R-Index (ELKI: Environment for DeveLoping KDD-Applications Supported by Index-Structures)

A B C D E F G H I J K L M N O P Q R S T U V W X Y
All Classes All Packages

R

r1s - Variable in class elki.clustering.kmeans.HartiganWongKMeans.Instance: The value [NC(L1) * D(I,L1)^2] / [NC(L1) -1] will be remembered and will remain the same for Point I until cluster L1 is updated.
RadiusCriterion - Class in elki.clustering.hierarchical.birch: Average Radius (R) criterion.
RadiusCriterion() - Constructor for class elki.clustering.hierarchical.birch.RadiusCriterion
RadiusCriterion.Par - Class in elki.clustering.hierarchical.birch: Parameterization class
RadiusDistance - Class in elki.index.tree.betula.distance: Average Radius (R) criterion.
RadiusDistance() - Constructor for class elki.index.tree.betula.distance.RadiusDistance
RadiusDistance.Par - Class in elki.index.tree.betula.distance: Parameterization class
rand - Variable in class elki.clustering.kcenter.GreedyKCenter.Par: Random factory for choosing the first element.
rand - Variable in class elki.clustering.kcenter.GreedyKCenter: Random factory for choosing the first element.
randIndex() - Method in class elki.evaluation.clustering.PairCounting: Computes the Rand index (RI).
random - Variable in class elki.clustering.hierarchical.birch.BIRCHKMeansPlusPlus: Random generator
random - Variable in class elki.clustering.kmeans.initialization.KMC2.Instance: Random generator
random - Variable in class elki.clustering.kmeans.initialization.KMeansPlusPlus.Instance: Random generator
random - Variable in class elki.clustering.kmeans.initialization.SphericalKMeansPlusPlus.Instance: Random generator
random - Variable in class elki.clustering.kmedoids.CLARA.Par: Random factory for initialization.
random - Variable in class elki.clustering.kmedoids.CLARA: Random factory for initialization.
random - Variable in class elki.clustering.kmedoids.CLARANS.Par: Random factory for initialization.
random - Variable in class elki.clustering.kmedoids.CLARANS: Random factory for initialization.
random - Variable in class elki.clustering.kmedoids.FastCLARA.Par: Random factory for initialization.
random - Variable in class elki.clustering.kmedoids.FastCLARA: Random factory for initialization.
random - Variable in class elki.clustering.kmedoids.FasterCLARA.Par: Random factory for initialization.
random - Variable in class elki.clustering.kmedoids.FasterCLARA: Random factory for initialization.
random - Variable in class elki.clustering.subspace.DOC.Par: Random seeding factory.
RANDOM_ID - Static variable in class elki.clustering.correlation.LMCLUS.Par: Random seeding
RANDOM_ID - Static variable in class elki.clustering.kcenter.GreedyKCenter.Par: Parameter for the random seed
RANDOM_ID - Static variable in class elki.clustering.kmedoids.CLARA.Par: Random generator.
RANDOM_ID - Static variable in class elki.clustering.kmedoids.CLARANS.Par: Random generator.
RANDOM_ID - Static variable in class elki.clustering.kmedoids.FastCLARA.Par: Random generator.
RANDOM_ID - Static variable in class elki.clustering.kmedoids.FasterCLARA.Par: Random generator.
RANDOM_ID - Static variable in class elki.clustering.subspace.DOC.Par: Random seeding parameter.
RANDOM_ID - Static variable in class elki.index.preprocessed.fastoptics.RandomProjectedNeighborsAndDensities.Par: Random seed parameter.
RandomlyChosen<O> - Class in elki.clustering.kmeans.initialization: Initialize K-means by randomly choosing k existing elements as initial cluster centers.
RandomlyChosen(RandomFactory) - Constructor for class elki.clustering.kmeans.initialization.RandomlyChosen: Constructor.
RandomlyChosen.Par<V> - Class in elki.clustering.kmeans.initialization: Parameterization class.
RandomNormalGenerated - Class in elki.clustering.kmeans.initialization: Initialize k-means by generating random vectors (normal distributed with \(N(\mu,\sigma)\) in each dimension).
RandomNormalGenerated(RandomFactory) - Constructor for class elki.clustering.kmeans.initialization.RandomNormalGenerated: Constructor.
RandomNormalGenerated.Par - Class in elki.clustering.kmeans.initialization: Parameterization class.
RandomProjectedNeighborsAndDensities - Class in elki.index.preprocessed.fastoptics: Random Projections used for computing neighbors and density estimates.
RandomProjectedNeighborsAndDensities(RandomFactory) - Constructor for class elki.index.preprocessed.fastoptics.RandomProjectedNeighborsAndDensities: Constructor.
RandomProjectedNeighborsAndDensities.Par - Class in elki.index.preprocessed.fastoptics: Parameterization class.
randomSample(DBIDs, int, Random, DBIDs) - Static method in class elki.clustering.kmedoids.CLARA: Draw a random sample of the desired size.
RandomUniformGenerated - Class in elki.clustering.kmeans.initialization: Initialize k-means by generating random vectors (uniform, within the value range of the data set).
RandomUniformGenerated(RandomFactory) - Constructor for class elki.clustering.kmeans.initialization.RandomUniformGenerated: Constructor.
RandomUniformGenerated.Par - Class in elki.clustering.kmeans.initialization: Parameterization class.
range - Variable in class elki.datasource.parser.ClusteringVectorParser: Range of the DBID values.
rangeQuery - Variable in class elki.clustering.dbscan.DBSCAN.Instance: Range query to use.
rangeQuery - Variable in class elki.clustering.optics.OPTICSHeap.Instance: Range query.
rangeQuery - Variable in class elki.clustering.optics.OPTICSList.Instance: Range query.
rate - Variable in class elki.clustering.kmeans.initialization.SampleKMeans: Sample size.
rate - Variable in class elki.clustering.kmeans.KMeansMinusMinus.Par: Outlier rate.
rate - Variable in class elki.clustering.kmeans.KMeansMinusMinus: Outlier rate.
RATE_ID - Static variable in class elki.clustering.kmeans.KMeansMinusMinus.Par: Parameter to specify the number of neighbors to ignore.
reachability - Variable in class elki.clustering.optics.ClusterOrder: Reachability storage.
reachability - Variable in class elki.clustering.optics.GeneralizedOPTICS.Instance: Reachability storage.
reachability - Variable in class elki.clustering.optics.OPTICSHeapEntry: The reachability of the entry.
reachability - Variable in class elki.clustering.optics.OPTICSList.Instance: Reachability storage.
reachDist - Variable in class elki.clustering.optics.FastOPTICS: Result: reachability distances
reassignToNearestCluster(IntegerDataStore, WritableIntegerDataStore, ArrayDBIDs, int, DBIDRef) - Method in class elki.clustering.silhouette.PAMSIL.Instance: Assign each object to the nearest cluster when replacing one medoid.
rebuildstat - Variable in class elki.index.tree.betula.CFTree: Number of tree rebuilds
rebuildTree() - Method in class elki.clustering.hierarchical.birch.CFTree: Rebuild the CFTree to condense it to approximately half the size.
rebuildTree() - Method in class elki.index.tree.betula.CFTree: Rebuild the CFTree to condense it to approximately half the size.
recall() - Method in class elki.evaluation.clustering.BCubed: Get the BCubed Recall (first clustering) (normalized, 0 = unequal)
recall() - Method in class elki.evaluation.clustering.PairCounting: Computes the pair-counting recall.
recompute(DBIDRef, int, double, int, double) - Method in class elki.clustering.kmedoids.CLARANS.Assignment: Recompute the assignment of one point.
recomputeCovarianceMatrices(Relation<? extends O>, WritableDataStore<double[]>, List<? extends EMClusterModel<? super O, ?>>, double) - Static method in class elki.clustering.em.EM: Recompute the covariance matrixes.
recomputeCovarianceMatrices(ArrayList<? extends ClusterFeature>, Map<ClusterFeature, double[]>, List<? extends BetulaClusterModel>, double, int) - Method in class elki.clustering.em.BetulaGMM: Recompute the covariance matrixes.
recomputeSeperation(double[]) - Method in class elki.clustering.kmeans.HamerlyKMeans.Instance: Recompute the separation of cluster means.
recomputeSeperation(double[]) - Method in class elki.clustering.kmeans.spherical.SphericalHamerlyKMeans.Instance: Recompute the separation of cluster means.
recomputeSeperation(double[][], double[][]) - Method in class elki.clustering.kmeans.CompareMeans.Instance: Recompute the separation of cluster means.
recomputeSeperation(double[], double[][]) - Method in class elki.clustering.kmeans.AbstractKMeans.Instance: Recompute the separation of cluster means.
recomputeSeperation(double[], double[][]) - Method in class elki.clustering.kmeans.spherical.SphericalElkanKMeans.Instance: Recompute the separation of cluster means.
recomputeVariance(Relation<? extends NumberVector>) - Method in class elki.clustering.kmeans.AbstractKMeans.Instance: Recompute the cluster variances.
recomputeVariance(Relation<? extends NumberVector>) - Method in class elki.clustering.kmeans.spherical.SphericalKMeans.Instance
Record() - Constructor for class elki.clustering.silhouette.FastMSC.Record
recursivelyFill(List<List<? extends Cluster<?>>>) - Method in class elki.evaluation.clustering.pairsegments.Segments
recursivelyFill(List<List<? extends Cluster<?>>>, int, SetDBIDs, SetDBIDs, int[], boolean) - Method in class elki.evaluation.clustering.pairsegments.Segments
REFERENCE_ID - Static variable in class elki.evaluation.clustering.EvaluateClustering.Par: Parameter to obtain the reference clustering.
referencealg - Variable in class elki.evaluation.clustering.EvaluateClustering.Par: Reference algorithm.
referencealg - Variable in class elki.evaluation.clustering.EvaluateClustering: Reference algorithm.
ReferenceClustering<M extends Model> - Class in elki.clustering.trivial: Reference clustering.
ReferenceClustering() - Constructor for class elki.clustering.trivial.ReferenceClustering: Constructor.
relation - Variable in class elki.clustering.biclustering.AbstractBiclustering: Relation we use.
relation - Variable in class elki.clustering.correlation.HiCO.Instance: Data relation.
relation - Variable in class elki.clustering.dbscan.predicates.ERiCNeighborPredicate.Instance: Vector data relation.
relation - Variable in class elki.clustering.kmeans.AbstractKMeans.Instance: Data relation.
relation - Variable in class elki.clustering.kmeans.initialization.KMC2.Instance: Data relation.
relation - Variable in class elki.clustering.kmeans.initialization.KMeansPlusPlus.NumberVectorInstance: Data relation.
relation - Variable in class elki.clustering.kmeans.initialization.SphericalKMeansPlusPlus.Instance: Data relation.
relation - Variable in class elki.clustering.kmeans.parallel.KMeansProcessor.Instance: Data relation.
relation - Variable in class elki.clustering.kmeans.parallel.KMeansProcessor: Data relation.
relation - Variable in class elki.clustering.subspace.HiSC.Instance: Data relation.
reset() - Method in class elki.clustering.biclustering.ChengAndChurch.BiclusterCandidate: Resets the values for the next cluster search.
resetAggregate() - Method in class elki.clustering.hierarchical.extraction.HDBSCANHierarchyExtraction.TempCluster: Reset the aggregate (for spurious clusters).
resetStatistics() - Method in class elki.clustering.hierarchical.birch.ClusteringFeature: Reset the CF to zero.
resetStatistics() - Method in class elki.index.tree.betula.features.BIRCHCF
resetStatistics() - Method in interface elki.index.tree.betula.features.ClusterFeature: Resets all statistics of CF
resetStatistics() - Method in class elki.index.tree.betula.features.VIIFeature
resetStatistics() - Method in class elki.index.tree.betula.features.VVIFeature
resetStatistics() - Method in class elki.index.tree.betula.features.VVVFeature
residue - Variable in class elki.clustering.biclustering.ChengAndChurch.BiclusterCandidate: The current bicluster score (mean squared residue).
RESTARTS_ID - Static variable in class elki.clustering.kmedoids.CLARANS.Par: The number of restarts to run.
restore(double, boolean) - Method in interface elki.clustering.hierarchical.linkage.Linkage: Restore a distance to the original scale.
restore(double, boolean) - Method in class elki.clustering.hierarchical.linkage.MinimumVarianceLinkage
restore(double, boolean) - Method in class elki.clustering.hierarchical.linkage.WardLinkage
resultList - Variable in class elki.clustering.dbscan.DBSCAN.Instance: Holds a list of clusters found.
resultList - Variable in class elki.clustering.SNNClustering: Holds a list of clusters found.
ReynoldsPAM<O> - Class in elki.clustering.kmedoids: The Partitioning Around Medoids (PAM) algorithm with some additional optimizations proposed by Reynolds et al.
ReynoldsPAM(Distance<? super O>, int, int, KMedoidsInitialization<O>) - Constructor for class elki.clustering.kmedoids.ReynoldsPAM: Constructor.
ReynoldsPAM.Instance - Class in elki.clustering.kmedoids: Instance for a single dataset.
ReynoldsPAM.Par<V> - Class in elki.clustering.kmedoids: Parameterization class.
rf - Variable in class elki.clustering.kmeans.initialization.betula.AbstractCFKMeansInitialization: Random number generator
right - Variable in class elki.clustering.em.KDTreeEM.KDTree: Interval in sorted list
rightChild - Variable in class elki.clustering.em.KDTreeEM.KDTree: Child nodes:
rightChild - Variable in class elki.clustering.kmeans.KDTreePruningKMeans.KDNode: Right child node
rightNeighbor(CLIQUEUnit, int) - Method in class elki.clustering.subspace.clique.CLIQUESubspace: Returns the right neighbor of the given unit in the specified dimension.
rnd - Variable in class elki.clustering.biclustering.ChengAndChurch: Random generator
rnd - Variable in class elki.clustering.correlation.LMCLUS.Par: Random generator
rnd - Variable in class elki.clustering.correlation.LMCLUS: Random factory
rnd - Variable in class elki.clustering.correlation.ORCLUS.Par: Random number generation.
rnd - Variable in class elki.clustering.correlation.ORCLUS: Random generator
rnd - Variable in class elki.clustering.hierarchical.birch.BIRCHKMeansPlusPlus.Par: Random generator
rnd - Variable in class elki.clustering.kmeans.initialization.AbstractKMeansInitialization.Par: Random generator
rnd - Variable in class elki.clustering.kmeans.initialization.AbstractKMeansInitialization: Random number generator
rnd - Variable in class elki.clustering.kmeans.initialization.betula.AbstractCFKMeansInitialization.Par: Random generator
rnd - Variable in class elki.clustering.kmeans.XMeans: Random factory.
rnd - Variable in class elki.clustering.kmedoids.initialization.LAB: Random generator
rnd - Variable in class elki.clustering.subspace.DOC: Randomizer used internally for sampling points.
rnd - Variable in class elki.clustering.subspace.PROCLUS.Par: Random generator
rnd - Variable in class elki.clustering.subspace.PROCLUS: Random generator
rnd - Variable in class elki.index.preprocessed.fastoptics.RandomProjectedNeighborsAndDensities.Par: Random factory.
rnd - Variable in class elki.index.preprocessed.fastoptics.RandomProjectedNeighborsAndDensities: Random factory.
root - Variable in class elki.clustering.hierarchical.birch.CFTree: Current root node.
root - Variable in class elki.clustering.kmeans.KDTreePruningKMeans.Instance: The root node of the tree
root - Variable in class elki.index.tree.betula.CFTree: Current root node.
rowcard - Variable in class elki.clustering.biclustering.ChengAndChurch.BiclusterCandidate: Cardinalities.
rowIDs - Variable in class elki.clustering.biclustering.AbstractBiclustering: The row ids corresponding to the currently set AbstractBiclustering.relation.
rowM - Variable in class elki.clustering.biclustering.ChengAndChurch.BiclusterCandidate: Means.
rows - Variable in class elki.clustering.biclustering.ChengAndChurch.BiclusterCandidate: Row and column bitmasks.
rowsBitsetToIDs(long[]) - Method in class elki.clustering.biclustering.AbstractBiclustering: Convert a bitset into integer row ids.
rowsBitsetToIDs(BitSet) - Method in class elki.clustering.biclustering.AbstractBiclustering: Convert a bitset into integer row ids.
rq - Variable in class elki.clustering.dbscan.predicates.EpsilonNeighborPredicate.Instance: Range query to use on the database.
rq - Variable in class elki.clustering.dbscan.predicates.SimilarityNeighborPredicate.Instance: Range query to use on the database.
run() - Method in class elki.clustering.correlation.HiCO.Instance
run() - Method in class elki.clustering.dbscan.GeneralizedDBSCAN.Instance: Run the actual GDBSCAN algorithm.
run() - Method in class elki.clustering.dbscan.parallel.ParallelGeneralizedDBSCAN.Instance: Run the parallel GDBSCAN algorithm.
run() - Method in class elki.clustering.hierarchical.extraction.ClustersWithNoiseExtraction.Instance: Extract all clusters from the pi-lambda-representation.
run() - Method in class elki.clustering.hierarchical.extraction.HDBSCANHierarchyExtraction.Instance: Extract all clusters from the pi-lambda-representation.
run() - Method in class elki.clustering.hierarchical.extraction.SimplifiedHierarchyExtraction.Instance: Extract all clusters from the pi-lambda-representation.
run() - Method in class elki.clustering.optics.GeneralizedOPTICS.Instance: Process the data set.
run() - Method in class elki.clustering.optics.OPTICSHeap.Instance: Process the data set.
run() - Method in class elki.clustering.optics.OPTICSList.Instance: Process the data set.
run() - Method in class elki.clustering.subspace.HiSC.Instance
run(double[][], int) - Method in class elki.clustering.hierarchical.birch.BIRCHKMeansPlusPlus: Perform k-means++ initialization.
run(int) - Method in class elki.clustering.kmeans.AbstractKMeans.Instance: Run the clustering.
run(int) - Method in class elki.clustering.kmeans.initialization.KMC2.Instance: Run k-means++ initialization for number vectors.
run(int) - Method in class elki.clustering.kmeans.initialization.KMeansPlusPlus.MedoidsInstance
run(int) - Method in class elki.clustering.kmeans.initialization.KMeansPlusPlus.NumberVectorInstance: Run k-means++ initialization for number vectors.
run(int) - Method in class elki.clustering.kmeans.initialization.SphericalKMeansPlusPlus.Instance: Run k-means++ initialization for number vectors.
run(int) - Method in class elki.clustering.kmeans.KDTreePruningKMeans.Instance
run(int) - Method in class elki.clustering.kmeans.YinYangKMeans.Instance
run(ClusterDistanceMatrix, ClusterMergeHistoryBuilder) - Method in class elki.clustering.hierarchical.AGNES.Instance: Run the main algorithm.
run(ClusterDistanceMatrix, ClusterMergeHistoryBuilder) - Method in class elki.clustering.hierarchical.Anderberg.Instance
run(ClusterDistanceMatrix, ClusterMergeHistoryBuilder) - Method in class elki.clustering.hierarchical.HACAM.Instance
run(ClusterDistanceMatrix, ClusterMergeHistoryBuilder) - Method in class elki.clustering.hierarchical.MedoidLinkage.Instance
run(ClusterDistanceMatrix, ClusterMergeHistoryBuilder) - Method in class elki.clustering.hierarchical.MiniMax.Instance
run(ClusterDistanceMatrix, ClusterMergeHistoryBuilder) - Method in class elki.clustering.hierarchical.NNChain.Instance
run(ClusterMergeHistory) - Method in class elki.clustering.hierarchical.extraction.AbstractCutDendrogram: Process a pointer hierarchy result.
run(ClusterMergeHistory) - Method in class elki.clustering.hierarchical.extraction.ClustersWithNoiseExtraction: Process an existing result.
run(ClusterMergeHistory) - Method in class elki.clustering.hierarchical.extraction.CutDendrogramByHeight
run(ClusterMergeHistory) - Method in class elki.clustering.hierarchical.extraction.CutDendrogramByNumberOfClusters
run(ClusterMergeHistory) - Method in class elki.clustering.hierarchical.extraction.HDBSCANHierarchyExtraction: Process an existing result.
run(ClusterMergeHistory) - Method in class elki.clustering.hierarchical.extraction.SimplifiedHierarchyExtraction: Process an existing result.
run(ClusterOrder) - Method in class elki.clustering.optics.OPTICSXi: Process the cluster order of an OPTICS clustering.
run(Database) - Method in class elki.clustering.hierarchical.extraction.AbstractCutDendrogram: Run the algorithms on a database.
run(Database, Relation<? extends NumberVector>) - Method in class elki.clustering.correlation.COPAC: Run the COPAC algorithm.
run(Database, Relation<? extends NumberVector>) - Method in class elki.clustering.correlation.ERiC: Performs the ERiC algorithm on the given database.
run(ArrayDBIDs, ClusterDistanceMatrix, ClusterMergeHistoryBuilder, DistanceQuery<?>) - Method in class elki.clustering.hierarchical.MedoidLinkage.Instance: Run medoid linkage
run(ArrayDBIDs, ClusterDistanceMatrix, ClusterMergeHistoryBuilder, DistanceQuery<?>, DBIDArrayMIter) - Method in class elki.clustering.hierarchical.HACAM.Instance: Run HACAM linkage
run(ArrayDBIDs, ClusterDistanceMatrix, ClusterMergeHistoryBuilder, DistanceQuery<?>, DBIDArrayMIter) - Method in class elki.clustering.hierarchical.MiniMax.Instance
run(ArrayDBIDs, ClusterDistanceMatrix, ClusterMergeHistoryBuilder, DistanceQuery<?>, DBIDArrayMIter) - Method in class elki.clustering.hierarchical.MiniMaxAnderberg.Instance
run(ArrayDBIDs, ClusterDistanceMatrix, ClusterMergeHistoryBuilder, DistanceQuery<?>, DBIDArrayMIter) - Method in class elki.clustering.hierarchical.MiniMaxNNChain.Instance
run(ArrayDBIDs, Relation<O>, ClusterMergeHistoryBuilder) - Method in class elki.clustering.hierarchical.LinearMemoryNNChain.Instance
run(ArrayModifiableDBIDs) - Method in class elki.clustering.kmedoids.SingleAssignmentKMedoids.Instance: Run the PAM optimization phase.
run(ArrayModifiableDBIDs, int) - Method in class elki.clustering.kmedoids.EagerPAM.Instance: Run the EagerPAM optimization phase.
run(ArrayModifiableDBIDs, int) - Method in class elki.clustering.kmedoids.FasterPAM.Instance: Run the PAM optimization phase.
run(ArrayModifiableDBIDs, int) - Method in class elki.clustering.kmedoids.FastPAM.Instance: Run the PAM optimization phase.
run(ArrayModifiableDBIDs, int) - Method in class elki.clustering.kmedoids.FastPAM1.Instance: Run the FastPAM optimization phase.
run(ArrayModifiableDBIDs, int) - Method in class elki.clustering.kmedoids.PAM.Instance: Run the PAM optimization phase.
run(ArrayModifiableDBIDs, int) - Method in class elki.clustering.kmedoids.ReynoldsPAM.Instance: Run the PAM optimization phase.
run(ArrayModifiableDBIDs, int) - Method in class elki.clustering.silhouette.FasterMSC.Instance: Run the FasterMSC optimization phase.
run(ArrayModifiableDBIDs, int) - Method in class elki.clustering.silhouette.FasterMSC.Instance2: Run the FasterMSC optimization phase.
run(ArrayModifiableDBIDs, int) - Method in class elki.clustering.silhouette.FastMSC.Instance: Run the FastMSC optimization phase.
run(ArrayModifiableDBIDs, int) - Method in class elki.clustering.silhouette.FastMSC.Instance2: Run the FastMSC optimization phase.
run(ArrayModifiableDBIDs, int) - Method in class elki.clustering.silhouette.PAMMEDSIL.Instance: Run the PAMMEDSIL optimization phase.
run(ArrayModifiableDBIDs, int) - Method in class elki.clustering.silhouette.PAMSIL.Instance: Run the PAMSIL optimization phase.
run(Relation<?>) - Method in class elki.clustering.trivial.ByLabelClustering: Run the actual clustering algorithm.
run(Relation<?>) - Method in class elki.clustering.trivial.ByLabelHierarchicalClustering: Run the actual clustering algorithm.
run(Relation<?>) - Method in class elki.clustering.trivial.TrivialAllInOne: Perform trivial clustering.
run(Relation<?>) - Method in class elki.clustering.trivial.TrivialAllNoise: Run the trivial clustering algorithm.
run(Relation<? extends NumberVector>) - Method in class elki.clustering.biclustering.AbstractBiclustering: Prepares the algorithm for running on a specific database.
run(Relation<? extends NumberVector>) - Method in class elki.clustering.correlation.HiCO: Run the HiCO algorithm.
run(Relation<? extends NumberVector>) - Method in class elki.clustering.correlation.LMCLUS: The main LMCLUS (Linear manifold clustering algorithm) is processed in this method.
run(Relation<? extends NumberVector>) - Method in class elki.clustering.correlation.ORCLUS: Performs the ORCLUS algorithm on the given database.
run(Relation<? extends NumberVector>) - Method in class elki.clustering.em.KDTreeEM: Calculates the EM Clustering with the given values by calling makeStats and calculation the new models from the given results
run(Relation<? extends NumberVector>) - Method in class elki.clustering.onedimensional.KNNKernelDensityMinimaClustering: Run the clustering algorithm on a data relation.
run(Relation<? extends NumberVector>) - Method in class elki.clustering.subspace.CLIQUE: Performs the CLIQUE algorithm on the given database.
run(Relation<? extends NumberVector>) - Method in class elki.clustering.subspace.DOC: Performs the DOC or FastDOC (as configured) algorithm.
run(Relation<? extends NumberVector>) - Method in class elki.clustering.subspace.HiSC: Run the HiSC algorithm
run(Relation<? extends NumberVector>) - Method in class elki.clustering.subspace.P3C: Performs the P3C algorithm on the given Database.
run(Relation<? extends NumberVector>, int) - Method in class elki.clustering.kmeans.initialization.Ostrovsky.NumberVectorInstance
run(Relation<NumberVector>) - Method in class elki.clustering.BetulaLeafPreClustering: Run the clustering algorithm.
run(Relation<NumberVector>) - Method in class elki.clustering.em.BetulaGMM: Run the clustering algorithm.
run(Relation<NumberVector>) - Method in class elki.clustering.hierarchical.birch.BIRCHLeafClustering: Run the clustering algorithm.
run(Relation<NumberVector>) - Method in class elki.clustering.hierarchical.birch.BIRCHLloydKMeans: Run the clustering algorithm.
run(Relation<NumberVector>) - Method in class elki.clustering.kmeans.BetulaLloydKMeans: Run the clustering algorithm.
run(Relation<O>) - Method in class elki.clustering.affinitypropagation.AffinityPropagation: Perform affinity propagation clustering.
run(Relation<O>) - Method in class elki.clustering.CanopyPreClustering: Run the canopy clustering algorithm
run(Relation<O>) - Method in class elki.clustering.CFSFDP: Perform CFSFDP clustering.
run(Relation<O>) - Method in class elki.clustering.dbscan.DBSCAN: Performs the DBSCAN algorithm on the given database.
run(Relation<O>) - Method in class elki.clustering.dbscan.LSDBC: Run the LSDBC algorithm
run(Relation<O>) - Method in class elki.clustering.em.EM: Performs the EM clustering algorithm on the given database.
run(Relation<O>) - Method in class elki.clustering.hierarchical.AGNES: Run the algorithm
run(Relation<O>) - Method in class elki.clustering.hierarchical.Anderberg
run(Relation<O>) - Method in class elki.clustering.hierarchical.HACAM: Run the algorithm
run(Relation<O>) - Method in class elki.clustering.hierarchical.HDBSCANLinearMemory: Run the algorithm
run(Relation<O>) - Method in class elki.clustering.hierarchical.LinearMemoryNNChain: Run the NNchain algorithm.
run(Relation<O>) - Method in class elki.clustering.hierarchical.MedoidLinkage: Run the algorithm
run(Relation<O>) - Method in class elki.clustering.hierarchical.MiniMax: Run the algorithm on a database.
run(Relation<O>) - Method in class elki.clustering.hierarchical.MiniMaxAnderberg: Run the algorithm
run(Relation<O>) - Method in class elki.clustering.hierarchical.MiniMaxNNChain: Run the algorithm
run(Relation<O>) - Method in class elki.clustering.hierarchical.NNChain
run(Relation<O>) - Method in class elki.clustering.hierarchical.SLINK: Performs the SLINK algorithm on the given database.
run(Relation<O>) - Method in class elki.clustering.hierarchical.SLINKHDBSCANLinearMemory: Run the algorithm
run(Relation<O>) - Method in class elki.clustering.kcenter.GreedyKCenter: Perform greedy k-center clustering on the relation.
run(Relation<O>) - Method in class elki.clustering.kmedoids.AlternatingKMedoids
run(Relation<O>) - Method in class elki.clustering.kmedoids.CLARANS: Run CLARANS clustering.
run(Relation<O>) - Method in class elki.clustering.kmedoids.FasterCLARA
run(Relation<O>) - Method in interface elki.clustering.kmedoids.KMedoidsClustering: Run k-medoids clustering.
run(Relation<O>) - Method in class elki.clustering.kmedoids.PAM
run(Relation<O>) - Method in class elki.clustering.Leader: Run the leader clustering algorithm.
run(Relation<O>) - Method in class elki.clustering.optics.AbstractOPTICS: Run OPTICS on the database.
run(Relation<O>) - Method in class elki.clustering.optics.OPTICSHeap
run(Relation<O>) - Method in class elki.clustering.optics.OPTICSList
run(Relation<O>) - Method in class elki.clustering.SNNClustering: Perform SNN clustering
run(Relation<O>, int, DistanceQuery<? super O>) - Method in class elki.clustering.kmedoids.AlternatingKMedoids
run(Relation<O>, int, DistanceQuery<? super O>) - Method in class elki.clustering.kmedoids.CLARANS
run(Relation<O>, int, DistanceQuery<? super O>) - Method in class elki.clustering.kmedoids.EagerPAM
run(Relation<O>, int, DistanceQuery<? super O>) - Method in class elki.clustering.kmedoids.FasterCLARA
run(Relation<O>, int, DistanceQuery<? super O>) - Method in class elki.clustering.kmedoids.FasterPAM
run(Relation<O>, int, DistanceQuery<? super O>) - Method in class elki.clustering.kmedoids.FastPAM
run(Relation<O>, int, DistanceQuery<? super O>) - Method in class elki.clustering.kmedoids.FastPAM1
run(Relation<O>, int, DistanceQuery<? super O>) - Method in interface elki.clustering.kmedoids.KMedoidsClustering: Run k-medoids clustering with a given distance query.
Not a very elegant API, but needed for some types of nested k-medoids.
run(Relation<O>, int, DistanceQuery<? super O>) - Method in class elki.clustering.kmedoids.PAM
run(Relation<O>, int, DistanceQuery<? super O>) - Method in class elki.clustering.kmedoids.ReynoldsPAM
run(Relation<O>, int, DistanceQuery<? super O>) - Method in class elki.clustering.kmedoids.SingleAssignmentKMedoids
run(Relation<O>, int, DistanceQuery<? super O>) - Method in class elki.clustering.silhouette.FasterMSC
run(Relation<O>, int, DistanceQuery<? super O>) - Method in class elki.clustering.silhouette.FastMSC
run(Relation<O>, int, DistanceQuery<? super O>) - Method in class elki.clustering.silhouette.PAMMEDSIL
run(Relation<O>, int, DistanceQuery<? super O>) - Method in class elki.clustering.silhouette.PAMSIL
run(Relation<O>, RangeSearcher<DBIDRef>) - Method in class elki.clustering.dbscan.DBSCAN.Instance: Run the DBSCAN algorithm
run(Relation<V>) - Method in class elki.clustering.dbscan.GriDBSCAN.Instance: Performs the DBSCAN algorithm on the given database.
run(Relation<V>) - Method in class elki.clustering.dbscan.GriDBSCAN: Performs the DBSCAN algorithm on the given database.
run(Relation<V>) - Method in class elki.clustering.kmeans.AnnulusKMeans
run(Relation<V>) - Method in class elki.clustering.kmeans.BestOfMultipleKMeans
run(Relation<V>) - Method in class elki.clustering.kmeans.BisectingKMeans
run(Relation<V>) - Method in class elki.clustering.kmeans.CompareMeans
run(Relation<V>) - Method in class elki.clustering.kmeans.ElkanKMeans
run(Relation<V>) - Method in class elki.clustering.kmeans.ExponionKMeans
run(Relation<V>) - Method in class elki.clustering.kmeans.FuzzyCMeans: Runs Fuzzy C Means clustering on the given Relation
run(Relation<V>) - Method in class elki.clustering.kmeans.HamerlyKMeans
run(Relation<V>) - Method in class elki.clustering.kmeans.HartiganWongKMeans
run(Relation<V>) - Method in class elki.clustering.kmeans.KDTreeFilteringKMeans
run(Relation<V>) - Method in class elki.clustering.kmeans.KDTreePruningKMeans
run(Relation<V>) - Method in interface elki.clustering.kmeans.KMeans: Run the clustering algorithm.
run(Relation<V>) - Method in class elki.clustering.kmeans.KMeansMinusMinus
run(Relation<V>) - Method in class elki.clustering.kmeans.KMediansLloyd
run(Relation<V>) - Method in class elki.clustering.kmeans.LloydKMeans
run(Relation<V>) - Method in class elki.clustering.kmeans.MacQueenKMeans
run(Relation<V>) - Method in class elki.clustering.kmeans.parallel.ParallelLloydKMeans
run(Relation<V>) - Method in class elki.clustering.kmeans.ShallotKMeans
run(Relation<V>) - Method in class elki.clustering.kmeans.SimplifiedElkanKMeans
run(Relation<V>) - Method in class elki.clustering.kmeans.SingleAssignmentKMeans
run(Relation<V>) - Method in class elki.clustering.kmeans.SortMeans
run(Relation<V>) - Method in class elki.clustering.kmeans.spherical.EuclideanSphericalElkanKMeans
run(Relation<V>) - Method in class elki.clustering.kmeans.spherical.EuclideanSphericalHamerlyKMeans
run(Relation<V>) - Method in class elki.clustering.kmeans.spherical.EuclideanSphericalSimplifiedElkanKMeans
run(Relation<V>) - Method in class elki.clustering.kmeans.spherical.SphericalElkanKMeans
run(Relation<V>) - Method in class elki.clustering.kmeans.spherical.SphericalHamerlyKMeans
run(Relation<V>) - Method in class elki.clustering.kmeans.spherical.SphericalKMeans
run(Relation<V>) - Method in class elki.clustering.kmeans.spherical.SphericalSimplifiedElkanKMeans
run(Relation<V>) - Method in class elki.clustering.kmeans.spherical.SphericalSimplifiedHamerlyKMeans
run(Relation<V>) - Method in class elki.clustering.kmeans.spherical.SphericalSingleAssignmentKMeans
run(Relation<V>) - Method in class elki.clustering.kmeans.XMeans: Run the algorithm on a database and relation.
run(Relation<V>) - Method in class elki.clustering.kmeans.YinYangKMeans
run(Relation<V>) - Method in class elki.clustering.kmedoids.CLARA
run(Relation<V>) - Method in class elki.clustering.kmedoids.FastCLARA
run(Relation<V>) - Method in class elki.clustering.kmedoids.FastCLARANS
run(Relation<V>) - Method in class elki.clustering.NaiveMeanShiftClustering: Run the mean-shift clustering algorithm.
run(Relation<V>) - Method in class elki.clustering.optics.FastOPTICS: Run the algorithm.
run(Relation<V>) - Method in class elki.clustering.subspace.PROCLUS: Performs the PROCLUS algorithm on the given database.
run(Relation<V>) - Method in class elki.clustering.subspace.SUBCLU: Performs the SUBCLU algorithm on the given database.
run(Relation<V>, int, DistanceQuery<? super V>) - Method in class elki.clustering.kmedoids.CLARA
run(Relation<V>, int, DistanceQuery<? super V>) - Method in class elki.clustering.kmedoids.FastCLARA
run(CFTree<?>, List<? extends ClusterFeature>, int) - Method in class elki.clustering.kmeans.initialization.betula.CFKPlusPlusLeaves: Perform k-means++ initialization.
runDBSCAN(Relation<V>, DBIDs, Subspace) - Method in class elki.clustering.subspace.SUBCLU: Runs the DBSCAN algorithm on the specified partition of the database in the given subspace.
runDBSCANOnCell(DBIDs, Relation<V>, ModifiableDoubleDBIDList, ArrayModifiableDBIDs, int) - Method in class elki.clustering.dbscan.GriDBSCAN.Instance
runDOC(Relation<? extends NumberVector>, ArrayModifiableDBIDs, int, int, int, int, int) - Method in class elki.clustering.subspace.DOC: Performs a single run of DOC, finding a single cluster.
runDOC(Relation<? extends NumberVector>, ArrayModifiableDBIDs, int, int, int, int, int) - Method in class elki.clustering.subspace.FastDOC: Performs a single run of FastDOC, finding a single cluster.

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