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

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All Classes All Packages

C

cache - Variable in class elki.clustering.kmedoids.CLARA.CachedDistanceQuery: Cache
CachedDistanceQuery(DistanceQuery<V>, int) - Constructor for class elki.clustering.kmedoids.CLARA.CachedDistanceQuery: Constructor.
cacheR1(DBIDIter, NumberVector, int) - Method in class elki.clustering.kmeans.HartiganWongKMeans.Instance: Compute and cache the R1 value.
calculateModelLimits(double[], double[], ConstrainedQuadraticProblemSolver, double, double[], double[], double[]) - Method in class elki.clustering.em.models.TextbookMultivariateGaussianModel: Compute the weight of a Gaussian with respect to a bounding box.
calculateModelLimits(KDTreeEM.KDTree, TextbookMultivariateGaussianModel, double[], double[], double[]) - Method in class elki.clustering.em.KDTreeEM: Calculates the model limits inside this node by translating the Gaussian model into a squared function.
calculateVariances(int[], double[][], ClusteringFeature[], int[]) - Method in class elki.clustering.hierarchical.birch.BIRCHLloydKMeans: Calculate variance of clusters based on clustering features.
calculateVariances(int[], double[][], ArrayList<? extends ClusterFeature>, int[]) - Method in class elki.clustering.kmeans.BetulaLloydKMeans: Calculate variance of clusters based on clustering features.
candidates - Variable in class elki.clustering.optics.GeneralizedOPTICS.Instance: Current list of candidates.
candidates - Variable in class elki.clustering.optics.OPTICSList.Instance: Current list of candidates.
CanopyPreClustering<O> - Class in elki.clustering: Canopy pre-clustering is a simple preprocessing step for clustering.
CanopyPreClustering(Distance<? super O>, double, double) - Constructor for class elki.clustering.CanopyPreClustering: Constructor.
capacity - Variable in class elki.clustering.hierarchical.birch.CFTree: Capacity of a node.
capacity - Variable in class elki.index.tree.betula.CFTree: Capacity of a node.
capacity() - Method in class elki.index.tree.betula.CFNode: Get the node capacity.
ccsim - Variable in class elki.clustering.kmeans.spherical.SphericalElkanKMeans.Instance: Cluster center similarities
cdim - Variable in class elki.clustering.dbscan.predicates.COPACNeighborPredicate.COPACModel: Correlation dimensionality.
cdist - Variable in class elki.clustering.kmeans.AnnulusKMeans.Instance: Cluster center distances.
cdist - Variable in class elki.clustering.kmeans.CompareMeans.Instance: Cluster center distances.
cdist - Variable in class elki.clustering.kmeans.ElkanKMeans.Instance: Cluster center distances
cdist - Variable in class elki.clustering.kmeans.ExponionKMeans.Instance: Cluster center distances.
cdist - Variable in class elki.clustering.kmeans.spherical.EuclideanSphericalElkanKMeans.Instance: Cluster center distances
cdrift - Variable in class elki.clustering.kmeans.YinYangKMeans.Instance: Distance moved by each center.
cells - Variable in class elki.clustering.dbscan.GriDBSCAN.Instance: Number of cells per dimension.
centroid - Variable in class elki.clustering.correlation.ORCLUS.ORCLUSCluster: The centroid of this cluster.
centroid - Variable in class elki.clustering.subspace.PROCLUS.PROCLUSCluster: The centroids of this cluster along each dimension.
centroid(int) - Method in class elki.clustering.hierarchical.birch.ClusteringFeature: Centroid value in dimension i.
centroid(int) - Method in class elki.index.tree.betula.features.BIRCHCF
centroid(int) - Method in interface elki.index.tree.betula.features.ClusterFeature: Returns the mean of the specified dimension.
centroid(int) - Method in class elki.index.tree.betula.features.VIIFeature
centroid(int) - Method in class elki.index.tree.betula.features.VVIFeature
centroid(int) - Method in class elki.index.tree.betula.features.VVVFeature
CentroidEuclideanDistance - Class in elki.clustering.hierarchical.birch: Centroid Euclidean distance.
CentroidEuclideanDistance - Class in elki.index.tree.betula.distance: Centroid Euclidean distance.
CentroidEuclideanDistance() - Constructor for class elki.clustering.hierarchical.birch.CentroidEuclideanDistance
CentroidEuclideanDistance() - Constructor for class elki.index.tree.betula.distance.CentroidEuclideanDistance
CentroidEuclideanDistance.Par - Class in elki.clustering.hierarchical.birch: Parameterization class.
CentroidEuclideanDistance.Par - Class in elki.index.tree.betula.distance: Parameterization class.
CentroidLinkage - Class in elki.clustering.hierarchical.linkage: Centroid linkage — Unweighted Pair-Group Method using Centroids (UPGMC).
CentroidLinkage() - Constructor for class elki.clustering.hierarchical.linkage.CentroidLinkage: Deprecated.
use the static instance CentroidLinkage.STATIC instead.
CentroidLinkage.Par - Class in elki.clustering.hierarchical.linkage: Class parameterizer.
CentroidManhattanDistance - Class in elki.clustering.hierarchical.birch: Centroid Manhattan Distance
CentroidManhattanDistance - Class in elki.index.tree.betula.distance: Centroid Manhattan Distance
CentroidManhattanDistance() - Constructor for class elki.clustering.hierarchical.birch.CentroidManhattanDistance
CentroidManhattanDistance() - Constructor for class elki.index.tree.betula.distance.CentroidManhattanDistance
CentroidManhattanDistance.Par - Class in elki.clustering.hierarchical.birch: Parameterization class.
CentroidManhattanDistance.Par - Class in elki.index.tree.betula.distance: Parameterization class.
centroids - Variable in class elki.clustering.kmeans.parallel.KMeansProcessor: Updated cluster centroids
centroids - Variable in class elki.clustering.kmeans.parallel.KMeansProcessor.Instance: Updated cluster centroids
centroids(Relation<? extends NumberVector>, List<? extends Cluster<?>>, NumberVector[], NoiseHandling) - Static method in class elki.evaluation.clustering.internal.SimplifiedSilhouette: Compute centroids.
cf - Variable in class elki.index.tree.betula.CFNode: Cluster feature
CFDistance - Interface in elki.index.tree.betula.distance: Distance function for BIRCH clustering.
CFDistanceMatrix - Class in elki.index.tree.betula: Cluster feature distance matrix, used for clustering.
CFDistanceMatrix(ClusterFeature[]) - Constructor for class elki.index.tree.betula.CFDistanceMatrix: Constructor.
cffactory - Variable in class elki.clustering.BetulaLeafPreClustering: CFTree factory.
cffactory - Variable in class elki.clustering.BetulaLeafPreClustering.Par: CFTree factory.
cffactory - Variable in class elki.clustering.em.BetulaGMM: CFTree factory.
cffactory - Variable in class elki.clustering.em.BetulaGMM.Par: CFTree factory.
cffactory - Variable in class elki.clustering.hierarchical.birch.BIRCHLeafClustering: CFTree factory.
cffactory - Variable in class elki.clustering.hierarchical.birch.BIRCHLeafClustering.Par: CFTree factory.
cffactory - Variable in class elki.clustering.hierarchical.birch.BIRCHLloydKMeans: CFTree factory.
cffactory - Variable in class elki.clustering.hierarchical.birch.BIRCHLloydKMeans.Par: CFTree factory.
cffactory - Variable in class elki.clustering.kmeans.BetulaLloydKMeans: CFTree factory.
cffactory - Variable in class elki.clustering.kmeans.BetulaLloydKMeans.Par: CFTree factory.
CFInitWeight - Interface in elki.clustering.kmeans.initialization.betula: Initialization weight function for k-means initialization with BETULA.
CFKPlusPlusLeaves - Class in elki.clustering.kmeans.initialization.betula: K-Means++-like initialization for BETULA k-means, treating the leaf clustering features as a flat list, and called "leaves" in the publication.
CFKPlusPlusLeaves(CFInitWeight, boolean, RandomFactory) - Constructor for class elki.clustering.kmeans.initialization.betula.CFKPlusPlusLeaves: Constructor.
CFKPlusPlusLeaves.Par - Class in elki.clustering.kmeans.initialization.betula: Parameterization class.
CFKPlusPlusTree - Class in elki.clustering.kmeans.initialization.betula: Initialize K-means by following tree paths weighted by their variance contribution.
CFKPlusPlusTree(CFInitWeight, boolean, int, RandomFactory) - Constructor for class elki.clustering.kmeans.initialization.betula.CFKPlusPlusTree: Constructor.
CFKPlusPlusTree.Par - Class in elki.clustering.kmeans.initialization.betula: Parameterization class.
CFKPlusPlusTrunk - Class in elki.clustering.kmeans.initialization.betula: Trunk strategy for initializing k-means with BETULA: only the nodes up to a particular level are considered for k-means++ style initialization.
CFKPlusPlusTrunk(CFInitWeight, boolean, RandomFactory) - Constructor for class elki.clustering.kmeans.initialization.betula.CFKPlusPlusTrunk: Constructor.
CFKPlusPlusTrunk.Par - Class in elki.clustering.kmeans.initialization.betula: Parameterization class.
CFNode<L extends ClusterFeature> - Class in elki.index.tree.betula: Interface for TreeNode
CFNode(L, int) - Constructor for class elki.index.tree.betula.CFNode: Constructor
CFRandomlyChosen - Class in elki.clustering.kmeans.initialization.betula: Initialize K-means by randomly choosing k existing elements as initial cluster centers for Clustering Features.
CFRandomlyChosen(RandomFactory) - Constructor for class elki.clustering.kmeans.initialization.betula.CFRandomlyChosen: Constructor.
CFRandomlyChosen.Par - Class in elki.clustering.kmeans.initialization.betula: Parameterization class.
cfs - Variable in class elki.index.tree.betula.CFDistanceMatrix: Cluster features
CFSFDP<O> - Class in elki.clustering: Clustering by fast search and find of density peaks (CFSFDP) is a density-based clustering method similar to mean-shift clustering.
CFSFDP(Distance<? super O>, double, int) - Constructor for class elki.clustering.CFSFDP: Constructor.
CFSFDP.Par<O> - Class in elki.clustering: Parameterizer
CFTree - Class in elki.clustering.hierarchical.birch: Partial implementation of the CFTree as used by BIRCH.
CFTree<L extends ClusterFeature> - Class in elki.index.tree.betula: Partial implementation of the CFTree as used by BIRCH and BETULA.
CFTree(BIRCHDistance, BIRCHAbsorptionCriterion, double, int) - Constructor for class elki.clustering.hierarchical.birch.CFTree: Constructor.
CFTree(ClusterFeature.Factory<L>, CFDistance, CFDistance, double, int, CFTree.Threshold, int, boolean) - Constructor for class elki.index.tree.betula.CFTree: Constructor.
CFTree.Factory - Class in elki.clustering.hierarchical.birch: CF-Tree Factory.
CFTree.Factory<L extends ClusterFeature> - Class in elki.index.tree.betula: CF-Tree Factory.
CFTree.Factory.Par - Class in elki.clustering.hierarchical.birch: Parameterization class for CFTrees.
CFTree.Factory.Par<L extends ClusterFeature> - Class in elki.index.tree.betula: Parameterization class for CFTrees.
CFTree.LeafIterator - Class in elki.clustering.hierarchical.birch: Iterator over leaf nodes.
CFTree.LeafIterator<L extends ClusterFeature> - Class in elki.index.tree.betula: Iterator over leaf nodes.
CFTree.Threshold - Enum in elki.index.tree.betula: Threshold update strategy.
CFWeightedRandomlyChosen - Class in elki.clustering.kmeans.initialization.betula: Initialize K-means by randomly choosing k existing elements as initial cluster centers for Clustering Features.
CFWeightedRandomlyChosen(RandomFactory) - Constructor for class elki.clustering.kmeans.initialization.betula.CFWeightedRandomlyChosen: Constructor.
CFWeightedRandomlyChosen.Par - Class in elki.clustering.kmeans.initialization.betula: Parameterization class.
changed - Variable in class elki.clustering.kmeans.parallel.KMeansProcessor: Whether the assignment changed during the last iteration.
changed - Variable in class elki.clustering.kmeans.parallel.KMeansProcessor.Instance: Changed flag.
changed() - Method in class elki.clustering.kmeans.parallel.KMeansProcessor: Get the "has changed" value.
checkDimensions(CLIQUEUnit, int) - Method in class elki.clustering.subspace.clique.CLIQUEUnit: Check that the first e dimensions agree.
checkGridCellSizes(int, long) - Method in class elki.clustering.dbscan.GriDBSCAN.Instance: Perform some sanity checks on the grid cells.
checkLower(Subspace, List<Subspace>) - Method in class elki.clustering.subspace.SUBCLU: Perform Apriori-style pruning.
checkMonotone() - Method in class elki.clustering.hierarchical.ClusterMergeHistoryBuilder: Check if merge distances are monotone.
checkStoppingCondition(KDTreeEM.KDTree, int[]) - Method in class elki.clustering.em.KDTreeEM: This methods checks the different stopping conditions given in the paper, thus calculating the Dimensions, that will be considered for child-trees.
ChengAndChurch - Class in elki.clustering.biclustering: Cheng and Church biclustering.
ChengAndChurch(double, double, int, Distribution, RandomFactory) - Constructor for class elki.clustering.biclustering.ChengAndChurch: Constructor.
ChengAndChurch.BiclusterCandidate - Class in elki.clustering.biclustering: Bicluster candidate.
ChengAndChurch.CellVisitor - Interface in elki.clustering.biclustering: Visitor pattern for processing cells.
children - Variable in class elki.clustering.hierarchical.extraction.HDBSCANHierarchyExtraction.TempCluster: (Finished) child clusters
children - Variable in class elki.clustering.hierarchical.extraction.SimplifiedHierarchyExtraction.TempCluster: (Finished) child clusters
children - Variable in class elki.index.tree.betula.CFNode: Children of the TreeNode
childrenTotal - Variable in class elki.clustering.hierarchical.extraction.HDBSCANHierarchyExtraction.TempCluster: Number of objects in children.
chiSquaredUniformTest(SetDBIDs[], long[], int) - Method in class elki.clustering.subspace.P3C: Performs a ChiSquared test to determine whether an attribute has a uniform distribution.
chol - Variable in class elki.clustering.em.models.MultivariateGaussianModel: Decomposition of covariance matrix.
chol - Variable in class elki.clustering.em.models.TextbookMultivariateGaussianModel: Decomposition of covariance matrix.
chol - Variable in class elki.clustering.em.models.TwoPassMultivariateGaussianModel: Decomposition of covariance matrix.
chooseInitialMeans(Relation<? extends NumberVector>, int, NumberVectorDistance<?>) - Method in class elki.clustering.kmeans.initialization.AFKMC2
chooseInitialMeans(Relation<? extends NumberVector>, int, NumberVectorDistance<?>) - Method in class elki.clustering.kmeans.initialization.FarthestPoints
chooseInitialMeans(Relation<? extends NumberVector>, int, NumberVectorDistance<?>) - Method in class elki.clustering.kmeans.initialization.FarthestSumPoints
chooseInitialMeans(Relation<? extends NumberVector>, int, NumberVectorDistance<?>) - Method in class elki.clustering.kmeans.initialization.FirstK
chooseInitialMeans(Relation<? extends NumberVector>, int, NumberVectorDistance<?>) - Method in class elki.clustering.kmeans.initialization.KMC2
chooseInitialMeans(Relation<? extends NumberVector>, int, NumberVectorDistance<?>) - Method in interface elki.clustering.kmeans.initialization.KMeansInitialization: Choose initial means
chooseInitialMeans(Relation<? extends NumberVector>, int, NumberVectorDistance<?>) - Method in class elki.clustering.kmeans.initialization.KMeansPlusPlus
chooseInitialMeans(Relation<? extends NumberVector>, int, NumberVectorDistance<?>) - Method in class elki.clustering.kmeans.initialization.Ostrovsky
chooseInitialMeans(Relation<? extends NumberVector>, int, NumberVectorDistance<?>) - Method in class elki.clustering.kmeans.initialization.Predefined
chooseInitialMeans(Relation<? extends NumberVector>, int, NumberVectorDistance<?>) - Method in class elki.clustering.kmeans.initialization.RandomlyChosen
chooseInitialMeans(Relation<? extends NumberVector>, int, NumberVectorDistance<?>) - Method in class elki.clustering.kmeans.initialization.RandomNormalGenerated
chooseInitialMeans(Relation<? extends NumberVector>, int, NumberVectorDistance<?>) - Method in class elki.clustering.kmeans.initialization.RandomUniformGenerated
chooseInitialMeans(Relation<? extends NumberVector>, int, NumberVectorDistance<?>) - Method in class elki.clustering.kmeans.initialization.SampleKMeans
chooseInitialMeans(Relation<? extends NumberVector>, int, NumberVectorDistance<?>) - Method in class elki.clustering.kmeans.initialization.SphericalAFKMC2
chooseInitialMeans(Relation<? extends NumberVector>, int, NumberVectorDistance<?>) - Method in class elki.clustering.kmeans.initialization.SphericalKMeansPlusPlus
chooseInitialMeans(Relation<? extends NumberVector>, int, NumberVectorDistance<?>) - Method in class elki.clustering.kmedoids.initialization.BUILD
chooseInitialMeans(Relation<? extends NumberVector>, int, NumberVectorDistance<?>) - Method in class elki.clustering.kmedoids.initialization.LAB
chooseInitialMeans(Relation<? extends NumberVector>, int, NumberVectorDistance<?>) - Method in class elki.clustering.kmedoids.initialization.ParkJun
chooseInitialMeans(CFTree<?>, List<? extends ClusterFeature>, int) - Method in class elki.clustering.kmeans.initialization.betula.AbstractCFKMeansInitialization: Build the initial models.
chooseInitialMeans(CFTree<?>, List<? extends ClusterFeature>, int) - Method in class elki.clustering.kmeans.initialization.betula.CFKPlusPlusLeaves
chooseInitialMeans(CFTree<?>, List<? extends ClusterFeature>, int) - Method in class elki.clustering.kmeans.initialization.betula.CFKPlusPlusTree
chooseInitialMeans(CFTree<?>, List<? extends ClusterFeature>, int) - Method in class elki.clustering.kmeans.initialization.betula.CFKPlusPlusTrunk
chooseInitialMeans(CFTree<?>, List<? extends ClusterFeature>, int) - Method in class elki.clustering.kmeans.initialization.betula.CFRandomlyChosen
chooseInitialMeans(CFTree<?>, List<? extends ClusterFeature>, int) - Method in class elki.clustering.kmeans.initialization.betula.CFWeightedRandomlyChosen
chooseInitialMedoids(int, DBIDs, DistanceQuery<? super O>) - Method in class elki.clustering.kmeans.initialization.FarthestPoints
chooseInitialMedoids(int, DBIDs, DistanceQuery<? super O>) - Method in class elki.clustering.kmeans.initialization.FarthestSumPoints
chooseInitialMedoids(int, DBIDs, DistanceQuery<? super O>) - Method in class elki.clustering.kmeans.initialization.FirstK
chooseInitialMedoids(int, DBIDs, DistanceQuery<? super O>) - Method in class elki.clustering.kmeans.initialization.KMeansPlusPlus
chooseInitialMedoids(int, DBIDs, DistanceQuery<? super O>) - Method in class elki.clustering.kmeans.initialization.RandomlyChosen
chooseInitialMedoids(int, DBIDs, DistanceQuery<? super O>) - Method in class elki.clustering.kmedoids.initialization.AlternateRefinement
chooseInitialMedoids(int, DBIDs, DistanceQuery<? super O>) - Method in class elki.clustering.kmedoids.initialization.BUILD
chooseInitialMedoids(int, DBIDs, DistanceQuery<? super O>) - Method in class elki.clustering.kmedoids.initialization.GreedyG
chooseInitialMedoids(int, DBIDs, DistanceQuery<? super O>) - Method in interface elki.clustering.kmedoids.initialization.KMedoidsInitialization: Choose initial means
chooseInitialMedoids(int, DBIDs, DistanceQuery<? super O>) - Method in class elki.clustering.kmedoids.initialization.KMedoidsKMedoidsInitialization
chooseInitialMedoids(int, DBIDs, DistanceQuery<? super O>) - Method in class elki.clustering.kmedoids.initialization.LAB
chooseInitialMedoids(int, DBIDs, DistanceQuery<? super O>) - Method in class elki.clustering.kmedoids.initialization.ParkJun
chooseNextNode(CFNode<?>, List<? extends ClusterFeature>, Random) - Method in class elki.clustering.kmeans.initialization.betula.CFKPlusPlusTree: Choose a child of the current node.
chooseRemaining(int, ArrayModifiableDBIDs, double) - Method in class elki.clustering.kmeans.initialization.KMeansPlusPlus.MedoidsInstance: Choose remaining means, weighted by distance.
chooseRemaining(int, List<NumberVector>, double) - Method in class elki.clustering.kmeans.initialization.KMC2.Instance: Choose remaining means, weighted by distance.
chooseRemaining(int, List<NumberVector>, double) - Method in class elki.clustering.kmeans.initialization.KMeansPlusPlus.NumberVectorInstance: Choose remaining means, weighted by distance.
chooseRemaining(int, List<NumberVector>, double) - Method in class elki.clustering.kmeans.initialization.SphericalKMeansPlusPlus.Instance: Choose remaining means, weighted by distance.
CIndex<O> - Class in elki.evaluation.clustering.internal: Compute the C-index of a data set.
CIndex(Distance<? super O>, NoiseHandling) - Constructor for class elki.evaluation.clustering.internal.CIndex: Constructor.
CIndex.Par<O> - Class in elki.evaluation.clustering.internal: Parameterization class.
CLARA<V> - Class in elki.clustering.kmedoids: Clustering Large Applications (CLARA) is a clustering method for large data sets based on PAM, partitioning around medoids (PAM) based on sampling.
CLARA(Distance<? super V>, int, int, KMedoidsInitialization<V>, int, double, boolean, RandomFactory) - Constructor for class elki.clustering.kmedoids.CLARA: Constructor.
CLARA.CachedDistanceQuery<V> - Class in elki.clustering.kmedoids: Cached distance query.
CLARA.Par<V> - Class in elki.clustering.kmedoids: Parameterization class.
CLARANS<O> - Class in elki.clustering.kmedoids: CLARANS: a method for clustering objects for spatial data mining is inspired by PAM (partitioning around medoids, PAM) and CLARA and also based on sampling.
CLARANS(Distance<? super O>, int, int, double, RandomFactory) - Constructor for class elki.clustering.kmedoids.CLARANS: Constructor.
CLARANS.Assignment - Class in elki.clustering.kmedoids: Assignment state.
CLARANS.Par<V> - Class in elki.clustering.kmedoids: Parameterization class.
cleanup(Processor.Instance) - Method in class elki.clustering.dbscan.parallel.ParallelGeneralizedDBSCAN.Instance
cleanup(Processor.Instance) - Method in class elki.clustering.kmeans.parallel.KMeansProcessor
clear() - Method in class elki.clustering.kmedoids.CLARA.CachedDistanceQuery: Clear the distance cache.
CLINK<O> - Class in elki.clustering.hierarchical: CLINK algorithm for complete linkage.
CLINK(Distance<? super O>) - Constructor for class elki.clustering.hierarchical.CLINK: Constructor.
CLINK.Par<O> - Class in elki.clustering.hierarchical: Parameterization class.
clinkstep3(DBIDArrayIter, int, WritableDBIDDataStore, WritableDoubleDataStore, WritableDoubleDataStore) - Method in class elki.clustering.hierarchical.CLINK: Third step: Determine the values for P and L
clinkstep4567(DBIDRef, ArrayDBIDs, DBIDArrayIter, int, WritableDBIDDataStore, WritableDoubleDataStore, WritableDoubleDataStore) - Method in class elki.clustering.hierarchical.CLINK: Fourth to seventh step of CLINK: find best insertion
clinkstep8(DBIDRef, DBIDArrayIter, int, WritableDBIDDataStore, WritableDoubleDataStore) - Method in class elki.clustering.hierarchical.CLINK: Update hierarchy.
CLIQUE - Class in elki.clustering.subspace: Implementation of the CLIQUE algorithm, a grid-based algorithm to identify dense clusters in subspaces of maximum dimensionality.
CLIQUE(int, double, boolean) - Constructor for class elki.clustering.subspace.CLIQUE: Constructor.
CLIQUE.Par - Class in elki.clustering.subspace: Parameterization class.
CLIQUESubspace - Class in elki.clustering.subspace.clique: Represents a subspace of the original data space in the CLIQUE algorithm.
CLIQUESubspace(int) - Constructor for class elki.clustering.subspace.clique.CLIQUESubspace: Creates a new one-dimensional subspace of the original data space.
CLIQUESubspace(long[]) - Constructor for class elki.clustering.subspace.clique.CLIQUESubspace: Creates a new k-dimensional subspace of the original data space.
CLIQUEUnit - Class in elki.clustering.subspace.clique: Represents a unit in the CLIQUE algorithm.
CLIQUEUnit(int, double, double) - Constructor for class elki.clustering.subspace.clique.CLIQUEUnit: Creates a new one-dimensional unit for the given interval.
CLIQUEUnit(CLIQUEUnit, int, double, double, ModifiableDBIDs) - Constructor for class elki.clustering.subspace.clique.CLIQUEUnit: Creates a new k-dimensional unit for the given intervals.
clone(TextbookMultivariateGaussianModel) - Method in class elki.clustering.em.models.TextbookMultivariateGaussianModel: Copy the parameters of another model.
clusprog - Variable in class elki.clustering.dbscan.DBSCAN.Instance: Progress for clusters (may be null).
cluster - Variable in class elki.clustering.correlation.ORCLUS.ProjectedEnergy: Resulting merged cluster
Cluster<M extends Model> - Class in elki.data: Generic cluster class, that may or not have hierarchical information.
Cluster(DBIDs) - Constructor for class elki.data.Cluster: Constructor without hierarchy information and name and model
Cluster(DBIDs, boolean) - Constructor for class elki.data.Cluster: Constructor without hierarchy information and name and model
Cluster(DBIDs, boolean, M) - Constructor for class elki.data.Cluster: Constructor without hierarchy information and name
Cluster(DBIDs, M) - Constructor for class elki.data.Cluster: Constructor without hierarchy information and name
Cluster(String, DBIDs) - Constructor for class elki.data.Cluster: Constructor without hierarchy information and model
Cluster(String, DBIDs, boolean) - Constructor for class elki.data.Cluster: Constructor without hierarchy information and model
Cluster(String, DBIDs, boolean, M) - Constructor for class elki.data.Cluster: Full constructor
Cluster(String, DBIDs, M) - Constructor for class elki.data.Cluster: Constructor without hierarchy information.
CLUSTER - Static variable in class elki.data.model.ClusterModel: Static cluster model that can be shared for all clusters (since the object doesn't include meta information.
ClusterCandidate(P3C.Signature) - Constructor for class elki.clustering.subspace.P3C.ClusterCandidate: Constructor.
ClusterContingencyTable - Class in elki.evaluation.clustering: Class storing the contingency table and related data on two clusterings.
ClusterContingencyTable(boolean, boolean, Clustering<?>, Clustering<?>) - Constructor for class elki.evaluation.clustering.ClusterContingencyTable: Constructor.
ClusterDensityMergeHistory - Class in elki.clustering.hierarchical: Hierarchical clustering merge list, with additional coredists information.
ClusterDensityMergeHistory(ArrayDBIDs, int[], double[], int[], boolean, DoubleDataStore) - Constructor for class elki.clustering.hierarchical.ClusterDensityMergeHistory: Constructor.
ClusterDistanceMatrix - Class in elki.clustering.hierarchical: Shared code for algorithms that work on a pairwise cluster distance matrix.
ClusterDistanceMatrix(int) - Constructor for class elki.clustering.hierarchical.ClusterDistanceMatrix: Constructor.
ClusterFeature - Interface in elki.index.tree.betula.features: Interface for basic ClusteringFeature functions
ClusterFeature.Factory<F extends ClusterFeature> - Interface in elki.index.tree.betula.features: Cluster feature factory
ClusterHierarchyBuilder(DBIDs) - Constructor for class elki.clustering.optics.OPTICSXi.ClusterHierarchyBuilder: Constructor.
clusterids - Variable in class elki.clustering.dbscan.GriDBSCAN.Instance: Cluster assignments.
clusterids - Variable in class elki.clustering.dbscan.parallel.ParallelGeneralizedDBSCAN.Instance: Cluster assignment storage.
clusterIds - Variable in class elki.evaluation.clustering.pairsegments.Segment: The cluster numbers in each ring
clustering - Variable in class elki.clustering.optics.OPTICSXi.ClusterHierarchyBuilder: ELKI clustering object
Clustering<M extends Model> - Class in elki.data: Result class for clusterings.
Clustering() - Constructor for class elki.data.Clustering: Constructor for an empty clustering
Clustering(List<Cluster<M>>) - Constructor for class elki.data.Clustering: Constructor with a list of top level clusters
ClusteringAdjustedRandIndexSimilarity - Class in elki.similarity.cluster: Measure the similarity of clusters via the Adjusted Rand Index.
ClusteringAdjustedRandIndexSimilarity() - Constructor for class elki.similarity.cluster.ClusteringAdjustedRandIndexSimilarity: Constructor - use the static instance ClusteringAdjustedRandIndexSimilarity.STATIC!
ClusteringAdjustedRandIndexSimilarity.Par - Class in elki.similarity.cluster: Parameterization class.
ClusteringAlgorithm<C extends Clustering<? extends Model>> - Interface in elki.clustering: Interface for Algorithms that are capable to provide a Clustering as Result. in general, clustering algorithms are supposed to implement the Algorithm-Interface.
ClusteringAlgorithmUtil - Class in elki.clustering: Utility functionality for writing clustering algorithms.
ClusteringAlgorithmUtil() - Constructor for class elki.clustering.ClusteringAlgorithmUtil: Private constructor.
ClusteringBCubedF1Similarity - Class in elki.similarity.cluster: Measure the similarity of clusters via the BCubed F1 Index.
ClusteringBCubedF1Similarity() - Constructor for class elki.similarity.cluster.ClusteringBCubedF1Similarity: Constructor - use the static instance ClusteringBCubedF1Similarity.STATIC!
ClusteringBCubedF1Similarity.Par - Class in elki.similarity.cluster: Parameterization class.
ClusteringDistanceSimilarity - Interface in elki.similarity.cluster: Distance and similarity measure for clusterings.
ClusteringFeature - Class in elki.clustering.hierarchical.birch: Clustering Feature of BIRCH
ClusteringFeature(int) - Constructor for class elki.clustering.hierarchical.birch.ClusteringFeature: Constructor.
ClusteringFowlkesMallowsSimilarity - Class in elki.similarity.cluster: Measure the similarity of clusters via the Fowlkes-Mallows Index.
ClusteringFowlkesMallowsSimilarity() - Constructor for class elki.similarity.cluster.ClusteringFowlkesMallowsSimilarity: Constructor - use the static instance ClusteringFowlkesMallowsSimilarity.STATIC!
ClusteringFowlkesMallowsSimilarity.Par - Class in elki.similarity.cluster: Parameterization class.
ClusteringRandIndexSimilarity - Class in elki.similarity.cluster: Measure the similarity of clusters via the Rand Index.
ClusteringRandIndexSimilarity() - Constructor for class elki.similarity.cluster.ClusteringRandIndexSimilarity: Constructor - use the static instance ClusteringRandIndexSimilarity.STATIC!
ClusteringRandIndexSimilarity.Par - Class in elki.similarity.cluster: Parameterization class.
clusterings - Variable in class elki.evaluation.clustering.pairsegments.Segments: Clusterings
clusteringsCount - Variable in class elki.evaluation.clustering.pairsegments.Segments: Number of clusterings in comparison
ClusteringVectorDumper - Class in elki.result: Output a clustering result in a simple and compact ascii format: whitespace separated cluster indexes
ClusteringVectorDumper(Path, boolean) - Constructor for class elki.result.ClusteringVectorDumper: Constructor.
ClusteringVectorDumper(Path, boolean, String) - Constructor for class elki.result.ClusteringVectorDumper: Constructor.
ClusteringVectorDumper.Par - Class in elki.result: Parameterization class.
ClusteringVectorParser - Class in elki.datasource.parser: Parser for simple clustering results in vector form, as written by ClusteringVectorDumper.
ClusteringVectorParser(CSVReaderFormat) - Constructor for class elki.datasource.parser.ClusteringVectorParser: Constructor.
ClusteringVectorParser.Par - Class in elki.datasource.parser: Parameterization class.
ClusterIntersectionSimilarity - Class in elki.similarity.cluster: Measure the similarity of clusters via the intersection size.
ClusterIntersectionSimilarity() - Constructor for class elki.similarity.cluster.ClusterIntersectionSimilarity: Constructor - use the static instance ClusterIntersectionSimilarity.STATIC!
ClusterIntersectionSimilarity.Par - Class in elki.similarity.cluster: Parameterization class.
ClusterJaccardSimilarity - Class in elki.similarity.cluster: Measure the similarity of clusters via the Jaccard coefficient.
ClusterJaccardSimilarity() - Constructor for class elki.similarity.cluster.ClusterJaccardSimilarity: Constructor - use the static instance ClusterJaccardSimilarity.STATIC!
ClusterJaccardSimilarity.Par - Class in elki.similarity.cluster: Parameterization class.
clustermap - Variable in class elki.clustering.hierarchical.ClusterDistanceMatrix: Mapping from positions to cluster numbers
clusterMembers - Variable in class elki.clustering.hierarchical.extraction.AbstractCutDendrogram.Instance: Collected cluster members
ClusterMergeHistory - Class in elki.clustering.hierarchical: Merge history representing a hierarchical clustering.
ClusterMergeHistory(ArrayDBIDs, int[], double[], int[], boolean) - Constructor for class elki.clustering.hierarchical.ClusterMergeHistory: Constructor.
ClusterMergeHistoryBuilder - Class in elki.clustering.hierarchical: Class to help building a pointer hierarchy.
ClusterMergeHistoryBuilder(ArrayDBIDs, boolean) - Constructor for class elki.clustering.hierarchical.ClusterMergeHistoryBuilder: Constructor.
ClusterModel - Class in elki.data.model: Generic cluster model.
ClusterModel() - Constructor for class elki.data.model.ClusterModel
clusterOrder - Variable in class elki.clustering.correlation.HiCO.Instance: Cluster order.
clusterOrder - Variable in class elki.clustering.optics.OPTICSHeap.Instance: Output cluster order.
clusterOrder - Variable in class elki.clustering.optics.OPTICSList.Instance: Output cluster order.
clusterOrder - Variable in class elki.clustering.subspace.HiSC.Instance: Cluster order.
ClusterOrder - Class in elki.clustering.optics: Class to store the result of an ordering clustering algorithm such as OPTICS.
ClusterOrder(ArrayModifiableDBIDs, WritableDoubleDataStore, WritableDBIDDataStore) - Constructor for class elki.clustering.optics.ClusterOrder: Constructor
ClusterOrder(DBIDs) - Constructor for class elki.clustering.optics.ClusterOrder: Constructor
ClusterPairSegmentAnalysis - Class in elki.evaluation.clustering.pairsegments: Evaluate clustering results by building segments for their pairs: shared pairs and differences.
ClusterPairSegmentAnalysis() - Constructor for class elki.evaluation.clustering.pairsegments.ClusterPairSegmentAnalysis: Constructor.
ClusterPrototypeMergeHistory - Class in elki.clustering.hierarchical: Cluster merge history with additional cluster prototypes (for HACAM, MedoidLinkage, and MiniMax clustering)
ClusterPrototypeMergeHistory(ArrayDBIDs, int[], double[], int[], boolean, ArrayDBIDs) - Constructor for class elki.clustering.hierarchical.ClusterPrototypeMergeHistory: Constructor.
ClusterRadius - Class in elki.evaluation.clustering.internal: Evaluate a clustering by the (weighted) cluster radius.
ClusterRadius(NumberVectorDistance<?>, NoiseHandling) - Constructor for class elki.evaluation.clustering.internal.ClusterRadius: Constructor.
ClusterRadius.Par - Class in elki.evaluation.clustering.internal: Parameterization class.
clusters - Variable in class elki.clustering.hierarchical.HACAM.Instance: Cluster to members map
clusters - Variable in class elki.clustering.hierarchical.MedoidLinkage.Instance: Cluster to members map
clusters - Variable in class elki.clustering.hierarchical.MiniMax.Instance: Map to cluster members
clusters - Variable in class elki.clustering.kmeans.AbstractKMeans.Instance: Store the elements per cluster.
clusters - Variable in class elki.clustering.kmeans.KMeansMinusMinus.Instance: Cluster storage.
clusters - Variable in class elki.evaluation.clustering.pairsegments.Segments: Clusters
clusterSizes - Variable in class elki.clustering.kmeans.KDTreePruningKMeans.Instance: Number of elements in each cluster.
clusterSums - Variable in class elki.clustering.kmeans.KDTreePruningKMeans.Instance: To aggregate the sum of a cluster.
ClustersWithNoiseExtraction - Class in elki.clustering.hierarchical.extraction: Extraction of a given number of clusters with a minimum size, and noise.
ClustersWithNoiseExtraction(HierarchicalClusteringAlgorithm, int, int) - Constructor for class elki.clustering.hierarchical.extraction.ClustersWithNoiseExtraction: Constructor.
ClustersWithNoiseExtraction.Instance - Class in elki.clustering.hierarchical.extraction: Instance for a single data set.
ClustersWithNoiseExtraction.Par - Class in elki.clustering.hierarchical.extraction: Parameterization class.
cnum - Variable in class elki.clustering.kmeans.AnnulusKMeans.Instance: Sorted neighbors
cnum - Variable in class elki.clustering.kmeans.ExponionKMeans.Instance: Sorted neighbors
cnum - Variable in class elki.clustering.kmeans.SortMeans.Instance: Sorted neighbors
co - Variable in class elki.clustering.optics.OPTICSXi.SteepScanPosition: Cluster order
colcard - Variable in class elki.clustering.biclustering.ChengAndChurch.BiclusterCandidate: Cardinalities.
colDim - Variable in class elki.clustering.biclustering.AbstractBiclustering: Column dimensionality.
colIDs - Variable in class elki.data.model.BiclusterModel: The column numbers included in the Bicluster.
collectChildren(HDBSCANHierarchyExtraction.TempCluster, Clustering<DendrogramModel>, WritableDoubleDataStore, HDBSCANHierarchyExtraction.TempCluster, Cluster<DendrogramModel>, boolean) - Method in class elki.clustering.hierarchical.extraction.HDBSCANHierarchyExtraction.Instance: Recursive flattening of clusters.
colM - Variable in class elki.clustering.biclustering.ChengAndChurch.BiclusterCandidate: Means.
cols - Variable in class elki.clustering.biclustering.ChengAndChurch.BiclusterCandidate: Row and column bitmasks.
colsBitsetToIDs(long[]) - Method in class elki.clustering.biclustering.AbstractBiclustering: Convert a bitset into integer column ids.
colsBitsetToIDs(BitSet) - Method in class elki.clustering.biclustering.AbstractBiclustering: Convert a bitset into integer column ids.
combine(int, double, int, double, int, double) - Method in class elki.clustering.hierarchical.linkage.CentroidLinkage
combine(int, double, int, double, int, double) - Method in class elki.clustering.hierarchical.linkage.CompleteLinkage
combine(int, double, int, double, int, double) - Method in class elki.clustering.hierarchical.linkage.FlexibleBetaLinkage
combine(int, double, int, double, int, double) - Method in class elki.clustering.hierarchical.linkage.GroupAverageLinkage
combine(int, double, int, double, int, double) - Method in interface elki.clustering.hierarchical.linkage.Linkage: Compute combined linkage for two clusters.
combine(int, double, int, double, int, double) - Method in class elki.clustering.hierarchical.linkage.MedianLinkage
combine(int, double, int, double, int, double) - Method in class elki.clustering.hierarchical.linkage.MinimumVarianceLinkage
combine(int, double, int, double, int, double) - Method in class elki.clustering.hierarchical.linkage.SingleLinkage
combine(int, double, int, double, int, double) - Method in class elki.clustering.hierarchical.linkage.WardLinkage
combine(int, double, int, double, int, double) - Method in class elki.clustering.hierarchical.linkage.WeightedAverageLinkage
COMMENT - Static variable in class elki.clustering.meta.ExternalClustering: The comment character.
commonPreferenceVectors - Variable in class elki.clustering.subspace.HiSC.Instance: Shared preference vectors.
compare(DBIDRef, DBIDRef) - Method in class elki.clustering.correlation.HiCO.Instance
compare(DBIDRef, DBIDRef) - Method in class elki.clustering.optics.GeneralizedOPTICS.Instance
compare(DBIDRef, DBIDRef) - Method in class elki.clustering.subspace.HiSC.Instance
CompareMeans<V extends elki.data.NumberVector> - Class in elki.clustering.kmeans: Compare-Means: Accelerated k-means by exploiting the triangle inequality and pairwise distances of means to prune candidate means.
CompareMeans(NumberVectorDistance<? super V>, int, int, KMeansInitialization) - Constructor for class elki.clustering.kmeans.CompareMeans: Constructor.
CompareMeans.Instance - Class in elki.clustering.kmeans: Inner instance, storing state for a single data set.
CompareMeans.Par<V extends elki.data.NumberVector> - Class in elki.clustering.kmeans: Parameterization class.
compareTo(ORCLUS.ProjectedEnergy) - Method in class elki.clustering.correlation.ORCLUS.ProjectedEnergy: Compares this object with the specified object for order.
compareTo(Border) - Method in class elki.clustering.dbscan.util.Border
compareTo(OPTICSHeapEntry) - Method in class elki.clustering.optics.OPTICSHeapEntry
compareTo(PROCLUS.DoubleIntInt) - Method in class elki.clustering.subspace.PROCLUS.DoubleIntInt
compareTo(Segment) - Method in class elki.evaluation.clustering.pairsegments.Segment
complete() - Method in class elki.clustering.hierarchical.ClusterMergeHistoryBuilder: Finalize the result.
complete(WritableDoubleDataStore) - Method in class elki.clustering.hierarchical.ClusterMergeHistoryBuilder: Build a result with additional coredists information.
CompleteLinkage - Class in elki.clustering.hierarchical.linkage: Complete-linkage ("maximum linkage") clustering method.
CompleteLinkage() - Constructor for class elki.clustering.hierarchical.linkage.CompleteLinkage: Deprecated.
use the static instance CompleteLinkage.STATIC instead.
CompleteLinkage.Par - Class in elki.clustering.hierarchical.linkage: Class parameterizer.
computeAverageDistInSet() - Method in class elki.index.preprocessed.fastoptics.RandomProjectedNeighborsAndDensities: Compute for each point a density estimate as inverse of average distance to a point in a projected set
computeBadMedoids(ArrayDBIDs, ArrayList<PROCLUS.PROCLUSCluster>, int) - Method in class elki.clustering.subspace.PROCLUS: Computes the bad medoids, where the medoid of a cluster with less than the specified threshold of objects is bad.
computeBoundingBox(Relation<? extends NumberVector>, DBIDArrayIter) - Method in class elki.clustering.em.KDTreeEM.KDTree: Compute the bounding box.
computeClusterQuality(int, int) - Method in class elki.clustering.subspace.DOC: Computes the quality of a cluster based on its size and number of relevant attributes, as described via the μ-function from the paper.
computeColResidue(double[][], int) - Method in class elki.clustering.biclustering.ChengAndChurch.BiclusterCandidate: Computes the mean column residue of the given col.
computeCoreDists(DBIDs, KNNSearcher<DBIDRef>, int) - Method in class elki.clustering.hierarchical.AbstractHDBSCAN: Compute the core distances for all objects.
computeCostDifferential(DBIDRef, double[]) - Method in class elki.clustering.kmedoids.FastCLARANS.Assignment: Compute the reassignment cost, for one swap.
computeCostDifferential(DBIDRef, int, CLARANS.Assignment) - Method in class elki.clustering.kmedoids.CLARANS.Assignment: Compute the reassignment cost, for one swap.
computeDiffs(List<CLIQUESubspace>, int[], int[]) - Method in class elki.clustering.subspace.CLIQUE: The specified sorted list of dense subspaces is divided into the selected set I and the pruned set P.
computeDimensionMap(List<PROCLUS.DoubleIntInt>, int, int) - Method in class elki.clustering.subspace.PROCLUS: Compute the dimension map.
computeEntropyFirst(int[][], int, int, double, double, double[]) - Static method in class elki.evaluation.clustering.Entropy: Compute entropy of first clustering.
computeEntropySecond(int[][], int, int, double, double, double[]) - Static method in class elki.evaluation.clustering.Entropy: Compute entropy of second clustering.
computeFuzzyMembership(Relation<? extends NumberVector>, ArrayList<P3C.Signature>, ModifiableDBIDs, WritableDataStore<double[]>, List<MultivariateGaussianModel>, int) - Method in class elki.clustering.subspace.P3C: Computes a fuzzy membership with the weights based on which cluster cores each data point is part of.
computeGridBaseOffsets(int) - Method in class elki.clustering.dbscan.GriDBSCAN.Instance: Compute the grid base offset.
computeLocalModel(DBIDRef, DoubleDBIDList, Relation<? extends NumberVector>) - Method in class elki.clustering.dbscan.predicates.COPACNeighborPredicate: COPAC model computation
computeLocalModel(DBIDRef, DoubleDBIDList, Relation<? extends NumberVector>) - Method in class elki.clustering.dbscan.predicates.FourCNeighborPredicate
computeLocalModel(DBIDRef, DoubleDBIDList, Relation<? extends NumberVector>) - Method in class elki.clustering.dbscan.predicates.PreDeConNeighborPredicate
computeLocalModel(DBIDRef, DoubleDBIDList, Relation<? extends O>) - Method in class elki.clustering.dbscan.predicates.AbstractRangeQueryNeighborPredicate: Method to compute the actual data model.
computeM_current(DBIDs, DBIDs, DBIDs, Random) - Method in class elki.clustering.subspace.PROCLUS: Computes the set of medoids in current iteration.
computeMeans(List<CLIQUESubspace>) - Method in class elki.clustering.subspace.CLIQUE: The specified sorted list of dense subspaces is divided into the selected set I and the pruned set P.
computeMeanSquaredDeviation(double[][]) - Method in class elki.clustering.biclustering.ChengAndChurch.BiclusterCandidate: Compute the mean square residue.
computeMIFull(int[][], int, int, int, int, double, double, double[]) - Method in class elki.evaluation.clustering.Entropy: Full computation of mutual information measures, including AMI/EMI.
computeMILarge(int[][], int, int, double, double) - Method in class elki.evaluation.clustering.Entropy: Compute mutual information measures, but skip expensive computation of AMI/EMI for large data sets, where they do not differ much.
computeReassignmentCost(DBIDRef, double[]) - Method in class elki.clustering.kmedoids.FastPAM1.Instance: Compute the reassignment cost, for all medoids in one pass.
computeReassignmentCost(DBIDRef, int) - Method in class elki.clustering.kmedoids.FastPAM.Instance: Compute the reassignment cost of one swap.
computeReassignmentCost(DBIDRef, int) - Method in class elki.clustering.kmedoids.PAM.Instance: Compute the reassignment cost of one swap.
computeReassignmentCost(DBIDRef, WritableDoubleDataStore) - Method in class elki.clustering.kmedoids.ReynoldsPAM.Instance: Compute the reassignment cost, for all medoids in one pass.
computeRemovalCost(double[]) - Method in class elki.clustering.kmedoids.FastCLARANS.Assignment: Precompute the costs of reassigning to the second closest medoid.
computeRemovalCost(int, WritableDoubleDataStore) - Method in class elki.clustering.kmedoids.ReynoldsPAM.Instance: Compute the cost of removing a medoid just once.
computeRowResidue(double[][], int, boolean) - Method in class elki.clustering.biclustering.ChengAndChurch.BiclusterCandidate: Computes the mean row residue of the given row.
computeSetsBounds(Relation<? extends NumberVector>, int, DBIDs) - Method in class elki.index.preprocessed.fastoptics.RandomProjectedNeighborsAndDensities: Create random projections, project points and put points into sets of size about minSplitSize/2
computeSquaredSeparation(double[][]) - Method in class elki.clustering.kmeans.AbstractKMeans.Instance: Initial separation of means.
computeTau(long, long, double, long, long) - Method in class elki.evaluation.clustering.internal.ConcordantPairsGammaTau: Compute the Tau correlation measure
computeWithinDistances(Relation<? extends NumberVector>, List<? extends Cluster<?>>, int) - Method in class elki.evaluation.clustering.internal.ConcordantPairsGammaTau
computeZijs(double[][], int) - Method in class elki.clustering.subspace.PROCLUS: Compute the z_ij values.
ConcordantPairsGammaTau - Class in elki.evaluation.clustering.internal: Compute the Gamma Criterion of a data set.
ConcordantPairsGammaTau(PrimitiveDistance<? super NumberVector>, NoiseHandling) - Constructor for class elki.evaluation.clustering.internal.ConcordantPairsGammaTau: Constructor.
ConcordantPairsGammaTau.Par - Class in elki.evaluation.clustering.internal: Parameterization class.
conditionalEntropyFirst() - Method in class elki.evaluation.clustering.Entropy: Get the conditional entropy of the first clustering (not normalized, 0 = equal).
conditionalEntropySecond() - Method in class elki.evaluation.clustering.Entropy: Get the conditional entropy of the first clustering (not normalized, 0 = equal).
configDelta(Parameterization) - Method in class elki.clustering.subspace.PreDeCon.Settings.Par: Configure the delta parameter.
configEpsilon(Parameterization) - Method in class elki.clustering.subspace.PreDeCon.Settings.Par: Configure the epsilon radius parameter.
configKappa(Parameterization) - Method in class elki.clustering.subspace.PreDeCon.Settings.Par: Configure the kappa parameter.
configLambda(Parameterization) - Method in class elki.clustering.subspace.PreDeCon.Settings.Par: Configure the delta parameter.
configMinPts(Parameterization) - Method in class elki.clustering.subspace.PreDeCon.Settings.Par: Configure the minPts aka "mu" parameter.
configure(Parameterization) - Method in class elki.clustering.BetulaLeafPreClustering.Par
configure(Parameterization) - Method in class elki.clustering.CFSFDP.Par
configure(Parameterization) - Method in class elki.clustering.correlation.COPAC.Par
configure(Parameterization) - Method in class elki.clustering.correlation.ERiC.Par
configure(Parameterization) - Method in class elki.clustering.correlation.FourC.Par
configure(Parameterization) - Method in class elki.clustering.correlation.FourC.Settings.Par
configure(Parameterization) - Method in class elki.clustering.correlation.HiCO.Par
configure(Parameterization) - Method in class elki.clustering.correlation.LMCLUS.Par
configure(Parameterization) - Method in class elki.clustering.correlation.ORCLUS.Par
configure(Parameterization) - Method in class elki.clustering.dbscan.DBSCAN.Par
configure(Parameterization) - Method in class elki.clustering.dbscan.GeneralizedDBSCAN.Par
configure(Parameterization) - Method in class elki.clustering.dbscan.LSDBC.Par
configure(Parameterization) - Method in class elki.clustering.dbscan.parallel.ParallelGeneralizedDBSCAN.Par
configure(Parameterization) - Method in class elki.clustering.dbscan.predicates.COPACNeighborPredicate.Par
configure(Parameterization) - Method in class elki.clustering.dbscan.predicates.ERiCNeighborPredicate.Par
configure(Parameterization) - Method in class elki.clustering.dbscan.predicates.FourCCorePredicate.Par
configure(Parameterization) - Method in class elki.clustering.dbscan.predicates.FourCNeighborPredicate.Par
configure(Parameterization) - Method in class elki.clustering.dbscan.predicates.MinPtsCorePredicate.Par
configure(Parameterization) - Method in class elki.clustering.dbscan.predicates.PreDeConCorePredicate.Par
configure(Parameterization) - Method in class elki.clustering.dbscan.predicates.PreDeConNeighborPredicate.Par
configure(Parameterization) - Method in class elki.clustering.em.BetulaGMM.Par
configure(Parameterization) - Method in class elki.clustering.em.EM.Par
configure(Parameterization) - Method in class elki.clustering.em.KDTreeEM.Par
configure(Parameterization) - Method in class elki.clustering.em.models.BetulaDiagonalGaussianModelFactory.Par
configure(Parameterization) - Method in class elki.clustering.em.models.BetulaMultivariateGaussianModelFactory.Par
configure(Parameterization) - Method in class elki.clustering.em.models.BetulaSphericalGaussianModelFactory.Par
configure(Parameterization) - Method in class elki.clustering.hierarchical.birch.BIRCHKMeansPlusPlus.Par
configure(Parameterization) - Method in class elki.clustering.hierarchical.birch.BIRCHLeafClustering.Par
configure(Parameterization) - Method in class elki.clustering.hierarchical.birch.BIRCHLloydKMeans.Par
configure(Parameterization) - Method in class elki.clustering.hierarchical.birch.CFTree.Factory.Par
configure(Parameterization) - Method in class elki.clustering.hierarchical.CLINK.Par
configure(Parameterization) - Method in class elki.clustering.hierarchical.extraction.AbstractCutDendrogram.Par
configure(Parameterization) - Method in class elki.clustering.hierarchical.extraction.ClustersWithNoiseExtraction.Par
configure(Parameterization) - Method in class elki.clustering.hierarchical.extraction.CutDendrogramByHeight.Par
configure(Parameterization) - Method in class elki.clustering.hierarchical.extraction.CutDendrogramByNumberOfClusters.Par
configure(Parameterization) - Method in class elki.clustering.hierarchical.extraction.HDBSCANHierarchyExtraction.Par
configure(Parameterization) - Method in class elki.clustering.hierarchical.extraction.SimplifiedHierarchyExtraction.Par
configure(Parameterization) - Method in class elki.clustering.hierarchical.linkage.FlexibleBetaLinkage.Par
configure(Parameterization) - Method in class elki.clustering.hierarchical.OPTICSToHierarchical.Par
configure(Parameterization) - Method in class elki.clustering.hierarchical.SLINK.Par
configure(Parameterization) - Method in class elki.clustering.kcenter.GreedyKCenter.Par
configure(Parameterization) - Method in class elki.clustering.kmeans.AbstractKMeans.Par
configure(Parameterization) - Method in class elki.clustering.kmeans.BetulaLloydKMeans.Par
configure(Parameterization) - Method in class elki.clustering.kmeans.FuzzyCMeans.Par
configure(Parameterization) - Method in class elki.clustering.kmeans.HamerlyKMeans.Par
configure(Parameterization) - Method in class elki.clustering.kmeans.HartiganWongKMeans.Parameterizer
configure(Parameterization) - Method in class elki.clustering.kmeans.initialization.AbstractKMeansInitialization.Par
configure(Parameterization) - Method in class elki.clustering.kmeans.initialization.betula.AbstractCFKMeansInitialization.Par
configure(Parameterization) - Method in class elki.clustering.kmeans.initialization.betula.CFKPlusPlusLeaves.Par
configure(Parameterization) - Method in class elki.clustering.kmeans.initialization.betula.CFKPlusPlusTree.Par
configure(Parameterization) - Method in class elki.clustering.kmeans.initialization.betula.CFKPlusPlusTrunk.Par
configure(Parameterization) - Method in class elki.clustering.kmeans.initialization.FarthestPoints.Par
configure(Parameterization) - Method in class elki.clustering.kmeans.initialization.KMC2.Par
configure(Parameterization) - Method in class elki.clustering.kmeans.initialization.Predefined.Par
configure(Parameterization) - Method in class elki.clustering.kmeans.initialization.SphericalAFKMC2.Par
configure(Parameterization) - Method in class elki.clustering.kmeans.initialization.SphericalKMeansPlusPlus.Par
configure(Parameterization) - Method in class elki.clustering.kmeans.KDTreePruningKMeans.Par
configure(Parameterization) - Method in class elki.clustering.kmeans.KMeansMinusMinus.Par
configure(Parameterization) - Method in class elki.clustering.kmeans.SimplifiedElkanKMeans.Par
configure(Parameterization) - Method in class elki.clustering.kmeans.SingleAssignmentKMeans.Par
configure(Parameterization) - Method in class elki.clustering.kmeans.spherical.EuclideanSphericalHamerlyKMeans.Par
configure(Parameterization) - Method in class elki.clustering.kmeans.spherical.EuclideanSphericalSimplifiedElkanKMeans.Par
configure(Parameterization) - Method in class elki.clustering.kmeans.spherical.SphericalHamerlyKMeans.Par
configure(Parameterization) - Method in class elki.clustering.kmeans.spherical.SphericalKMeans.Par
configure(Parameterization) - Method in class elki.clustering.kmeans.spherical.SphericalSimplifiedElkanKMeans.Par
configure(Parameterization) - Method in class elki.clustering.kmeans.spherical.SphericalSimplifiedHamerlyKMeans.Par
configure(Parameterization) - Method in class elki.clustering.kmeans.spherical.SphericalSingleAssignmentKMeans.Par
configure(Parameterization) - Method in class elki.clustering.kmeans.YinYangKMeans.Par
configure(Parameterization) - Method in class elki.clustering.kmedoids.AlternatingKMedoids.Par
configure(Parameterization) - Method in class elki.clustering.kmedoids.CLARA.Par
configure(Parameterization) - Method in class elki.clustering.kmedoids.CLARANS.Par
configure(Parameterization) - Method in class elki.clustering.kmedoids.FastCLARA.Par
configure(Parameterization) - Method in class elki.clustering.kmedoids.FasterCLARA.Par
configure(Parameterization) - Method in class elki.clustering.kmedoids.FastPAM.Par
configure(Parameterization) - Method in class elki.clustering.kmedoids.initialization.AlternateRefinement.Par
configure(Parameterization) - Method in class elki.clustering.kmedoids.initialization.KMedoidsKMedoidsInitialization.Par
configure(Parameterization) - Method in class elki.clustering.kmedoids.PAM.Par
configure(Parameterization) - Method in class elki.clustering.kmedoids.SingleAssignmentKMedoids.Par
configure(Parameterization) - Method in class elki.clustering.meta.ExternalClustering.Par
configure(Parameterization) - Method in class elki.clustering.onedimensional.KNNKernelDensityMinimaClustering.Par
configure(Parameterization) - Method in class elki.clustering.optics.OPTICSXi.Par
configure(Parameterization) - Method in class elki.clustering.subspace.CLIQUE.Par
configure(Parameterization) - Method in class elki.clustering.subspace.DOC.Par
configure(Parameterization) - Method in class elki.clustering.subspace.FastDOC.Par
configure(Parameterization) - Method in class elki.clustering.subspace.HiSC.Par
configure(Parameterization) - Method in class elki.clustering.subspace.P3C.Par
configure(Parameterization) - Method in class elki.clustering.subspace.PreDeCon.Par
configure(Parameterization) - Method in class elki.clustering.subspace.PreDeCon.Settings.Par
configure(Parameterization) - Method in class elki.clustering.subspace.PROCLUS.Par
configure(Parameterization) - Method in class elki.clustering.subspace.SUBCLU.Par
configure(Parameterization) - Method in class elki.clustering.trivial.ByLabelClustering.Par
configure(Parameterization) - Method in class elki.evaluation.clustering.EvaluateClustering.Par
configure(Parameterization) - Method in class elki.evaluation.clustering.extractor.CutDendrogramByHeightExtractor.Par
configure(Parameterization) - Method in class elki.evaluation.clustering.extractor.CutDendrogramByNumberOfClustersExtractor.Par
configure(Parameterization) - Method in class elki.evaluation.clustering.extractor.HDBSCANHierarchyExtractionEvaluator.Par
configure(Parameterization) - Method in class elki.evaluation.clustering.extractor.SimplifiedHierarchyExtractionEvaluator.Par
configure(Parameterization) - Method in class elki.evaluation.clustering.internal.CIndex.Par
configure(Parameterization) - Method in class elki.evaluation.clustering.internal.ClusterRadius.Par
configure(Parameterization) - Method in class elki.evaluation.clustering.internal.ConcordantPairsGammaTau.Par
configure(Parameterization) - Method in class elki.evaluation.clustering.internal.DaviesBouldinIndex.Par
configure(Parameterization) - Method in class elki.evaluation.clustering.internal.DBCV.Par
configure(Parameterization) - Method in class elki.evaluation.clustering.internal.PBMIndex.Par
configure(Parameterization) - Method in class elki.evaluation.clustering.internal.Silhouette.Par
configure(Parameterization) - Method in class elki.evaluation.clustering.internal.SimplifiedSilhouette.Par
configure(Parameterization) - Method in class elki.evaluation.clustering.internal.SquaredErrors.Par
configure(Parameterization) - Method in class elki.evaluation.clustering.internal.VarianceRatioCriterion.Par
configure(Parameterization) - Method in class elki.index.preprocessed.fastoptics.RandomProjectedNeighborsAndDensities.Par
configure(Parameterization) - Method in class elki.index.tree.betula.CFTree.Factory.Par
configure(Parameterization) - Method in class elki.result.ClusteringVectorDumper.Par
configureInformationCriterion(Parameterization) - Method in class elki.clustering.kmeans.GMeans.Par
constructOneSignatures(SetDBIDs[][], long[][]) - Method in class elki.clustering.subspace.P3C: Construct the 1-signatures by merging adjacent dense bins.
contains(NumberVector) - Method in class elki.clustering.subspace.clique.CLIQUEUnit: Returns true, if the intervals of this unit contain the specified feature vector.
contains(DBIDRef) - Method in class elki.clustering.dbscan.predicates.COPACNeighborPredicate.COPACModel
containsLeftNeighbor(CLIQUEUnit, int) - Method in class elki.clustering.subspace.clique.CLIQUEUnit: Returns true if this unit is the left neighbor of the given unit.
containsRightNeighbor(CLIQUEUnit, int) - Method in class elki.clustering.subspace.clique.CLIQUEUnit: Returns true if this unit is the right neighbor of the given unit.
contingency - Variable in class elki.evaluation.clustering.ClusterContingencyTable: Contingency matrix
contmat - Variable in class elki.evaluation.clustering.EvaluateClustering.ScoreResult: Cluster contingency table
convergence - Variable in class elki.clustering.affinitypropagation.AffinityPropagation: Terminate after 10 iterations with no changes.
convertOutput(ClusterMergeHistoryBuilder, ArrayDBIDs, DBIDDataStore, DoubleDataStore) - Static method in class elki.clustering.hierarchical.SLINK: Convert a SLINK pointer representation to a cluster merge history.
convertToMergeList(ArrayDBIDs, DoubleLongHeap, ClusterMergeHistoryBuilder) - Method in class elki.clustering.hierarchical.AbstractHDBSCAN: Convert spanning tree to a pointer representation.
COPAC - Class in elki.clustering.correlation: COPAC is an algorithm to partition a database according to the correlation dimension of its objects and to then perform an arbitrary clustering algorithm over the partitions.
COPAC(COPAC.Settings) - Constructor for class elki.clustering.correlation.COPAC: Constructor.
COPAC.Par - Class in elki.clustering.correlation: Parameterization class.
COPAC.Settings - Class in elki.clustering.correlation: Class to wrap the COPAC settings.
COPACModel(int, SetDBIDs) - Constructor for class elki.clustering.dbscan.predicates.COPACNeighborPredicate.COPACModel: COPAC model.
COPACNeighborPredicate - Class in elki.clustering.dbscan.predicates: COPAC neighborhood predicate.
COPACNeighborPredicate(COPAC.Settings) - Constructor for class elki.clustering.dbscan.predicates.COPACNeighborPredicate: Constructor.
COPACNeighborPredicate.COPACModel - Class in elki.clustering.dbscan.predicates: Model used by COPAC for core point property.
COPACNeighborPredicate.Instance - Class in elki.clustering.dbscan.predicates: Instance for a particular data set.
COPACNeighborPredicate.Par - Class in elki.clustering.dbscan.predicates: Parameterization class.
copyMeans(double[][], double[][]) - Method in class elki.clustering.kmeans.AbstractKMeans.Instance: Copy means
core - Variable in class elki.clustering.dbscan.util.Border: Cluster number
core - Variable in class elki.data.model.CoreObjectsModel: Objects that are part of the cluster core.
Core - Class in elki.clustering.dbscan.util: Core point assignment.
Core(int) - Constructor for class elki.clustering.dbscan.util.Core: Constructor.
coredist - Variable in class elki.clustering.hierarchical.extraction.HDBSCANHierarchyExtraction.Instance: Core distances, if available.
coredist - Variable in class elki.clustering.hierarchical.extraction.SimplifiedHierarchyExtraction.Instance: Core distances (if available, may be null).
coredists - Variable in class elki.clustering.hierarchical.AbstractHDBSCAN.HDBSCANAdapter: Core distance storage.
coredists - Variable in class elki.clustering.hierarchical.ClusterDensityMergeHistory: Core distance information.
coremodel - Variable in class elki.clustering.dbscan.GeneralizedDBSCAN: Track which objects are "core" objects.
coremodel - Variable in class elki.clustering.dbscan.GeneralizedDBSCAN.Instance: Track which objects are "core" objects.
coremodel - Variable in class elki.clustering.dbscan.GeneralizedDBSCAN.Par: Track which objects are "core" objects.
coremodel - Variable in class elki.clustering.dbscan.parallel.ParallelGeneralizedDBSCAN: Track which objects are "core" objects.
coremodel - Variable in class elki.clustering.dbscan.parallel.ParallelGeneralizedDBSCAN.Instance: Track which objects are "core" objects.
coremodel - Variable in class elki.clustering.dbscan.parallel.ParallelGeneralizedDBSCAN.Par: Track which objects are "core" objects.
COREMODEL_ID - Static variable in class elki.clustering.dbscan.GeneralizedDBSCAN.Par: Flag to keep track of core points.
COREMODEL_ID - Static variable in class elki.clustering.dbscan.parallel.ParallelGeneralizedDBSCAN.Par: Flag to keep track of core points.
CoreObjectsModel - Class in elki.data.model: Cluster model using "core" objects.
CoreObjectsModel(DBIDs) - Constructor for class elki.data.model.CoreObjectsModel: Constructor.
corepred - Variable in class elki.clustering.dbscan.GeneralizedDBSCAN: The core predicate factory.
corepred - Variable in class elki.clustering.dbscan.GeneralizedDBSCAN.Instance: The core object property
corepred - Variable in class elki.clustering.dbscan.GeneralizedDBSCAN.Par: Core point predicate.
corepred - Variable in class elki.clustering.dbscan.parallel.ParallelGeneralizedDBSCAN: The core predicate factory.
corepred - Variable in class elki.clustering.dbscan.parallel.ParallelGeneralizedDBSCAN.Instance: The core object property
corepred - Variable in class elki.clustering.dbscan.parallel.ParallelGeneralizedDBSCAN.Par: Core point predicate.
COREPRED_ID - Static variable in class elki.clustering.dbscan.GeneralizedDBSCAN.Par: Parameter for core predicate.
COREPRED_ID - Static variable in class elki.clustering.dbscan.parallel.ParallelGeneralizedDBSCAN.Par: Parameter for core predicate.
CorePredicate<T> - Interface in elki.clustering.dbscan.predicates: Predicate for GeneralizedDBSCAN to evaluate whether a point is a core point or not.
CorePredicate.Instance<T> - Interface in elki.clustering.dbscan.predicates: Instance for a particular data set.
cores - Variable in class elki.clustering.dbscan.GriDBSCAN.Instance: Core identifier objects (shared to conserve memory).
cores - Variable in class elki.clustering.dbscan.parallel.ParallelGeneralizedDBSCAN.Instance: Core objects (shared)
CorrelationClusterOrder - Class in elki.clustering.optics: Cluster order entry for correlation-based OPTICS variants.
CorrelationClusterOrder(ArrayModifiableDBIDs, WritableDoubleDataStore, WritableDBIDDataStore, WritableIntegerDataStore) - Constructor for class elki.clustering.optics.CorrelationClusterOrder: Constructor.
correlationDistance(PCAFilteredResult, PCAFilteredResult, int) - Method in class elki.clustering.correlation.HiCO: Computes the correlation distance between the two subspaces defined by the specified PCAs.
CorrelationModel - Class in elki.data.model: Cluster model using a filtered PCA result and an centroid.
CorrelationModel(PCAFilteredResult, double[]) - Constructor for class elki.data.model.CorrelationModel: Constructor
correlationValue - Variable in class elki.clustering.correlation.HiCO.Instance: Correlation value.
correlationValue - Variable in class elki.clustering.optics.CorrelationClusterOrder: The correlation dimension.
correlationValue - Variable in class elki.clustering.subspace.HiSC.Instance: Correlation dimensionality.
countTies(double[], int[]) - Method in class elki.evaluation.clustering.internal.ConcordantPairsGammaTau: Count (and annotate) the number of tied values.
covariance - Variable in class elki.clustering.em.models.MultivariateGaussianModel: Covariance matrix.
covariance - Variable in class elki.clustering.em.models.TextbookMultivariateGaussianModel: Covariance matrix.
covariance - Variable in class elki.clustering.em.models.TwoPassMultivariateGaussianModel: Covariance matrix.
covariance() - Method in class elki.index.tree.betula.features.BIRCHCF
covariance() - Method in interface elki.index.tree.betula.features.ClusterFeature: returns the covariance matrix
covariance() - Method in class elki.index.tree.betula.features.VIIFeature
covariance() - Method in class elki.index.tree.betula.features.VVIFeature
covariance() - Method in class elki.index.tree.betula.features.VVVFeature
covarianceMatrix - Variable in class elki.data.model.EMModel: Cluster covariance matrix
coverage - Variable in class elki.clustering.subspace.clique.CLIQUESubspace: The coverage of this subspace, which is the number of all feature vectors that fall inside the dense units of this subspace.
critical - Variable in class elki.clustering.kmeans.GMeans: Critical value
critical - Variable in class elki.clustering.kmeans.GMeans.Par: Critical value
CRITICAL_ID - Static variable in class elki.clustering.kmeans.GMeans.Par: Critical value for the Anderson-Darling-Test
cs - Variable in class elki.clustering.dbscan.util.MultiBorder: Cluster numbers
csim - Variable in class elki.clustering.kmeans.spherical.SphericalHamerlyKMeans.Instance: Cluster self-similarity.
csim - Variable in class elki.clustering.kmeans.spherical.SphericalSimplifiedElkanKMeans.Instance: Cluster self-similarity.
csim - Variable in class elki.clustering.kmeans.spherical.SphericalSimplifiedHamerlyKMeans.Instance: Cluster self-similarity.
csize - Variable in class elki.clustering.hierarchical.ClusterMergeHistoryBuilder: Cluster size storage.
cur - Variable in class elki.clustering.optics.OPTICSXi.SteepScanPosition: Variable for accessing.
curclu - Variable in class elki.datasource.parser.ClusteringVectorParser: Current clustering.
curclusters - Variable in class elki.clustering.optics.OPTICSXi.ClusterHierarchyBuilder: Current "unattached" clusters.
curlbl - Variable in class elki.datasource.parser.ClusteringVectorParser: Current labels.
current - Variable in class elki.clustering.hierarchical.birch.CFTree.LeafIterator: Current leaf entry.
current - Variable in class elki.index.tree.betula.CFTree.LeafIterator: Current leaf entry.
CutDendrogramByHeight - Class in elki.clustering.hierarchical.extraction: Extract a flat clustering from a full hierarchy, represented in pointer form.
CutDendrogramByHeight(HierarchicalClusteringAlgorithm, double, boolean) - Constructor for class elki.clustering.hierarchical.extraction.CutDendrogramByHeight: Constructor.
CutDendrogramByHeight(HierarchicalClusteringAlgorithm, double, boolean, boolean) - Constructor for class elki.clustering.hierarchical.extraction.CutDendrogramByHeight: Constructor.
CutDendrogramByHeight.Instance - Class in elki.clustering.hierarchical.extraction: Instance for a single data set.
CutDendrogramByHeight.Par - Class in elki.clustering.hierarchical.extraction: Parameterization class.
CutDendrogramByHeightExtractor - Class in elki.evaluation.clustering.extractor: Extract clusters from a hierarchical clustering, during the evaluation phase.
CutDendrogramByHeightExtractor(CutDendrogramByHeight) - Constructor for class elki.evaluation.clustering.extractor.CutDendrogramByHeightExtractor: Constructor.
CutDendrogramByHeightExtractor.Par - Class in elki.evaluation.clustering.extractor: Parameterization class.
CutDendrogramByNumberOfClusters - Class in elki.clustering.hierarchical.extraction: Extract a flat clustering from a full hierarchy, represented in pointer form.
CutDendrogramByNumberOfClusters(HierarchicalClusteringAlgorithm, int, boolean) - Constructor for class elki.clustering.hierarchical.extraction.CutDendrogramByNumberOfClusters: Constructor.
CutDendrogramByNumberOfClusters(HierarchicalClusteringAlgorithm, int, boolean, boolean) - Constructor for class elki.clustering.hierarchical.extraction.CutDendrogramByNumberOfClusters: Constructor.
CutDendrogramByNumberOfClusters.Instance - Class in elki.clustering.hierarchical.extraction: Instance for a single data set.
CutDendrogramByNumberOfClusters.Par - Class in elki.clustering.hierarchical.extraction: Parameterization class.
CutDendrogramByNumberOfClustersExtractor - Class in elki.evaluation.clustering.extractor: Extract clusters from a hierarchical clustering, during the evaluation phase.
CutDendrogramByNumberOfClustersExtractor(CutDendrogramByNumberOfClusters) - Constructor for class elki.evaluation.clustering.extractor.CutDendrogramByNumberOfClustersExtractor: Constructor.
CutDendrogramByNumberOfClustersExtractor.Par - Class in elki.evaluation.clustering.extractor: Parameterization class.

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