Environment for
DeveLoping
KDD-Applications
Supported by Index-Structures

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

Packages that use Database
de.lmu.ifi.dbs.elki.algorithm Algorithms suitable as a task for the KDDTask main routine. 
de.lmu.ifi.dbs.elki.algorithm.clustering Clustering algorithms Clustering algorithms are supposed to implement the Algorithm-Interface. 
de.lmu.ifi.dbs.elki.algorithm.clustering.correlation Correlation clustering algorithms 
de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.cash Helper classes for the CASH algorithm. 
de.lmu.ifi.dbs.elki.algorithm.clustering.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.subspace.clique Helper classes for the CLIQUE algorithm. 
de.lmu.ifi.dbs.elki.algorithm.outlier Outlier detection algorithms 
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.visualization Visualization applications in ELKI. 
de.lmu.ifi.dbs.elki.data.model Cluster models classes for various algorithms. 
de.lmu.ifi.dbs.elki.database ELKI database layer - loading, storing, indexing and accessing data 
de.lmu.ifi.dbs.elki.database.connection Database connections are classes implementing data sources. 
de.lmu.ifi.dbs.elki.distance Distances and (in subpackages) distance functions and similarity functions
de.lmu.ifi.dbs.elki.distance.distancefunction Distance functions for use within ELKI. 
de.lmu.ifi.dbs.elki.distance.distancefunction.adapter Distance functions deriving distances from e.g. similarity measures 
de.lmu.ifi.dbs.elki.distance.similarityfunction Similarity functions. 
de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel Kernel functions. 
de.lmu.ifi.dbs.elki.evaluation Functionality for the evaluation of algorithms. 
de.lmu.ifi.dbs.elki.index Index structure implementations 
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants M-Tree and variants. 
de.lmu.ifi.dbs.elki.index.tree.spatial Tree-based index structures for spatial indexing. 
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rdknn RdKNNTree 
de.lmu.ifi.dbs.elki.math.linearalgebra.pca Principal Component Analysis (PCA) and Eigenvector processing. 
de.lmu.ifi.dbs.elki.preprocessing Preprocessors used for data preparation in a first step of various algorithms or distance and similarity measures. 
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.visualization.scales Scales handling for plotting. 
 

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.
 CorrelationAnalysisSolution<V> DependencyDerivator.generateModel(Database<V> db, Collection<Integer> ids)
          Runs the pca on the given set of IDs.
 CorrelationAnalysisSolution<V> DependencyDerivator.generateModel(Database<V> db, Collection<Integer> ids, V centroidDV)
          Runs the pca on the given set of IDs and for the given centroid.
 R Algorithm.run(Database<O> database)
          Runs the algorithm.
 R AbstractAlgorithm.run(Database<O> database)
          Calls the runInTime()-method of extending classes.
protected  AprioriResult APRIORI.runInTime(Database<BitVector> database)
          Performs the APRIORI algorithm on the given database.
protected abstract  R AbstractAlgorithm.runInTime(Database<O> database)
          The run method encapsulated in measure of runtime.
protected  KNNDistanceOrderResult<D> KNNDistanceOrder.runInTime(Database<O> database)
          Provides an order of the kNN-distances for all objects within the specified database.
protected  Result DummyAlgorithm.runInTime(Database<V> database)
          Iterates over all points in the database.
protected  Result NullAlgorithm.runInTime(Database<V> database)
          Iterates over all points in the database.
 CorrelationAnalysisSolution<V> DependencyDerivator.runInTime(Database<V> db)
          Computes quantitatively linear dependencies among the attributes of the given database based on a linear correlation PCA.
protected  AnnotationFromHashMap<KNNList<D>> KNNJoin.runInTime(Database<V> database)
          Joins in the given spatial database to each object its k-nearest neighbors.
protected  CollectionResult<CTriple<Integer,Integer,Double>> MaterializeDistances.runInTime(Database<V> database)
          Iterates over all points in the database.
 

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 SNNClustering.expandCluster(Database<O> database, Integer startObjectID, FiniteProgress objprog, IndefiniteProgress clusprog)
          DBSCAN-function expandCluster adapted to SNN criterion.
protected  void DBSCAN.expandCluster(Database<O> database, Integer startObjectID, FiniteProgress objprog, IndefiniteProgress clusprog)
          DBSCAN-function expandCluster.
protected  void ProjectedDBSCAN.expandCluster(Database<V> database, Integer startObjectID, FiniteProgress objprog, IndefiniteProgress clusprog)
          ExpandCluster function of DBSCAN.
protected  void OPTICS.expandClusterOrder(Database<O> database, Integer objectID, FiniteProgress 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)
          Returns the shared nearest neighbors of the specified query object in the given database.
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.
 C ClusteringAlgorithm.run(Database<O> database)
          Runs the algorithm.
protected  Clustering<Model> SNNClustering.runInTime(Database<O> database)
          Performs the SNN clustering algorithm on the given database.
protected  ClusterOrderResult<D> DeLiClu.runInTime(Database<O> database)
          Performs the DeLiClu algorithm on the given database.
protected  Clustering<Model> ByLabelClustering.runInTime(Database<O> database)
          Run the actual clustering algorithm.
protected  Clustering<Model> DBSCAN.runInTime(Database<O> database)
          Performs the DBSCAN algorithm on the given database.
protected  MultiResult SLINK.runInTime(Database<O> database)
          Performs the SLINK algorithm on the given database.
protected  ClusterOrderResult<D> OPTICS.runInTime(Database<O> database)
          Performs the OPTICS algorithm on the given database.
protected  Clustering<Model> TrivialAllInOne.runInTime(Database<O> database)
          Run the actual clustering algorithm.
protected  Clustering<Model> TrivialAllNoise.runInTime(Database<O> database)
          Run the actual clustering algorithm.
protected  Clustering<Model> ByLabelHierarchicalClustering.runInTime(Database<O> database)
          Run the actual clustering algorithm.
protected  Clustering<Model> ProjectedDBSCAN.runInTime(Database<V> database)
           
protected  Clustering<EMModel<V>> EM.runInTime(Database<V> database)
          Performs the EM clustering algorithm on the given database.
protected  Clustering<Model> KMeans.runInTime(Database<V> database)
          Performs the k-means 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.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<V>> COPAC.PARTITION_DB_PARAM
          Parameter to specify the database class for each partition, must extend Database.
private  Class<? extends Database<V>> COPAC.partitionDatabase
          Holds the instance of the partition database specified by COPAC.PARTITION_DB_PARAM.
 

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<DoubleVector> CASH.buildDerivatorDB(Database<ParameterizationFunction> database, CASHInterval interval)
          Builds a database for the derivator consisting of the ids in the specified interval.
private  Database<DoubleVector> CASH.buildDerivatorDB(Database<ParameterizationFunction> database, Set<Integer> 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
private  void ORCLUS.assign(Database<V> database, List<ORCLUS.ORCLUSCluster> 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<DoubleVector> CASH.buildDerivatorDB(Database<ParameterizationFunction> database, CASHInterval interval)
          Builds a database for the derivator consisting of the ids in the specified interval.
private  Database<DoubleVector> CASH.buildDerivatorDB(Database<ParameterizationFunction> database, Set<Integer> ids)
          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 parameterization functions stored in the specified database.
private  Clustering<Model> CASH.doRun(Database<ParameterizationFunction> database, FiniteProgress progress)
          Runs the CASH algorithm on the specified database, this method is recursively called until only noise is left.
private  SortedMap<Integer,List<Cluster<CorrelationModel<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.ORCLUSCluster 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.ORCLUSCluster> 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.ORCLUSCluster> clusters, int k_new, int d_new)
          Reduces the number of seeds to k_new
private  ORCLUS.ProjectedEnergy ORCLUS.projectedEnergy(Database<V> database, ORCLUS.ORCLUSCluster c_i, ORCLUS.ORCLUSCluster 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 interval and assigns all points having a distance less then the standard deviation of the derivator model to the model to this model.
private  LinearEquationSystem CASH.runDerivator(Database<ParameterizationFunction> database, int dimensionality, Set<Integer> ids)
          Runs the derivator on the specified interval and assigns all points having a distance less then the standard deviation of the derivator model to the model to this model.
protected  Clustering<Model> CASH.runInTime(Database<ParameterizationFunction> database)
          Performs the CASH algorithm on the given database.
protected  Clustering<CorrelationModel<V>> ERiC.runInTime(Database<V> database)
          Performs the ERiC algorithm on the given database.
protected  Clustering<Model> ORCLUS.runInTime(Database<V> database)
          Performs the ORCLUS algorithm on the given database.
protected  Clustering<Model> COPAC.runInTime(Database<V> database)
          Performs the COPAC algorithm on the given database.
private  void COPAC.runPartitionAlgorithm(Database<V> database, Map<Integer,List<Integer>> partitionMap)
          Runs the partition algorithm and creates the result.
private  ORCLUS.ORCLUSCluster ORCLUS.union(Database<V> database, ORCLUS.ORCLUSCluster c1, ORCLUS.ORCLUSCluster 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.PROCLUSCluster> 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<Cluster<AxesModel>> clusters, int dimensionality)
          Builds the cluster hierarchy
private  void DiSH.checkClusters(Database<V> database, DiSHDistanceFunction<V,DiSHPreprocessor<V,?>> distanceFunction, Map<BitSet,List<Pair<BitSet,DatabaseObjectGroupCollection<List<Integer>>>>> clustersMap)
          Removes the clusters with size < minpts from the cluster map and adds them to their parents.
private  void DiSH.computeClusters(Database<V> database, ClusterOrderResult<PreferenceVectorBasedCorrelationDistance> clusterOrder)
          Computes the hierarchical clusters according to the cluster order.
private  Map<CLIQUESubspace<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.PROCLUSCluster> clusters, Map<Integer,Set<Integer>> dimensions, Database<V> database)
          Evaluates the quality of the clusters.
private  Map<BitSet,List<Pair<BitSet,DatabaseObjectGroupCollection<List<Integer>>>>> DiSH.extractClusters(Database<V> database, DiSHDistanceFunction<V,DiSHPreprocessor<V,?>> distanceFunction, ClusterOrderResult<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  Pair<BitSet,DatabaseObjectGroupCollection<List<Integer>>> DiSH.findParent(Database<V> database, DiSHDistanceFunction<V,DiSHPreprocessor<V,?>> distanceFunction, Pair<BitSet,DatabaseObjectGroupCollection<List<Integer>>> child, Map<BitSet,List<Pair<BitSet,DatabaseObjectGroupCollection<List<Integer>>>>> clustersMap)
          Returns the parent of the specified cluster
private  Map<Integer,List<DistanceResultPair<DoubleDistance>>> PROCLUS.getLocalities(Set<Integer> m_c, Database<V> database)
          Computes the localities of the specified medoids.
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, Cluster<AxesModel> parent, List<Cluster<AxesModel>> children)
          Returns true, if the specified parent cluster is a parent of one child of the children clusters.
protected  Clustering<AxesModel> DiSH.runInTime(Database<V> database)
          Performs the DiSH algorithm on the given database.
protected  Clustering<Model> PROCLUS.runInTime(Database<V> database)
          Performs the PROCLUS algorithm on the given database.
protected  Clustering<CLIQUESubspace<V>> CLIQUE.runInTime(Database<V> database)
          Performs the CLIQUE algorithm on the given database.
 

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<CLIQUESubspace<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.outlier
 

Methods in de.lmu.ifi.dbs.elki.algorithm.outlier with parameters of type Database
private  PriorityQueue<FCPair<Double,Integer>> ABOD.calcDistsandNN(Database<V> data, KernelMatrix<V> kernelMatrix, int sampleSize, Integer aKey, HashMap<Integer,Double> dists)
           
private  PriorityQueue<FCPair<Double,Integer>> ABOD.calcDistsandRNDSample(Database<V> data, KernelMatrix<V> kernelMatrix, int sampleSize, Integer aKey, HashMap<Integer,Double> dists)
           
private  void ABOD.generateExplanation(Database<V> data, Integer key, LinkedList<Integer> expList)
           
 void ABOD.getExplanations(Database<V> data)
           
 MultiResult ABOD.getFastRanking(Database<V> database, int k, int sampleSize)
          Main part of the algorithm.
private  KNNList<DoubleDistance> SOD.getKNN(Database<V> database, Integer queryObject)
          Provides the k nearest neighbors in terms of the shared nearest neighbor distance.
 MultiResult ABOD.getRanking(Database<V> database, int k)
          Main part of the algorithm.
protected  MultiResult LOCI.runInTime(Database<O> database)
          Runs the algorithm in the timed evaluation part.
protected  MultiResult LOF.runInTime(Database<O> database)
          Performs the Generalized LOF_SCORE algorithm on the given database.
protected  MultiResult SOD.runInTime(Database<V> database)
          Performs the SOD algorithm on the given database.
protected  MultiResult ABOD.runInTime(Database<V> database)
           
 

Constructors in de.lmu.ifi.dbs.elki.algorithm.outlier with parameters of type Database
SODModel(Database<O> database, List<Integer> neighborhood, double alpha, O queryObject)
           
 

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
private  DoubleMinMax DistanceStatisticsWithClasses.exactMinMax(Database<V> database, DistanceFunction<V,D> distFunc)
           
protected  CollectionResult<DoubleVector> EvaluateRankingQuality.runInTime(Database<V> database)
          Run the algorithm.
protected  CollectionResult<DoubleVector> RankingQualityHistogram.runInTime(Database<V> database)
          Run the algorithm.
protected  CollectionResult<DoubleVector> DistanceStatisticsWithClasses.runInTime(Database<V> database)
          Iterates over all points in the database.
private  DoubleMinMax DistanceStatisticsWithClasses.sampleMinMax(Database<V> database, DistanceFunction<V,D> distFunc)
           
 

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

Fields in de.lmu.ifi.dbs.elki.application.visualization declared as Database
protected  Database<O> KNNExplorer.ExplorerWindow.db
           
 

Methods in de.lmu.ifi.dbs.elki.application.visualization with parameters of type Database
 void KNNExplorer.ExplorerWindow.run(Database<O> db, DistanceFunction<O,N> distanceFunction)
           
 

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

Fields in de.lmu.ifi.dbs.elki.data.model declared as Database
private  Database<V> Bicluster.database
          The database this bicluster is defined for.
 

Methods in de.lmu.ifi.dbs.elki.data.model that return Database
 Database<V> Bicluster.getDatabase()
          Getter to retrieve the database
 

Constructors in de.lmu.ifi.dbs.elki.data.model with parameters of type Database
Bicluster(int[] rowIDs, int[] colIDs, Database<V> database)
          Defines a new bicluster for given parameters.
BiclusterWithInverted(int[] rowIDs, int[] colIDs, Database<V> database)
           
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.
 

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 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 Database
<O extends DatabaseObject,R extends Clustering<M>,M extends Model,L extends ClassLabel>
Database<O>
LabelsFromClustering.makeDatabaseFromClustering(Database<O> olddb, R clustering, Class<L> classLabel)
          Retrieve a cloned database that - does not contain noise points - has labels assigned based on the given clustering Useful for e.g. training a classifier based on a clustering.
 

Methods in de.lmu.ifi.dbs.elki.database that return types with arguments of type Database
<O extends DatabaseObject,R extends Clustering<M>,M extends Model>
Map<Cluster<M>,Database<O>>
PartitionsFromClustering.makeDatabasesFromClustering(Database<O> olddb, R clustering)
          Use an existing clustering to partition a database.
<O extends DatabaseObject,R extends Clustering<M>,M extends Model,L extends ClassLabel>
Map<L,Database<O>>
PartitionsFromClustering.makeDatabasesFromClustering(Database<O> olddb, R clustering, Class<L> classLabel)
          Use an existing clustering to partition a 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, List<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, List<String> dbParameters)
           
 

Methods in de.lmu.ifi.dbs.elki.database with parameters of type Database
<O extends DatabaseObject,R extends Clustering<M>,M extends Model,L extends ClassLabel>
Database<O>
LabelsFromClustering.makeDatabaseFromClustering(Database<O> olddb, R clustering, Class<L> classLabel)
          Retrieve a cloned database that - does not contain noise points - has labels assigned based on the given clustering Useful for e.g. training a classifier based on a clustering.
<O extends DatabaseObject,R extends Clustering<M>,M extends Model>
Map<Cluster<M>,Database<O>>
PartitionsFromClustering.makeDatabasesFromClustering(Database<O> olddb, R clustering)
          Use an existing clustering to partition a database.
<O extends DatabaseObject,R extends Clustering<M>,M extends Model,L extends ClassLabel>
Map<L,Database<O>>
PartitionsFromClustering.makeDatabasesFromClustering(Database<O> olddb, R clustering, Class<L> classLabel)
          Use an existing clustering to partition a database.
 

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, List<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, List<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
          Holds the instance of the database specified by AbstractDatabaseConnection.DATABASE_PARAM.
 

Fields in de.lmu.ifi.dbs.elki.database.connection with type parameters of type Database
private  ClassParameter<Database<O>> AbstractDatabaseConnection.DATABASE_PARAM
          Parameter to specify the database to be provided by the parse method, must extend Database.
 

Methods in de.lmu.ifi.dbs.elki.database.connection that return Database
 Database<MultiRepresentedObject<O>> MultipleFileBasedDatabaseConnection.getDatabase(Normalization<MultiRepresentedObject<O>> normalization)
           
 Database<O> InputStreamDatabaseConnection.getDatabase(Normalization<O> normalization)
           
 Database<O> DatabaseConnection.getDatabase(Normalization<O> normalization)
          Returns a Database according to parameter settings.
 

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 objects for which the measurements 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 AbstractPreprocessorBasedDistanceFunction.setDatabase(Database<O> database, boolean verbose, boolean time)
          Calls AbstractMeasurementFunction(database, verbose, time) and runs the preprocessor on the database.
 void AbstractLocallyWeightedDistanceFunction.setDatabase(Database<O> database, boolean verbose, boolean time)
           
 void KernelBasedLocallyWeightedDistanceFunction.setDatabase(Database<V> database, boolean verbose, boolean time)
           
 

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

Methods in de.lmu.ifi.dbs.elki.distance.distancefunction.adapter with parameters of type Database
 void SimilarityAdapterAbstract.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)
          Calls AbstractMeasurementFunction(database, verbose, time) and 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.evaluation
 

Methods in de.lmu.ifi.dbs.elki.evaluation with parameters of type Database
private  Iterator<Integer> ComputeROCCurve.getIterableResult(Database<O> database, Result result)
          Find an "iterable" result that looks like object IDs.
private  Cluster<Model> ComputeROCCurve.getReferenceCluster(Database<O> database, String class_name)
          Find the "positive" reference cluster using a by label clustering.
protected  MultiResult ComputeROCCurve.runInTime(Database<O> database)
           
 

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)
           
 

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 database 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 database in the distance function of this index.
 

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

Methods in de.lmu.ifi.dbs.elki.math.linearalgebra.pca with parameters of type Database
 PCAResult PCARunner.processDatabase(Database<V> database)
          Run PCA on the complete database
 Matrix StandardCovarianceMatrixBuilder.processDatabase(Database<V> database)
          Compute Covariance Matrix for a complete database
 Matrix CovarianceMatrixBuilder.processDatabase(Database<V> database)
          Compute Covariance Matrix for a complete database
 PCAFilteredResult PCAFilteredRunner.processIds(Collection<Integer> ids, Database<V> database)
          Run PCA on a collection of database IDs
 PCAResult PCARunner.processIds(Collection<Integer> ids, Database<V> database)
          Run PCA on a collection of database IDs
 Matrix WeightedCovarianceMatrixBuilder.processIds(Collection<Integer> ids, Database<V> database)
          Weighted Covariance Matrix for a set of IDs.
 Matrix KernelCovarianceMatrixBuilder.processIds(Collection<Integer> ids, Database<V> database)
          Returns the local kernel matrix of the specified ids.
 Matrix StandardCovarianceMatrixBuilder.processIds(Collection<Integer> ids, Database<V> database)
          Compute Covariance Matrix for a collection of database IDs
abstract  Matrix CovarianceMatrixBuilder.processIds(Collection<Integer> ids, Database<V> database)
          Compute Covariance Matrix for a collection of database IDs
 PCAFilteredResult PCAFilteredRunner.processQueryResult(Collection<DistanceResultPair<D>> results, Database<V> database)
          Run PCA on a QueryResult Collection
 PCAResult PCARunner.processQueryResult(Collection<DistanceResultPair<D>> results, Database<V> database)
          Run PCA on a QueryResult Collection
 Matrix CovarianceMatrixBuilder.processQueryResults(Collection<DistanceResultPair<D>> results, Database<V> database)
          Compute Covariance Matrix for a QueryResult Collection By default it will just collect the ids and run processIds
 Matrix WeightedCovarianceMatrixBuilder.processQueryResults(Collection<DistanceResultPair<D>> results, Database<V> database, int k)
          Compute Covariance Matrix for a QueryResult Collection By default it will just collect the ids and run processIds
 Matrix CovarianceMatrixBuilder.processQueryResults(Collection<DistanceResultPair<D>> results, Database<V> database, int k)
          Compute Covariance Matrix for a QueryResult Collection By default it will just collect the ids and run processIds
 

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  SpatialIndexDatabase<O,N,E> SpatialApproximationMaterializeKNNPreprocessor.getSpatialDatabase(Database<O> database)
          Do some (limited) type checking, then cast the database into a spatial database.
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  List<Integer> KnnQueryBasedHiCOPreprocessor.objectIDsForPCA(Integer id, Database<V> database, boolean verbose, boolean time)
           
protected  List<Integer> RangeQueryBasedHiCOPreprocessor.objectIDsForPCA(Integer id, Database<V> database, boolean verbose, boolean time)
           
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 considered within the PCA for the specified object id.
protected  List<DistanceResultPair<DoubleDistance>> KnnQueryBasedHiCOPreprocessor.resultsForPCA(Integer id, Database<V> database, boolean verbose, boolean time)
           
protected  List<DistanceResultPair<DoubleDistance>> RangeQueryBasedHiCOPreprocessor.resultsForPCA(Integer id, Database<V> database, boolean verbose, boolean time)
           
protected abstract  List<DistanceResultPair<DoubleDistance>> HiCOPreprocessor.resultsForPCA(Integer id, Database<V> database, boolean verbose, boolean time)
          Returns the ids of the objects and distances stored in the specified database to be considered within the PCA for the specified object id.
 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 MaterializeKNNPreprocessor.run(Database<O> database, boolean verbose, boolean time)
          Annotates the nearest neighbors based on the values of MaterializeKNNPreprocessor.k and MaterializeKNNPreprocessor.distanceFunction to each database object.
 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 SpatialApproximationMaterializeKNNPreprocessor.run(Database<O> database, boolean verbose, boolean time)
          Annotates the nearest neighbors based on the values of MaterializeKNNPreprocessor.k and MaterializeKNNPreprocessor.distanceFunction to each database object.
 void DiSHPreprocessor.run(Database<V> database, boolean verbose, boolean time)
           
 void HiSCPreprocessor.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 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 FourCPreprocessor.runVarianceAnalysis(Integer id, List<DistanceResultPair<D>> neighbors, Database<V> database)
          This method implements the type of variance analysis to be computed for a given point.
protected abstract  void ProjectedDBSCANPreprocessor.runVarianceAnalysis(Integer id, List<DistanceResultPair<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<DistanceResultPair<D>> neighbors, Database<V> database)
          TODO provide correct commentary This method implements the type of variance analysis to be computed for a given point.
 

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

Fields in de.lmu.ifi.dbs.elki.result declared as Database
private  Database<O> AnnotationFromDatabase.database
          database storage
protected  Database<O> OrderingFromAssociation.db
          Database
 

Methods in de.lmu.ifi.dbs.elki.result that return Database
 Database<O> AnnotationFromDatabase.getDatabase()
          Return database referenced by annotations
 

Methods in de.lmu.ifi.dbs.elki.result with parameters of type Database
 void ResultHandler.processResult(Database<O> db, R result)
          Process a result.
 void DiscardResultHandler.processResult(Database<O> db, R result)
          Process the result... by discarding
 void ResultWriter.processResult(Database<O> db, Result result)
          Process a single result.
 

Constructors in de.lmu.ifi.dbs.elki.result with parameters of type Database
AnnotationFromDatabase(Database<O> db, AssociationID<T> association)
          Constructor
OrderingFromAssociation(Database<O> db, AssociationID<T> association)
          Minimal constructor, using implied comparator and ascending order.
OrderingFromAssociation(Database<O> db, AssociationID<T> association, boolean descending)
          Constructor without comparator
OrderingFromAssociation(Database<O> db, AssociationID<T> association, Comparator<T> comparator, boolean descending)
          Full Constructor (with comparator)
 

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
 void TextWriter.output(Database<O> db, Result r, StreamFactory streamOpener)
          Stream output.
protected  void TextWriter.printSettings(Database<O> db, TextWriterStream out, List<AttributeSettings> settings)
          Writes a header providing information concerning the underlying database and the specified parameter-settings.
private  void TextWriter.writeGroupResult(Database<O> db, StreamFactory streamOpener, DatabaseObjectGroup group, List<AnnotationResult<?>> ra, List<OrderingResult> ro, NamingScheme naming, List<AttributeSettings> settings)
           
private  void TextWriter.writeIterableResult(Database<O> db, StreamFactory streamOpener, IterableResult<?> ri, List<MetadataResult> rm)
           
 

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
DatabaseUtil.centroid(Database<O> database)
          Returns the centroid as a RealVector object of the specified database.
static
<V extends RealVector<V,?>>
V
DatabaseUtil.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
DatabaseUtil.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
<V extends RealVector<V,?>>
V
DatabaseUtil.centroid(Database<V> database, Iterator<Integer> iter, 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
DatabaseUtil.covarianceMatrix(Database<O> database)
          Determines the covariance matrix of the objects stored in the given database.
static
<O extends RealVector<O,?>>
Matrix
DatabaseUtil.covarianceMatrix(Database<O> database, O centroid)
           Determines the covariance matrix of the objects stored in the given database w.r.t. the given centroid.
static
<V extends RealVector<V,?>>
Matrix
DatabaseUtil.covarianceMatrix(Database<V> database, Collection<Integer> ids)
          Determines the covariance matrix of the objects stored in the given database.
static
<O extends DatabaseObject>
Class<? extends DatabaseObject>
DatabaseUtil.getBaseObjectClassExpensive(Database<O> database)
          Do a full inspection of the database to find the base object class.
static SortedSet<ClassLabel> DatabaseUtil.getClassLabels(Database<?> database)
          Retrieves all class labels within the database.
static
<O extends DatabaseObject>
Class<? extends O>
DatabaseUtil.guessObjectClass(Database<O> database)
          Do a cheap guess at the databases object class.
static double[][] DatabaseUtil.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[]
DatabaseUtil.variances(Database<O> database)
          Determines the variances in each dimension of all objects stored in the given database.
static double[] DatabaseUtil.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[]
DatabaseUtil.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[]
DatabaseUtil.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.visualization
 

Methods in de.lmu.ifi.dbs.elki.visualization with parameters of type Database
 void ResultROCCurveVisualizer.processResult(Database<O> db, MultiResult result)
           
 

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

Methods in de.lmu.ifi.dbs.elki.visualization.scales with parameters of type Database
static
<O extends DoubleVector>
LinearScale[]
Scales.calcScales(Database<O> db)
          Compute a linear scale for each dimension.
 


Release 0.2.1 (2009-07-13_1605)