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

Packages that use Database

de.lmu.ifi.dbs.elki.algorithm	Package to collect algorithms suitable as a task for the KDDTask main routine.
de.lmu.ifi.dbs.elki.algorithm.clustering	Package collects clustering algorithms.
de.lmu.ifi.dbs.elki.algorithm.clustering.biclustering	Package to collect biclustering algorithms suitable as a task for the KDDTask main routine.
de.lmu.ifi.dbs.elki.algorithm.clustering.correlation	Package to collect correlation clustering algorithms suitable as a task for the KDDTask main routine.
de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.cash	Helper classes for the CASH algorithm.
de.lmu.ifi.dbs.elki.algorithm.clustering.subspace	Package to collect algorithms for clustering in axis-parallel subspaces, suitable as a task for the KDDTask main routine.
de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.clique	Helper classes for the CLIQUE algorithm.
de.lmu.ifi.dbs.elki.algorithm.result	Package to collect result classes for the results of algorithms.
de.lmu.ifi.dbs.elki.algorithm.result.clustering	Package to collect result classes for the results of clustering algorithms.
de.lmu.ifi.dbs.elki.algorithm.result.clustering.biclustering	Package to collect result classes for the results of biclustering algorithms.
de.lmu.ifi.dbs.elki.database	Package collects variants of databases and related classes.
de.lmu.ifi.dbs.elki.database.connection	Provides database connection classes.
de.lmu.ifi.dbs.elki.distance	Package collects distances and - in its subpackages - distance and similarity functions.
de.lmu.ifi.dbs.elki.distance.distancefunction	Package collects distance functions.
de.lmu.ifi.dbs.elki.distance.similarityfunction	Package collects similarity functions.
de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel	Package collects kernel functions.
de.lmu.ifi.dbs.elki.index	Package collects variants of index structures.
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants	Package collects variants of the M-Tree.
de.lmu.ifi.dbs.elki.index.tree.spatial	Package collects spatial tree-based index structures.
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rdknn	Package collects classes for the RdKNNTree
de.lmu.ifi.dbs.elki.preprocessing	Package collects preprocessors used for data preparation in a first step of various algorithms or distance measures.
de.lmu.ifi.dbs.elki.utilities	Package collects various classes and methods of global utility.
de.lmu.ifi.dbs.elki.varianceanalysis	Classes for analysis of variance by different methods.

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

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

protected BitSet[]	APRIORI.frequentItemsets(BitSet[] candidates, Database<BitVector> database) Returns the frequent BitSets out of the given BitSets with respect to the given database.
void	Algorithm.run(Database<O> database) Runs the algorithm.
void	AbstractAlgorithm.run(Database<O> database) Calls the runInTime()-method of extending classes.
protected void	APRIORI.runInTime(Database<BitVector> database)
protected void	KNNDistanceOrder.runInTime(Database<O> database)
protected abstract void	AbstractAlgorithm.runInTime(Database<O> database) The run method encapsulated in measure of runtime.
void	DependencyDerivator.runInTime(Database<V> db) Runs the pca.
protected void	KNNJoin.runInTime(Database<V> database) Runs the algorithm.

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

protected void	EM.assignProbabilitiesToInstances(Database<V> database, List<Double> normDistrFactor, List<V> means, List<Matrix> invCovMatr, List<Double> clusterWeights) Assigns the current probability values to the instances in the database.
protected void	DBSCAN.expandCluster(Database<O> database, Integer startObjectID, Progress progress) DBSCAN-function expandCluster.
protected void	SNNClustering.expandCluster(Database<O> database, Integer startObjectID, Progress progress) DBSCAN-function expandCluster adapted to SNN criterion.
protected void	ProjectedDBSCAN.expandCluster(Database<V> database, Integer startObjectID, Progress progress) ExpandCluster function of DBSCAN.
protected void	OPTICS.expandClusterOrder(Database<O> database, Integer objectID, Progress progress) OPTICS-function expandClusterOrder.
protected double	EM.expectationOfMixture(Database<V> database) The expectation value of the current mixture of distributions.
protected List<Integer>	SNNClustering.findSNNNeighbors(Database<O> database, Integer queryObject)
protected List<V>	EM.initialMeans(Database<V> database) Creates k random points distributed uniformly within the attribute ranges of the given database.
protected List<V>	KMeans.means(List<List<Integer>> clusters, List<V> means, Database<V> database) Returns the mean vectors of the given clusters in the given database.
protected void	OPTICS.runInTime(Database<O> database)
protected void	DBSCAN.runInTime(Database<O> database) Performs the DBSCAN algorithm on the given database.
protected void	DeLiClu.runInTime(Database<O> database)
protected void	SLINK.runInTime(Database<O> database) Runs the algorithm.
protected void	SNNClustering.runInTime(Database<O> database) Performs the SNN clustering algorithm on the given database.
protected void	KMeans.runInTime(Database<V> database) Performs the k-means algorithm on the given database.
protected void	ProjectedDBSCAN.runInTime(Database<V> database)
protected void	EM.runInTime(Database<V> database) Performs the EM clustering algorithm on the given database.
protected List<List<Integer>>	KMeans.sort(List<V> means, Database<V> database) Returns a list of clusters.

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

private Database<V>

AbstractBiclustering.database Keeps the currently set database.

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

protected void

AbstractBiclustering.runInTime(Database<V> database) Prepares the algorithm for running on a specific database.

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

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

private Database<ParameterizationFunction>

CASH.database The database holding the objects.

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

private ClassParameter<Database>	COPAA.PARTITION_DB_PARAM Parameter to specify the database class for each partition, must extend Database.
protected Class<Database<V>>	COPAA.partitionDatabase Holds the class of the partition databases.

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

private Database<ParameterizationFunction>	CASH.buildDB(int dim, Matrix basis, Set<Integer> ids, Database<ParameterizationFunction> database) Builds a dim-1 dimensional database where the objects are projected into the specified subspace.
private Database<RealVector>	CASH.buildDerivatorDB(Database<ParameterizationFunction> database, CASHInterval interval) 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

private void	ORCLUS.assign(Database<V> database, List<ORCLUS.Cluster> clusters) Creates a partitioning of the database by assigning each object to its closest seed.
private Database<ParameterizationFunction>	CASH.buildDB(int dim, Matrix basis, Set<Integer> ids, Database<ParameterizationFunction> database) Builds a dim-1 dimensional database where the objects are projected into the specified subspace.
private Database<RealVector>	CASH.buildDerivatorDB(Database<ParameterizationFunction> database, CASHInterval interval) Builds a database for the derivator consisting of the ids in the specified interval.
private double[]	CASH.determineMinMaxDistance(Database<ParameterizationFunction> database, int dimensionality) Determines the minimum and maximum function value of all parametrization functions stored in the specified database.
private SubspaceClusterMap	CASH.doRun(Database<ParameterizationFunction> database, Progress progress) Runs the CASH algorithm on the specified database, this method is recursively called until only noise is left.
private SortedMap<Integer,List<HierarchicalCorrelationCluster<V>>>	ERiC.extractCorrelationClusters(Database<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(Database<V> database, ORCLUS.Cluster cluster, int dim) Finds the basis of the subspace of dimensionality dim for the specified cluster.
private Set<Integer>	CASH.getDatabaseIDs(Database<ParameterizationFunction> database) Returns the set of ids belonging to the specified database.
private void	CASH.initHeap(DefaultHeap<Integer,CASHInterval> heap, Database<ParameterizationFunction> database, int dim, Set<Integer> ids) Initializes the heap with the root intervals.
private List<ORCLUS.Cluster>	ORCLUS.initialSeeds(Database<V> database, int k) Initializes the list of seeds wit a random sample of size k.
private void	ORCLUS.merge(Database<V> database, List<ORCLUS.Cluster> clusters, int k_new, int d_new) Reduces the number of seeds to k_new
private ORCLUS.ProjectedEnergy	ORCLUS.projectedEnergy(Database<V> database, ORCLUS.Cluster c_i, ORCLUS.Cluster c_j, int i, int j, int dim) Computes the projected energy of the specified clusters.
private Matrix	CASH.runDerivator(Database<ParameterizationFunction> database, int dim, CASHInterval interval, Set<Integer> ids) Runs the derivator on the specified inerval and assigns all points having a distance less then the standard deviation of the derivator model to the model to this model.
protected void	CASH.runInTime(Database<ParameterizationFunction> database) The run method encapsulated in measure of runtime.
protected void	ORCLUS.runInTime(Database<V> database)
protected void	ERiC.runInTime(Database<V> database) The run method encapsulated in measure of runtime.
protected void	COPAA.runInTime(Database<V> database)
protected PartitionResults<V>	COPAC.runPartitionAlgorithm(Database<V> database, Map<Integer,List<Integer>> partitionMap)
protected PartitionResults<V>	COPAA.runPartitionAlgorithm(Database<V> database, Map<Integer,List<Integer>> partitionMap) Runs the partition algorithm and creates the result.
private ORCLUS.Cluster	ORCLUS.union(Database<V> database, ORCLUS.Cluster c1, ORCLUS.Cluster c2, int dim) Returns the union of the two specified clusters.

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

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

private Database<ParameterizationFunction>

CASHIntervalSplit.database The database storing the parameterization functions.

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

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

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

private Map<Integer,PROCLUS.Cluster>	PROCLUS.assignPoints(Map<Integer,Set<Integer>> dimensions, Database<V> database) Assigns the objects to the clusters.
private double	PROCLUS.avgDistance(V centroid, Set<Integer> objectIDs, Database<V> database, int dimension) Computes the average distance of the objects to the centroid along the specified dimension.
private void	DiSH.buildHierarchy(Database<V> database, DiSHDistanceFunction<V,DiSHPreprocessor<V,?>> distanceFunction, List<HierarchicalAxesParallelCorrelationCluster> clusters, int dimensionality) Builds the cluster hierarchy
private void	DiSH.checkClusters(Database<V> database, DiSHDistanceFunction<V,DiSHPreprocessor<V,?>> distanceFunction, Map<BitSet,List<HierarchicalAxesParallelCorrelationCluster>> clustersMap) Removes the clusters with size < minpts from the cluster map and adds them to their parents.
private void	DiSH.computeClusters(Database<V> database, ClusterOrder<V,PreferenceVectorBasedCorrelationDistance> clusterOrder) Computes the hierarchical clusters according to the cluster order.
private Map<CLIQUEModel<V>,Set<Integer>>	CLIQUE.determineClusters(Database<V> database, SortedSet<CLIQUESubspace<V>> denseSubspaces) Determines the clusters in the specified dense subspaces.
private double	PROCLUS.evaluateClusters(Map<Integer,PROCLUS.Cluster> clusters, Map<Integer,Set<Integer>> dimensions, Database<V> database) Evaluates the quality of the clusters.
private Map<BitSet,List<HierarchicalAxesParallelCorrelationCluster>>	DiSH.extractClusters(Database<V> database, DiSHDistanceFunction<V,DiSHPreprocessor<V,?>> distanceFunction, ClusterOrder<V,PreferenceVectorBasedCorrelationDistance> clusterOrder) Extracts the clusters from the cluster order.
private SortedSet<CLIQUESubspace<V>>	CLIQUE.findDenseSubspaceCandidates(Database<V> database, Set<CLIQUESubspace<V>> denseSubspaces) Determines the k-dimensional dense subspace candidates.
private SortedSet<CLIQUESubspace<V>>	CLIQUE.findDenseSubspaces(Database<V> database, SortedSet<CLIQUESubspace<V>> denseSubspaces) Determines the k>1 dimensional dense subspaces and performs a pruning if this option is chosen.
private Map<Integer,Set<Integer>>	PROCLUS.findDimensions(Set<Integer> medoids, Database<V> database) Determines the set of correlated dimensions for each medoid in the specified medoid set.
private SortedSet<CLIQUESubspace<V>>	CLIQUE.findOneDimensionalDenseSubspaceCandidates(Database<V> database) Determines the one-dimensional dense subspace candidates by making a pass over the database.
private SortedSet<CLIQUESubspace<V>>	CLIQUE.findOneDimensionalDenseSubspaces(Database<V> database) Determines the one dimensional dense subspaces and performs a pruning if this option is chosen.
private HierarchicalAxesParallelCorrelationCluster	DiSH.findParent(Database<V> database, DiSHDistanceFunction<V,DiSHPreprocessor<V,?>> distanceFunction, HierarchicalAxesParallelCorrelationCluster child, Map<BitSet,List<HierarchicalAxesParallelCorrelationCluster>> clustersMap) Returns the parent of the specified cluster
private Map<Integer,List<QueryResult<DoubleDistance>>>	PROCLUS.getLocalities(Set<Integer> m_c, Database<V> database)
private Collection<CLIQUEUnit<V>>	CLIQUE.initOneDimensionalUnits(Database<V> database) Initializes and returns the one dimensional units.
private boolean	DiSH.isParent(Database<V> database, DiSHDistanceFunction<V,DiSHPreprocessor<V,?>> distanceFunction, HierarchicalAxesParallelCorrelationCluster parent, List<HierarchicalAxesParallelCorrelationCluster> children) Returns true, if the specified parent cluster is a parent of one child of the children clusters.
protected void	CLIQUE.runInTime(Database<V> database) Performs the CLIQUE algorithm on the given database.
protected void	PROCLUS.runInTime(Database<V> database)
protected void	DiSH.runInTime(Database<V> database) The run method encapsulated in measure of runtime.

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

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

Map<CLIQUEModel<V>,Set<Integer>>

CLIQUESubspace.determineClusters(Database<V> database) Determines all clusters in this subspace by performing a depth-first search algorithm to find connected dense units.

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

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

protected Database<O>

AbstractResult.db The database containing the objects of this result.

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

Database<O>

AbstractResult.getDatabase() Returns the database to which this clustering result belongs to.

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

AbstractResult(Database<O> db)
Creates a new abstract result object.

AprioriResult(List<BitSet> solution, Map<BitSet,Integer> supports, Database<BitVector> database)
Provides a apriori result.

CorrelationAnalysisSolution(LinearEquationSystem solution, Database<V> db, Matrix strongEigenvectors, Matrix weakEigenvectors, Matrix similarityMatrix, Vector centroid)
Provides a new CorrelationAnalysisSolution holding the specified matrix.

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

KNNDistanceOrderResult(Database<O> db, List<D> knnDistances)

PartitionResults(Database<O> db, Map<Integer,Result<O>> resultMap)
A result for a partitioning algorithm providing a single result for a single partition.

PointerRepresentation(HashMap<Integer,Integer> pi, HashMap<Integer,SLINK.SLinkDistance> lambda, DistanceFunction<O,D> distanceFunction, Database<O> database)
Creates a new pointer representation.

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

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

<L extends ClassLabel<L>> Database<O>	PartitionClusteringResults.associate(Class<L> classLabel) Returns a database containing only non-noise objects.
<L extends ClassLabel<L>> Database<O>	Clusters.associate(Class<L> classLabel)
<L extends ClassLabel<L>> Database<O>	ClustersPlusNoise.associate(Class<L> classLabel)
<L extends ClassLabel<L>> Database<O>	ClusteringResult.associate(Class<L> classLabel) Returns a new Database containing only non-noise Objects with a clusterID associated as AssociationID#CLASS.
private Database<RealVector>	SubspaceClusterMap.buildDerivatorDB(Database<ParameterizationFunction> database, Set<Integer> ids) Builds a database for the derivator consisting of the ids in the specified interval.
Database<O>	PartitionClusteringResults.noise()
Database<O>	Clusters.noise()
Database<O>	ClustersPlusNoise.noise()
Database<O>	ClusteringResult.noise() Returns a database containing only noise objects.

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

<L extends ClassLabel<L>> Map<L,Database<O>>	PartitionClusteringResults.clustering(Class<L> classLabel) Returns a mapping to databases containing only non-noise objects.
<L extends ClassLabel<L>> Map<L,Database<O>>	Clusters.clustering(Class<L> classLabel)
<L extends ClassLabel<L>> Map<L,Database<O>>	ClustersPlusNoise.clustering(Class<L> classLabel)
<L extends ClassLabel<L>> Map<L,Database<O>>	ClusteringResult.clustering(Class<L> classLabel) Returns a Map of ClassLabel to Database, comprising a separate database for each cluster.

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

void	SubspaceClusterMap.add(Integer dimensionality, Set<Integer> ids, Database<ParameterizationFunction> database) Adds a cluster with the specified subspace dimensionality and the specified ids to this map.
private Database<RealVector>	SubspaceClusterMap.buildDerivatorDB(Database<ParameterizationFunction> database, Set<Integer> ids) Builds a database for the derivator consisting of the ids in the specified interval.
private LinearEquationSystem	SubspaceClusterMap.runDerivator(Database<ParameterizationFunction> database, int dimensionality, Set<Integer> ids) Runs the derivator on the specified inerval and assigns all points having a distance less then the standard deviation of the derivator model to the model to this model.

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

CASHResult(Database<ParameterizationFunction> db, SubspaceClusterMap clusterMap, int dimensionality)
todo

CLIQUEModel(Database<V> db, CLIQUESubspace<V> subspace)
Creates a new cluster model for a cluster in the CLIQUE algorithm with the specified parameters.

ClusterOrder(Database<O> database, DistanceFunction<O,D> distanceFunction)
Provides the cluster order of the OPTICS algorithm.

Clusters(Integer[][] clusters, Database<O> db)
Provides a result of a clustering-algorithm that computes several clusters and remaining noise.

ClustersPlusNoise(Integer[][] clustersAndNoise, Database<O> db)
Provides a result of a clustering-algorithm that computes several clusters and remaining noise.

ClustersPlusNoisePlusCorrelationAnalysis(Integer[][] clustersAndNoise, Database<V> db, CorrelationAnalysisSolution<V>[] correlationAnalysisSolutions)
Provides a result of a clustering-algorithm that computes several clusters and remaining noise and a correlation analysis for each cluster

ClustersPlusNoisePlusCorrelationAnalysis(Integer[][] clustersAndNoise, Database<V> db, CorrelationAnalysisSolution<V>[] correlationAnalysisSolutions, NumberFormat nf)
Provides a result of a clustering-algorithm that computes several clusters and remaining noise and a correlation analysis for each cluster

EMClusters(Integer[][] clusters, Database<V> db)

EMModel(Database<V> db, V mean, Matrix covarianceMatrix)
todo comment

HierarchicalAxesParallelCorrelationClusters(List<HierarchicalAxesParallelCorrelationCluster> rootClusters, ClusterOrder<V,D> clusterOrder, Database<V> db)
Provides a result of a clustering algorithm that computes hierarchical axes parallel correlation clusters from a cluster order.

HierarchicalCASHClusters(HierarchicalCASHCluster rootCluster, Database<ParameterizationFunction> db)
Provides a result of a clustering algorithm that computes correlation clusters in arbitrary subspaces.

HierarchicalClusters(C rootCluster, Database<O> db)
Provides a result of a clustering algorithm that computes hierarchical clusters from a cluster order.

HierarchicalClusters(List<C> rootClusters, Database<O> db)
Provides a result of a clustering algorithm that computes hierarchical clusters from a cluster order.

HierarchicalCorrelationClusters(List<HierarchicalCorrelationCluster<V>> rootClusters, Database<V> db)
Provides a result of a clustering algorithm that computes correlation clusters in arbitrary subspaces.

PartitionClusteringResults(Database<O> db, Map<Integer,ClusteringResult<O>> resultMap, Integer noise)
A result for a partitioning algorithm providing a single result for a single partition.

SubspaceClusterModel(Database<V> db, BitSet attributes)

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

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

private Database<V>

Bicluster.database The database this bilcuster is defined for.

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

Bicluster(int[] rowIDs, int[] colIDs, Database<V> database)
Defines a new bicluster for given parameters.

Biclustering(Database<V> database)
Provides a Result.

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

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

class	AbstractDatabase<O extends DatabaseObject> 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	IndexDatabase<O extends DatabaseObject> IndexDatabase is a database implementation which is supported by an index structure.
class	InvertedListDatabase<N extends Number,O extends FeatureVector<O,N>> Database implemented by inverted lists that supports range queries on a specific dimension.
class	MetricalIndexDatabase<O extends DatabaseObject,D extends Distance<D>,N extends MetricalNode<N,E>,E extends MTreeEntry<D>> MetricalIndexDatabase is a database implementation which is supported by a metrical index structure.
class	SequentialDatabase<O extends DatabaseObject> SequentialDatabase is a simple implementation of a Database.
class	SpatialIndexDatabase<O extends NumberVector<O,?>,N extends SpatialNode<N,E>,E extends SpatialEntry> SpatialIndexDatabase is a database implementation which is supported by a spatial index structure.

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

Map<Integer,Database<O>>	Database.partition(Map<Integer,List<Integer>> partitions) Returns a Map of partition IDs to Databases according to the specified Map of partition IDs to Lists of IDs.
Map<Integer,Database<O>>	AbstractDatabase.partition(Map<Integer,List<Integer>> partitions)
Map<Integer,Database<O>>	Database.partition(Map<Integer,List<Integer>> partitions, Class<? extends Database<O>> dbClass, String[] dbParameters) Returns a Map of partition IDs to Databases of the specified class according to the specified Map of partition IDs to Lists of IDs.
Map<Integer,Database<O>>	AbstractDatabase.partition(Map<Integer,List<Integer>> partitions, Class<? extends Database<O>> dbClass, String[] dbParameters)

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

Map<Integer,Database<O>>	Database.partition(Map<Integer,List<Integer>> partitions, Class<? extends Database<O>> dbClass, String[] dbParameters) Returns a Map of partition IDs to Databases of the specified class according to the specified Map of partition IDs to Lists of IDs.
Map<Integer,Database<O>>	AbstractDatabase.partition(Map<Integer,List<Integer>> partitions, Class<? extends Database<O>> dbClass, String[] dbParameters)

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

DatabaseEvent(Database<? extends DatabaseObject> source, List<Integer> objectIDs)
Used to create an event when database objects have been updated, inserted, or removed.

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

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

(package private) Database<O>

AbstractDatabaseConnection.database The database.

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

private ClassParameter<Database>

AbstractDatabaseConnection.DATABASE_PARAM Parameter to specify the database to be provided by the parse method.

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

Database<MultiRepresentedObject<O>>	MultipleFileBasedDatabaseConnection.getDatabase(Normalization<MultiRepresentedObject<O>> normalization)
Database<O>	DatabaseConnection.getDatabase(Normalization<O> normalization) Returns a Database according to parameter settings.
Database<O>	InputStreamDatabaseConnection.getDatabase(Normalization<O> normalization)

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

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

private Database<O>

AbstractMeasurementFunction.database The database that holds the DatabaseObject for which the distances should be computed.

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

protected Database<O>

AbstractMeasurementFunction.getDatabase() Returns the database holding the objects for which the measurements should be computed.

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

void	AbstractMeasurementFunction.setDatabase(Database<O> database, boolean verbose, boolean time)
void	MeasurementFunction.setDatabase(Database<O> database, boolean verbose, boolean time) Set the database that holds the associations for the DatabaseObject for which the measurements should be computed.

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

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

void	FrequencyDependentItemsetDistanceFunction.setDatabase(Database<BitVector> database, boolean verbose, boolean time) Sets the database, initializes a new map of frequencies.
void	KernelBasedLocallyWeightedDistanceFunction.setDatabase(Database<O> database, boolean verbose, boolean time)
void	AbstractPreprocessorBasedDistanceFunction.setDatabase(Database<O> database, boolean verbose, boolean time) Runs the preprocessor on the database.
void	AbstractLocallyWeightedDistanceFunction.setDatabase(Database<O> database, boolean verbose, boolean time)
void	FractalDimensionBasedDistanceFunction.setDatabase(Database<V> database, boolean verbose, boolean time)

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

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

void	AbstractPreprocessorBasedSimilarityFunction.setDatabase(Database<O> database, boolean verbose, boolean time) Runs the preprocessor on the database.

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

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

void	ArbitraryKernelFunctionWrapper.setDatabase(Database<O> database, boolean verbose, boolean time)

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

KernelMatrix(KernelFunction<O,DoubleDistance> kernelFunction, Database<O> database)
Provides a new kernel matrix.

KernelMatrix(KernelFunction<O,DoubleDistance> kernelFunction, Database<O> database, List<Integer> ids)
Provides a new kernel matrix.

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

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

void	Index.setDatabase(Database<O> database) Sets the database in the distance function of this index (if existing).

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

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

void	AbstractMTree.setDatabase(Database<O> database) Sets the databse in the distance function of this index (if existing).

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

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

void	SpatialIndex.setDatabase(Database<O> database) Sets the databse in the distance function of this index (if existing).

Uses of Database in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rdknn

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

void	RdKNNTree.setDatabase(Database<O> database) Sets the databse in the distance function of this index.

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

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

private Database<O>

PreprocessorHandler.database The database to run the preprocessor on.

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

private BitSet	HiSCPreprocessor.determinePreferenceVector(Database<V> database, Integer id, List<Integer> neighborIDs, StringBuffer msg) Determines the preference vector according to the specified neighbor ids.
private BitSet	DiSHPreprocessor.determinePreferenceVector(Database<V> database, Set<Integer>[] neighborIDs, StringBuffer msg) Determines the preference vector according to the specified neighbor ids.
private BitSet	DiSHPreprocessor.determinePreferenceVectorByApriori(Database<V> database, Set<Integer>[] neighborIDs, StringBuffer msg) Determines the preference vector with the apriori strategy.
private DimensionSelectingDistanceFunction<N,V>[]	DiSHPreprocessor.initDistanceFunctions(Database<V> database, int dimensionality, boolean verbose, boolean time) Initializes the dimension selecting distancefunctions to determine the preference vectors.
protected abstract List<Integer>	HiCOPreprocessor.objectIDsForPCA(Integer id, Database<V> database, boolean verbose, boolean time) Returns the ids of the objects stored in the specified database to be considerd within the PCA for the specified object id.
protected List<Integer>	RangeQueryBasedHiCOPreprocessor.objectIDsForPCA(Integer id, Database<V> database, boolean verbose, boolean time)
protected List<Integer>	KnnQueryBasedHiCOPreprocessor.objectIDsForPCA(Integer id, Database<V> database, boolean verbose, boolean time)
void	SharedNearestNeighborsPreprocessor.run(Database<O> database, boolean verbose, boolean time) Annotates the nearest neighbors based on the values of SharedNearestNeighborsPreprocessor.numberOfNeighbors and SharedNearestNeighborsPreprocessor.distanceFunction to each database object.
void	Preprocessor.run(Database<O> database, boolean verbose, boolean time) This method executes the actual preprocessing step of this Preprocessor for the objects of the specified database.
void	DiSHPreprocessor.run(Database<V> database, boolean verbose, boolean time)
void	ProjectedDBSCANPreprocessor.run(Database<V> database, boolean verbose, boolean time)
void	HiCOPreprocessor.run(Database<V> database, boolean verbose, boolean time) This method determines the correlation dimensions of the objects stored in the specified database and sets the necessary associations in the database.
void	HiSCPreprocessor.run(Database<V> database, boolean verbose, boolean time)
void	FracClusPreprocessor.run(Database<V> database, boolean verbose, boolean time)
void	PreprocessorHandler.runPreprocessor(Database<O> database, boolean verbose, boolean time) Runs the preprocessor on the database if the omit flag is not set or the database does contain the association id neither for any id nor as global association.
protected void	KernelFourCPreprocessor.runVarianceAnalysis(Integer id, List<QueryResult<D>> neighbors, Database<V> database) This method implements the type of variance analysis to be computed for a given point.
protected void	PreDeConPreprocessor.runVarianceAnalysis(Integer id, List<QueryResult<D>> neighbors, Database<V> database) TODO provide correct commentary This method implements the type of variance analysis to be computed for a given point.
protected abstract void	ProjectedDBSCANPreprocessor.runVarianceAnalysis(Integer id, List<QueryResult<D>> neighbors, Database<V> database) This method implements the type of variance analysis to be computed for a given point.
protected void	FourCPreprocessor.runVarianceAnalysis(Integer id, List<QueryResult<D>> neighbors, Database<V> database) This method implements the type of variance analysis to be computed for a given point.

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

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

static <O extends RealVector<O,?>> O	Util.centroid(Database<O> database) Returns the centroid as a RealVector object of the specified database.
static <V extends RealVector<V,?>> V	Util.centroid(Database<V> database, Collection<Integer> ids) Returns the centroid as a RealVector object of the specified objects stored in the given database.
static <V extends RealVector<V,?>> V	Util.centroid(Database<V> database, Collection<Integer> ids, BitSet bitSet) Returns the centroid w.r.t. the dimensions specified by the given BitSet as a RealVector object of the specified objects stored in the given database.
static <O extends RealVector<O,?>> Matrix	Util.covarianceMatrix(Database<O> database) Determines the covariance matrix of the objects stored in the given database.
static <V extends RealVector<V,?>> Matrix	Util.covarianceMatrix(Database<V> database, Collection<Integer> ids) Determines the covariance matrix of the objects stored in the given database.
static SortedSet<ClassLabel<?>>	Util.getClassLabels(Database<?> database) Retrieves all class labels within the database.
static <O extends DatabaseObject> Collection<Integer>	Util.getDatabaseIDs(Database<O> db) Returns a collection of the ids of the objects stored in the specified database.
static double[][]	Util.min_max(Database<RealVector<?,?>> database) Determines the minimum and maximum values in each dimension of all objects stored in the given database.
static <O extends RealVector<O,?>> double[]	Util.variances(Database<O> database) Determines the variances in each dimension of all objects stored in the given database.
static double[]	Util.variances(Database<RealVector<?,?>> database, RealVector<?,?> centroid, Collection<Integer>[] ids) Determines the variances in each dimension of the specified objects stored in the given database.
static <V extends RealVector<V,?>> double[]	Util.variances(Database<V> database, Collection<Integer> ids) Determines the variances in each dimension of the specified objects stored in the given database.
static <V extends RealVector<V,?>> double[]	Util.variances(Database<V> database, V centroid, Collection<Integer> ids) Determines the variances in each dimension of the specified objects stored in the given database.

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

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

protected Matrix	LocalKernelPCA.pcaMatrix(Database<V> database, Collection<Integer> ids) Returns the local kernel matrix of the specified ids.
protected Matrix	LinearLocalPCA.pcaMatrix(Database<V> database, Collection<Integer> ids) Returns the covariance matrix of the specified ids.
protected abstract Matrix	LocalPCA.pcaMatrix(Database<V> database, Collection<Integer> ids) Determines and returns the matrix that is used for performaing the pca.
void	LocalPCA.run(Collection<Integer> ids, Database<V> database) Performs a LocalPCA for the object with the specified ids stored in the given database.
void	GlobalPCA.run(Database<O> database) Computes the principal components for objects of the given database.