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.classification	Classification 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.em	Expectation-Maximization clustering algorithm.
de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan	Generalized DBSCAN.
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical	Hierarchical agglomerative clustering (HAC).
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.extraction	Extraction of partitional clusterings from hierarchical results.
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans	K-means clustering and variations.
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization	Initialization strategies for k-means.
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.parallel	Parallelized implementations of k-means.
de.lmu.ifi.dbs.elki.algorithm.clustering.meta	Meta clustering algorithms, that get their result from other clusterings or external sources.
de.lmu.ifi.dbs.elki.algorithm.clustering.optics	OPTICS family of clustering algorithms.
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.clustering.uncertain	Clustering algorithms for uncertain data.
de.lmu.ifi.dbs.elki.algorithm.itemsetmining	Algorithms for frequent itemset mining such as APRIORI.
de.lmu.ifi.dbs.elki.algorithm.outlier	Outlier detection algorithms
de.lmu.ifi.dbs.elki.algorithm.outlier.anglebased	Angle-based outlier detection algorithms.
de.lmu.ifi.dbs.elki.algorithm.outlier.clustering	Clustering based outlier detection.
de.lmu.ifi.dbs.elki.algorithm.outlier.distance	Distance-based outlier detection algorithms, such as DBOutlier and kNN.
de.lmu.ifi.dbs.elki.algorithm.outlier.distance.parallel	Parallel implementations of distance-based outlier detectors.
de.lmu.ifi.dbs.elki.algorithm.outlier.lof	LOF family of outlier detection algorithms.
de.lmu.ifi.dbs.elki.algorithm.outlier.lof.parallel	Parallelized variants of LOF.
de.lmu.ifi.dbs.elki.algorithm.outlier.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.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.evaluation.clustering.internal	Internal evaluation measures for clusterings.
de.lmu.ifi.dbs.elki.evaluation.histogram	Functionality for the evaluation of algorithms using histograms.
de.lmu.ifi.dbs.elki.result	Result types, representation and handling
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.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.
CollectionResult<MaterializeDistances.DistanceEntry>	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.
KNNDistancesSampler.KNNDistanceOrderResult	KNNDistancesSampler.run(Database database, Relation<O> relation) Provides an order of the kNN-distances for all objects within the specified database.
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.classification

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

Modifier and Type	Method and Description
void	Classifier.buildClassifier(Database database, Relation<? extends ClassLabel> classLabels) Performs the training.
void	KNNClassifier.buildClassifier(Database database, Relation<? extends ClassLabel> labels)
void	PriorProbabilityClassifier.buildClassifier(Database database, Relation<? extends ClassLabel> labelrep) Learns the prior probability for all classes.
Result	KNNClassifier.run(Database database) Deprecated.
R	AbstractClassifier.run(Database database) Deprecated.

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 DistanceQuery<V>	AbstractProjectedClustering.getDistanceQuery(Database database) Returns the distance function.
C	ClusteringAlgorithm.run(Database database)
Clustering<Model>	SNNClustering.run(Database database, Relation<O> relation) Perform SNN clustering
Clustering<PrototypeModel<O>>	CanopyPreClustering.run(Database database, Relation<O> relation) Run the algorithm
Clustering<MeanModel>	NaiveMeanShiftClustering.run(Database database, Relation<V> relation) Run the mean-shift clustering algorithm.

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<DimensionModel>	COPAC.run(Database database, Relation<V> relation) Run the COPAC algorithm.
Clustering<Model>	ORCLUS.run(Database database, Relation<V> relation) Performs the ORCLUS algorithm on the given database.
Clustering<CorrelationModel<V>>	ERiC.run(Database database, Relation<V> relation) Performs the ERiC algorithm on the given database.
CorrelationClusterOrder	HiCO.run(Database db, Relation<V> relation)

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

Constructor and Description
Instance(Database db, Relation<V> relation) Constructor.

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

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

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

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

Modifier and Type	Method and Description
ERiCNeighborPredicate.Instance	ERiCNeighborPredicate.instantiate(Database database, Relation<V> relation) Full instantiation interface.
COPACNeighborPredicate.Instance	COPACNeighborPredicate.instantiate(Database database, Relation<V> relation) Full instantiation method.
<T> CorePredicate.Instance<T>	CorePredicate.instantiate(Database database, SimpleTypeInformation<?> type) Instantiate for a database.
<T> CorePredicate.Instance<T>	FourCCorePredicate.instantiate(Database database, SimpleTypeInformation<?> type)
<T> NeighborPredicate.Instance<T>	NeighborPredicate.instantiate(Database database, SimpleTypeInformation<?> type) Instantiate for a database.
<T> NeighborPredicate.Instance<T>	ERiCNeighborPredicate.instantiate(Database database, SimpleTypeInformation<?> type)
<T> NeighborPredicate.Instance<T>	FourCNeighborPredicate.instantiate(Database database, SimpleTypeInformation<?> type)
<T> CorePredicate.Instance<T>	PreDeConCorePredicate.instantiate(Database database, SimpleTypeInformation<?> type)
<T> NeighborPredicate.Instance<T>	EpsilonNeighborPredicate.instantiate(Database database, SimpleTypeInformation<?> type)
<T> NeighborPredicate.Instance<T>	PreDeConNeighborPredicate.instantiate(Database database, SimpleTypeInformation<?> type)
<T> CorePredicate.Instance<T>	MinPtsCorePredicate.instantiate(Database database, SimpleTypeInformation<?> type)
<T> NeighborPredicate.Instance<T>	COPACNeighborPredicate.instantiate(Database database, SimpleTypeInformation<?> type)
Clustering<Model>	GeneralizedDBSCAN.run(Database database)
Clustering<Model>	LSDBC.run(Database database, Relation<O> relation) Run the LSDBC algorithm

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
PointerHierarchyRepresentationResult	HierarchicalClusteringAlgorithm.run(Database db)
PointerHierarchyRepresentationResult	SLINK.run(Database database, Relation<O> relation) Performs the SLINK algorithm on the given database.
PointerHierarchyRepresentationResult	AnderbergHierarchicalClustering.run(Database db, Relation<O> relation) Run the algorithm
PointerHierarchyRepresentationResult	AGNES.run(Database db, Relation<O> relation) Run the algorithm
PointerDensityHierarchyRepresentationResult	SLINKHDBSCANLinearMemory.run(Database db, Relation<O> relation) Run the algorithm
PointerDensityHierarchyRepresentationResult	HDBSCANLinearMemory.run(Database db, Relation<O> relation) Run the algorithm

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

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

Modifier and Type	Method and Description
Clustering<DendrogramModel>	ExtractFlatClusteringFromHierarchy.run(Database database)
Clustering<DendrogramModel>	HDBSCANHierarchyExtraction.run(Database database)
Clustering<DendrogramModel>	SimplifiedHierarchyExtraction.run(Database database)

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
Clustering<KMeansModel>	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>	SingleAssignmentKMeans.run(Database database, Relation<V> relation)
Clustering<KMeansModel>	KMeansMacQueen.run(Database database, Relation<V> relation)
Clustering<KMeansModel>	KMeansHamerly.run(Database database, Relation<V> relation)
Clustering<MedoidModel>	KMedoidsEM.run(Database database, Relation<V> relation) Run k-medoids
Clustering<M>	XMeans.run(Database database, Relation<V> relation) Run the algorithm on a database and relation.
Clustering<KMeansModel>	KMeansHybridLloydMacQueen.run(Database database, Relation<V> relation)
Clustering<KMeansModel>	KMeansElkan.run(Database database, Relation<V> relation)
Clustering<KMeansModel>	KMeansLloyd.run(Database database, Relation<V> relation)
Clustering<M>	BestOfMultipleKMeans.run(Database database, Relation<V> relation)
Clustering<MeanModel>	KMediansLloyd.run(Database database, Relation<V> relation)
Clustering<MedoidModel>	CLARA.run(Database database, Relation<V> relation)
protected List<Cluster<M>>	XMeans.splitCluster(Cluster<M> parentCluster, Database database, Relation<V> relation) Conditionally splits the clusters based on the information criterion.

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

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

Modifier and Type	Method and Description
<T extends NumberVector,O extends NumberVector> List<O>	PredefinedInitialMeans.chooseInitialMeans(Database database, Relation<T> relation, int k, NumberVectorDistanceFunction<? super T> distanceFunction, NumberVector.Factory<O> factory)
<T extends V,O extends NumberVector> List<O>	KMeansInitialization.chooseInitialMeans(Database database, Relation<T> relation, int k, NumberVectorDistanceFunction<? super T> distanceFunction, NumberVector.Factory<O> factory) Choose initial means
<T extends V,O extends NumberVector> List<O>	SampleKMeansInitialization.chooseInitialMeans(Database database, Relation<T> relation, int k, NumberVectorDistanceFunction<? super T> distanceFunction, NumberVector.Factory<O> factory)
<T extends NumberVector,V extends NumberVector> List<V>	FarthestSumPointsInitialMeans.chooseInitialMeans(Database database, Relation<T> relation, int k, NumberVectorDistanceFunction<? super T> distanceFunction, NumberVector.Factory<V> factory)
<T extends NumberVector,V extends NumberVector> List<V>	RandomlyChosenInitialMeans.chooseInitialMeans(Database database, Relation<T> relation, int k, NumberVectorDistanceFunction<? super T> distanceFunction, NumberVector.Factory<V> factory)
<T extends NumberVector,V extends NumberVector> List<V>	RandomlyGeneratedInitialMeans.chooseInitialMeans(Database database, Relation<T> relation, int k, NumberVectorDistanceFunction<? super T> distanceFunction, NumberVector.Factory<V> factory)
<T extends NumberVector,V extends NumberVector> List<V>	FirstKInitialMeans.chooseInitialMeans(Database database, Relation<T> relation, int k, NumberVectorDistanceFunction<? super T> distanceFunction, NumberVector.Factory<V> factory)
<T extends NumberVector,V extends NumberVector> List<V>	KMeansPlusPlusInitialMeans.chooseInitialMeans(Database database, Relation<T> relation, int k, NumberVectorDistanceFunction<? super T> distanceFunction, NumberVector.Factory<V> factory)
<T extends NumberVector,V extends NumberVector> List<V>	PAMInitialMeans.chooseInitialMeans(Database database, Relation<T> relation, int k, NumberVectorDistanceFunction<? super T> distanceFunction, NumberVector.Factory<V> factory)
<T extends NumberVector,V extends NumberVector> List<V>	FarthestPointsInitialMeans.chooseInitialMeans(Database database, Relation<T> relation, int k, NumberVectorDistanceFunction<? super T> distanceFunction, NumberVector.Factory<V> factory)

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

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

Modifier and Type	Method and Description
Clustering<KMeansModel>	ParallelLloydKMeans.run(Database database, Relation<V> relation)

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

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

Modifier and Type	Method and Description
private void	ExternalClustering.attachToRelation(Database database, Relation<?> r, gnu.trove.list.array.TIntArrayList assignment, ArrayList<String> name) Build a clustering from the file result.
Clustering<? extends Model>	ExternalClustering.run(Database database) Run the algorithm.

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

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

Modifier and Type	Method and Description
ClusterOrder	OPTICSTypeAlgorithm.run(Database database)
Clustering<OPTICSModel>	OPTICSXi.run(Database database, Relation<?> relation)
ClusterOrder	DeLiClu.run(Database database, Relation<NV> relation)
abstract ClusterOrder	AbstractOPTICS.run(Database db, Relation<O> relation) Run OPTICS on the database.
ClusterOrder	OPTICSList.run(Database db, Relation<O> relation)
abstract ClusterOrder	GeneralizedOPTICS.run(Database db, Relation<O> relation) Run OPTICS on the database.
ClusterOrder	OPTICSHeap.run(Database db, Relation<O> relation)
ClusterOrder	FastOPTICS.run(Database db, Relation<V> rel) Run the algorithm.

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

Constructor and Description
Instance(Database db, Relation<O> relation) Constructor for a single data set.
Instance(Database db, Relation<O> relation) Constructor for a single data set.
Instance(Database db, Relation<O> relation) Constructor for a single data set.

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>	DOC.run(Database database, Relation<V> relation) Performs the DOC or FastDOC (as configured) algorithm on the given Database.
Clustering<SubspaceModel>	DiSH.run(Database db, Relation<V> relation) Performs the DiSH algorithm on the given database.
ClusterOrder	HiSC.run(Database db, Relation<V> relation)
Clustering<SubspaceModel>	PROCLUS.run(Database database, Relation<V> relation) Performs the PROCLUS algorithm on the given database.
Clustering<SubspaceModel>	P3C.run(Database database, Relation<V> relation) Performs the P3C algorithm on the given Database.
private Cluster<SubspaceModel>	DOC.runDOC(Database database, 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>	DOC.runFastDOC(Database database, Relation<V> relation, ArrayModifiableDBIDs S, int d, int n, int m, int r) Performs a single run of FastDOC, finding a single cluster.

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

Constructor and Description
Instance(Database db, Relation<V> relation) Constructor.
Instance(Database db, Relation<V> relation) Constructor.

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.clustering.uncertain

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

Modifier and Type	Method and Description
<T> NeighborPredicate.Instance<T>	FDBSCANNeighborPredicate.instantiate(Database database, SimpleTypeInformation<?> type)
C	CenterOfMassMetaClustering.run(Database database, Relation<? extends UncertainObject> relation) This run method will do the wrapping.
Clustering<?>	RepresentativeUncertainClustering.run(Database database, Relation<? extends UncertainObject> relation) This run method will do the wrapping.
Clustering<?>	UKMeans.run(Database database, Relation<DiscreteUncertainObject> relation) Run the clustering.

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

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

Modifier and Type	Method and Description
FrequentItemsetsResult	FPGrowth.run(Database db, Relation<BitVector> relation) Run the FP-Growth algorithm
FrequentItemsetsResult	Eclat.run(Database db, Relation<BitVector> relation) Run the Eclat algorithm

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
OutlierResult	OutlierAlgorithm.run(Database database)
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	SimpleCOP.run(Database database, Relation<V> data)

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

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

Modifier and Type	Method and Description
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	ABOD.run(Database db, Relation<V> relation) Run ABOD on the data set.

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

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

Modifier and Type	Method and Description
OutlierResult	SilhouetteOutlierDetection.run(Database database)
OutlierResult	KMeansOutlierDetection.run(Database database, Relation<O> relation) Run the outlier detection algorithm.
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.distance

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

Modifier and Type	Method and Description
protected DoubleDataStore	DBOutlierScore.computeOutlierScores(Database database, Relation<O> relation, double d)
protected DoubleDataStore	DBOutlierDetection.computeOutlierScores(Database database, Relation<O> relation, double neighborhoodSize)
protected abstract DoubleDataStore	AbstractDBOutlier.computeOutlierScores(Database database, Relation<O> relation, double d) computes an outlier score for each object of the database.
OutlierResult	ReferenceBasedOutlierDetection.run(Database database, Relation<? extends NumberVector> relation) Run the algorithm on the given relation.
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	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.

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

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

Modifier and Type	Method and Description
OutlierResult	ParallelKNNWeightOutlier.run(Database database, Relation<O> relation) Run the parallel kNN weight outlier detector.
OutlierResult	ParallelKNNOutlier.run(Database database, Relation<O> relation)

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>,KNNQuery<O>>,Pair<RKNNQuery<O>,RKNNQuery<O>>>	OnlineLOF.getKNNAndRkNNQueries(Database database, Relation<O> relation, StepProgress stepprog) Get the kNN and rkNN queries for the algorithm.
private Pair<KNNQuery<O>,KNNQuery<O>>	FlexibleLOF.getKNNQueries(Database database, Relation<O> relation, StepProgress stepprog) Get the kNN queries for the algorithm.
protected Pair<KNNQuery<O>,KNNQuery<O>>	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>, de.lmu.ifi.dbs.elki.database.query.knn.KNNQuery<O>, 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	KDEOS.run(Database database, Relation<O> rel) Run the KDEOS outlier detection 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	COF.run(Database database, Relation<O> relation) Runs the COF algorithm on the given database.
OutlierResult	ALOCI.run(Database database, Relation<O> relation)
OutlierResult	LOF.run(Database database, Relation<O> relation) Runs the LOF 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.lof.parallel

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

Modifier and Type	Method and Description
OutlierResult	ParallelLOF.run(Database database, Relation<O> relation)
OutlierResult	ParallelSimplifiedLOF.run(Database database, Relation<O> relation)

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	CTLuMoranScatterplotOutlier.run(Database database, Relation<N> nrel, Relation<? extends NumberVector> relation) Main method.
OutlierResult	TrimmedMeanApproach.run(Database database, Relation<N> nrel, Relation<? extends NumberVector> relation) Run the algorithm.
OutlierResult	CTLuMedianAlgorithm.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	CTLuScatterplotOutlier.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	CTLuMedianMultipleAttributes.run(Database database, Relation<N> spatial, Relation<O> attributes) Run the algorithm
OutlierResult	SLOM.run(Database database, Relation<N> spatial, Relation<O> relation)
OutlierResult	CTLuMeanMultipleAttributes.run(Database database, Relation<N> spatial, Relation<O> attributes) Run the algorithm
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>	ExtendedNeighborhood.Factory.extendNeighborhood(Database database, Relation<? extends O> relation) Method to load the external neighbors.
NeighborSetPredicate	ExternalNeighborhood.Factory.instantiate(Database database, Relation<?> relation)
NeighborSetPredicate	NeighborSetPredicate.Factory.instantiate(Database database, Relation<? extends O> relation) Instantiation method.
NeighborSetPredicate	ExtendedNeighborhood.Factory.instantiate(Database database, Relation<? extends O> relation)
NeighborSetPredicate	PrecomputedKNearestNeighborNeighborhood.Factory.instantiate(Database database, Relation<? extends O> relation)
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.spatial.neighborhood.weighted

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

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

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)
OutlierResult	AggarwalYuEvolutionary.run(Database database, Relation<V> relation) Performs the evolutionary algorithm on the given 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)
Result	HopkinsStatisticClusteringTendency.run(Database database, Relation<NumberVector> relation) Runs the algorithm in the timed evaluation part.
Result	EstimateIntrinsicDimensionality.run(Database database, Relation<O> relation)
HistogramResult<DoubleVector>	RankingQualityHistogram.run(Database database, Relation<O> relation) Process a database
CollectionResult<DoubleVector>	AveragePrecisionAtK.run(Database database, Relation<O> relation, Relation<?> lrelation) Run the algorithm
EvaluateRetrievalPerformance.RetrievalPerformanceResult	EvaluateRetrievalPerformance.run(Database database, Relation<O> relation, Relation<?> 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 Database storing data using hashtable storage, and thus allowing additional and removal of objects.
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> DistanceQuery<O>	QueryUtil.getDistanceQuery(Database database, DistanceFunction<? super O> distanceFunction, Object... hints) Get a distance query for a given distance function, automatically choosing a relation.
static <O> KNNQuery<O>	QueryUtil.getKNNQuery(Database database, DistanceFunction<? super O> distanceFunction, Object... hints) Get a KNN query object for the given distance function.
static <O> RangeQuery<O>	QueryUtil.getRangeQuery(Database database, DistanceFunction<? super O> distanceFunction, Object... hints) Get a range query object for the given distance function for radius-based neighbor search.
static <O> SimilarityQuery<O>	QueryUtil.getSimilarityQuery(Database database, SimilarityFunction<? super O> 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.evaluation.clustering.internal

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

Modifier and Type	Method and Description
double	EvaluateSimplifiedSilhouette.evaluateClustering(Database db, Relation<? extends NumberVector> rel, Clustering<?> c) Evaluate a single clustering.
double	EvaluateVarianceRatioCriteria.evaluateClustering(Database db, Relation<? extends NumberVector> rel, Clustering<?> c) Evaluate a single clustering.
double	EvaluateConcordantPairs.evaluateClustering(Database db, Relation<? extends NumberVector> rel, Clustering<?> c) Evaluate a single clustering.
double	EvaluateDaviesBouldin.evaluateClustering(Database db, Relation<? extends NumberVector> rel, Clustering<?> c) Evaluate a single clustering.
double	EvaluateSquaredErrors.evaluateClustering(Database db, Relation<? extends NumberVector> rel, Clustering<?> c) Evaluate a single clustering.
double	EvaluatePBMIndex.evaluateClustering(Database db, Relation<? extends NumberVector> rel, Clustering<?> c) Evaluate a single clustering.
double	EvaluateCIndex.evaluateClustering(Database db, Relation<? extends O> rel, DistanceQuery<O> dq, Clustering<?> c) Evaluate a single clustering.
double	EvaluateSilhouette.evaluateClustering(Database db, Relation<O> rel, DistanceQuery<O> dq, Clustering<?> c) Evaluate a single clustering.

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.result

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

Modifier and Type	Method and Description
static Database	ResultUtil.findDatabase(ResultHierarchy hier) Find the first database result in the tree.
static Database	ResultUtil.findDatabase(ResultHierarchy hier, 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 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.writeClusteringResult(XMLStreamWriter xmlw, Clustering<Model> clustering, Database database)
private void	KMLOutputHandler.writeOutlierResult(XMLStreamWriter xmlw, OutlierResult outlierResult, Database database)

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.
static <O> KNNQuery<O>	DatabaseUtil.precomputedKNNQuery(Database database, Relation<O> relation, DistanceFunction<? super O> distf, int k) Get (or create) a precomputed kNN query for the database.
static <O> KNNQuery<O>	DatabaseUtil.precomputedKNNQuery(Database database, Relation<O> relation, DistanceQuery<O> dq, int k) Get (or create) a precomputed kNN query for the database.

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.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
Result	AlgorithmStep.runAlgorithms(Database database) Run algorithms.
void	EvaluationStep.runEvaluators(ResultHierarchy hier, Database db)
void	OutputStep.runResultHandlers(ResultHierarchy hier, Database db) Run the result handlers.

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	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>	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