of Database in de.lmu.ifi.dbs.elki.application.jsmap

Fields in de.lmu.ifi.dbs.elki.application.jsmap declared as Database

Modifier and Type	Field and Description
private Database	JSONWebServer.db The database we use for obtaining object bundles.

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

Subinterfaces of Database in de.lmu.ifi.dbs.elki.database

Modifier and Type	Interface and Description
interface	UpdatableDatabase Database API with updates.

Classes in de.lmu.ifi.dbs.elki.database that implement Database

Modifier and Type	Class and Description
class	AbstractDatabase Abstract base class for database API implementations.
class	HashmapDatabase Provides a mapping for associations based on a Hashtable and functions to get the next usable ID for insertion, making IDs reusable after deletion of the entry.
class	ProxyDatabase A proxy database to use e.g. for projections and partitions.
class	StaticArrayDatabase This database class uses array-based storage and thus does not allow for dynamic insert, delete and update operations.

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

Modifier and Type	Method and Description
protected abstract Database	AbstractDatabase.Parameterizer.makeInstance()

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

Modifier and Type	Method and Description
static <O,D extends Distance<D>> DistanceQuery<O,D>	QueryUtil.getDistanceQuery(Database database, DistanceFunction<? super O,D> distanceFunction, Object... hints) Get a distance query for a given distance function, automatically choosing a relation.
static <O,D extends Distance<D>> KNNQuery<O,D>	QueryUtil.getKNNQuery(Database database, 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(Database database, DistanceFunction<? super O,D> distanceFunction, Object... hints) Get a range query object for the given distance function.
static <O,D extends Distance<D>> SimilarityQuery<O,D>	QueryUtil.getSimilarityQuery(Database database, SimilarityFunction<? super O,D> similarityFunction, Object... hints) Get a similarity query, automatically choosing a relation.

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

Constructor and Description
ProxyDatabase(DBIDs ids, Database database) Constructor, proxying all relations of an existing database.

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

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

Modifier and Type	Field and Description
private Database	MaterializedRelation.database Our database
private Database	ProxyView.database Our database
private Database	DBIDView.database The database

Methods in de.lmu.ifi.dbs.elki.database.relation that return Database

Modifier and Type	Method and Description
Database	MaterializedRelation.getDatabase()
Database	ProxyView.getDatabase()
Database	ConvertToStringView.getDatabase()
Database	Relation.getDatabase() Get the associated database.
Database	DBIDView.getDatabase()
Database	ProjectedView.getDatabase()

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

Modifier and Type	Method and Description
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 Database

Constructor and Description
DBIDView(Database database, DBIDs ids) Constructor.
MaterializedRelation(Database database, SimpleTypeInformation<O> type, DBIDs ids) Constructor.
MaterializedRelation(Database database, SimpleTypeInformation<O> type, DBIDs ids, String name) Constructor.
MaterializedRelation(Database database, SimpleTypeInformation<O> type, DBIDs ids, String name, DataStore<O> content) Constructor.
ProxyView(Database database, DBIDs idview, Relation<O> inner) Constructor.

Uses of Database in de.lmu.ifi.dbs.elki.evaluation.histogram

Methods in de.lmu.ifi.dbs.elki.evaluation.histogram with parameters of type Database

Modifier and Type	Method and Description
HistogramResult<DoubleVector>	ComputeOutlierHistogram.evaluateOutlierResult(Database database, OutlierResult or) Evaluate a single outlier result as histogram.

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

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

Modifier and Type	Method and Description
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 Database in de.lmu.ifi.dbs.elki.result

Methods in de.lmu.ifi.dbs.elki.result that return Database

Modifier and Type	Method and Description
static Database	ResultUtil.findDatabase(Result baseResult) Find the first database result in the tree.

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

Modifier and Type	Method and Description
static <O> void	ResultUtil.ensureClusteringResult(Database db, Result result) Ensure that the result contains at least one Clustering.
static SelectionResult	ResultUtil.ensureSelectionResult(Database db) Ensure that there also is a selection container object.
private void	KMLOutputHandler.writeKMLData(XMLStreamWriter xmlw, OutlierResult outlierResult, Database database)

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

Methods in de.lmu.ifi.dbs.elki.result.optics that return Database

Modifier and Type	Method and Description
Database	ClusterOrderResult.ReachabilityDistanceAdapter.getDatabase()
Database	ClusterOrderResult.PredecessorAdapter.getDatabase()

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

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

Modifier and Type	Method and Description
void	TextWriter.output(Database db, Result r, StreamFactory streamOpener, Pattern filter) Stream output.
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 Database in de.lmu.ifi.dbs.elki.utilities

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

Modifier and Type	Method and Description
static SortedSet<ClassLabel>	DatabaseUtil.getClassLabels(Database database) Retrieves all class labels within the database.
static ArrayModifiableDBIDs	DatabaseUtil.getObjectsByLabelMatch(Database database, Pattern name_pattern) Find object by matching their labels.
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.

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

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

Modifier and Type	Method and Description
static String	VisualizerParameterizer.getTitle(Database db, Result result) Try to automatically generate a title for this.

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

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

Modifier and Type	Field and Description
(package private) Database	SelectionTableWindow.database The database we use

Uses of Database in de.lmu.ifi.dbs.elki.workflow

Fields in de.lmu.ifi.dbs.elki.workflow declared as Database

Modifier and Type	Field and Description
private Database	InputStep.database Holds the database to have the algorithms run with.
protected Database	InputStep.Parameterizer.database Holds the database to have the algorithms run on.

Methods in de.lmu.ifi.dbs.elki.workflow that return Database

Modifier and Type	Method and Description
Database	InputStep.getDatabase() Get the database to use.

Methods in de.lmu.ifi.dbs.elki.workflow with parameters of type Database

Modifier and Type	Method and Description
HierarchicalResult	AlgorithmStep.runAlgorithms(Database database) Run algorithms.
void	EvaluationStep.runEvaluators(HierarchicalResult r, Database db)

Constructors in de.lmu.ifi.dbs.elki.workflow with parameters of type Database

Constructor and Description
InputStep(Database database) Constructor.

Uses of Database in tutorial.clustering

Methods in tutorial.clustering with parameters of type Database

Modifier and Type	Method and Description
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.

Uses of Database in tutorial.outlier

Methods in tutorial.outlier with parameters of type Database

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