A-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

A

abs - Variable in class elki.index.tree.betula.CFTree: Absorption criterion
abs - Variable in class elki.index.tree.betula.CFTree.Factory: BIRCH distance function to use for point absorption
abs - Variable in class elki.index.tree.betula.CFTree.Factory.Par: BIRCH distance function to use for point absorption
absolute - Variable in class elki.clustering.correlation.FourC.Settings: Use absolute variance, not relative variance.
absorption - Variable in class elki.clustering.hierarchical.birch.CFTree: Criterion for absorbing points.
absorption - Variable in class elki.clustering.hierarchical.birch.CFTree.Factory: Criterion for absorbing points.
absorption - Variable in class elki.clustering.hierarchical.birch.CFTree.Factory.Par: Criterion for absorbing points.
ABSORPTION_ID - Static variable in class elki.clustering.hierarchical.birch.CFTree.Factory.Par: Absorption parameter.
ABSORPTION_ID - Static variable in class elki.index.tree.betula.CFTree.Factory.Par: Absorption parameter.
absstat - Variable in class elki.index.tree.betula.CFTree: Number ob absorption calculations
AbstractBiclustering<M extends BiclusterModel> - Class in elki.clustering.biclustering: Abstract class as a convenience for different biclustering approaches.
AbstractBiclustering() - Constructor for class elki.clustering.biclustering.AbstractBiclustering: Constructor.
AbstractCFKMeansInitialization - Class in elki.clustering.kmeans.initialization.betula: Abstract base class for CF k-means initializations.
AbstractCFKMeansInitialization(RandomFactory) - Constructor for class elki.clustering.kmeans.initialization.betula.AbstractCFKMeansInitialization: Constructor.
AbstractCFKMeansInitialization.Par - Class in elki.clustering.kmeans.initialization.betula: Parameterization class.
AbstractCutDendrogram - Class in elki.clustering.hierarchical.extraction: Abstract base class for extracting clusters from dendrograms.
AbstractCutDendrogram(HierarchicalClusteringAlgorithm, boolean, boolean) - Constructor for class elki.clustering.hierarchical.extraction.AbstractCutDendrogram: Constructor.
AbstractCutDendrogram.Instance - Class in elki.clustering.hierarchical.extraction: Instance for a single data set.
AbstractCutDendrogram.Par - Class in elki.clustering.hierarchical.extraction: Parameterization class.
AbstractHDBSCAN<O> - Class in elki.clustering.hierarchical: Abstract base class for HDBSCAN variations.
AbstractHDBSCAN(Distance<? super O>, int) - Constructor for class elki.clustering.hierarchical.AbstractHDBSCAN: Constructor.
AbstractHDBSCAN.HDBSCANAdapter - Class in elki.clustering.hierarchical: Class for processing the HDBSCAN G_mpts graph.
AbstractHDBSCAN.HeapMSTCollector - Class in elki.clustering.hierarchical: Class for collecting the minimum spanning tree edges into a heap.
AbstractKMeans<V extends elki.data.NumberVector,M extends Model> - Class in elki.clustering.kmeans: Abstract base class for k-means implementations.
AbstractKMeans(int, int, KMeansInitialization) - Constructor for class elki.clustering.kmeans.AbstractKMeans: Constructor.
AbstractKMeans(NumberVectorDistance<? super V>, int, int, KMeansInitialization) - Constructor for class elki.clustering.kmeans.AbstractKMeans: Constructor.
AbstractKMeans.Instance - Class in elki.clustering.kmeans: Inner instance for a run, for better encapsulation, that encapsulates the standard flow of most (but not all) k-means variations.
AbstractKMeans.Par<V extends elki.data.NumberVector> - Class in elki.clustering.kmeans: Parameterization class.
AbstractKMeansInitialization - Class in elki.clustering.kmeans.initialization: Abstract base class for common k-means initializations.
AbstractKMeansInitialization(RandomFactory) - Constructor for class elki.clustering.kmeans.initialization.AbstractKMeansInitialization: Constructor.
AbstractKMeansInitialization.Par - Class in elki.clustering.kmeans.initialization: Parameterization class.
AbstractKMeansQualityMeasure<O extends elki.data.NumberVector> - Class in elki.clustering.kmeans.quality: Base class for evaluating clusterings by information criteria (such as AIC or BIC).
AbstractKMeansQualityMeasure() - Constructor for class elki.clustering.kmeans.quality.AbstractKMeansQualityMeasure
AbstractOPTICS<O> - Class in elki.clustering.optics: The OPTICS algorithm for density-based hierarchical clustering.
AbstractOPTICS(Distance<? super O>, double, int) - Constructor for class elki.clustering.optics.AbstractOPTICS: Constructor.
AbstractProjectedClustering<R extends Clustering<?>> - Class in elki.clustering: Abstract superclass for projected clustering algorithms, like PROCLUS and ORCLUS.
AbstractProjectedClustering(int, int, int) - Constructor for class elki.clustering.AbstractProjectedClustering: Internal constructor.
AbstractProjectedClustering.Par - Class in elki.clustering: Parameterization class.
AbstractRangeQueryNeighborPredicate<O,M,N> - Class in elki.clustering.dbscan.predicates: Abstract local model neighborhood predicate.
AbstractRangeQueryNeighborPredicate(double, Distance<? super O>) - Constructor for class elki.clustering.dbscan.predicates.AbstractRangeQueryNeighborPredicate: Full constructor.
AbstractRangeQueryNeighborPredicate.Instance<N,M> - Class in elki.clustering.dbscan.predicates: Instance for a particular data set.
acceptsType(SimpleTypeInformation<? extends PreDeConNeighborPredicate.PreDeConModel>) - Method in class elki.clustering.dbscan.predicates.FourCCorePredicate
acceptsType(SimpleTypeInformation<? extends PreDeConNeighborPredicate.PreDeConModel>) - Method in class elki.clustering.dbscan.predicates.PreDeConCorePredicate
acceptsType(SimpleTypeInformation<? extends DBIDs>) - Method in class elki.clustering.dbscan.predicates.MinPtsCorePredicate
acceptsType(SimpleTypeInformation<? extends T>) - Method in interface elki.clustering.dbscan.predicates.CorePredicate: Test whether the neighborhood type T is accepted by this predicate.
accuracy - Variable in class elki.evaluation.clustering.MaximumMatchingAccuracy: Accuracy calculated with maximum matching
actualPairs - Variable in class elki.evaluation.clustering.pairsegments.Segments: Pairs actually present in the data set
add(int, double, int) - Method in class elki.clustering.hierarchical.ClusterMergeHistoryBuilder: A more robust "add" operation (involving a union-find) where we may use arbitrary objects i and j to refer to clusters, not only the largest ID in each cluster.
add(int, AsClusterFeature) - Method in class elki.index.tree.betula.CFNode: Add a subtree.
add(ClusteringFeature[], ClusteringFeature) - Method in class elki.clustering.hierarchical.birch.CFTree: Add a node to the first unused slot.
add(DBIDRef) - Method in class elki.clustering.hierarchical.extraction.SimplifiedHierarchyExtraction.TempCluster: Add new objects to the cluster.
add(DBIDRef, double, DBIDRef) - Method in class elki.clustering.optics.ClusterOrder: Add an object to the cluster order.
add(AsClusterFeature) - Method in class elki.index.tree.betula.CFNode: Add a subtree
addChild(Cluster<DendrogramModel>) - Method in class elki.clustering.hierarchical.extraction.SimplifiedHierarchyExtraction.TempCluster: Add a child cluster.
addChildCluster(Cluster<M>, Cluster<M>) - Method in class elki.data.Clustering: Add a cluster to the clustering.
addCluster(DBIDArrayIter, int, int) - Method in class elki.clustering.optics.OPTICSXi.ClusterHierarchyBuilder: Build a cluster object.
addDBIDs(DBIDs) - Method in class elki.clustering.hierarchical.extraction.SimplifiedHierarchyExtraction.TempCluster: Add new objects to the cluster.
addDenseUnit(CLIQUEUnit) - Method in class elki.clustering.subspace.clique.CLIQUESubspace: Adds the specified dense unit to this subspace.
addEdge(double, int, int) - Method in class elki.clustering.hierarchical.AbstractHDBSCAN.HeapMSTCollector
addFeatureVector(DBIDRef, NumberVector) - Method in class elki.clustering.subspace.clique.CLIQUEUnit: Adds the id of the specified feature vector to this unit, if this unit contains the feature vector.
addRecursive(int[], int, byte[], int, int) - Method in class elki.clustering.hierarchical.ClusterMergeHistoryBuilder: Recursively add merges (children first) to the order, to obtain a monotone ordering.
addSingleton(SimplifiedHierarchyExtraction.TempCluster, int, DBIDRef, double, boolean) - Method in class elki.clustering.hierarchical.extraction.SimplifiedHierarchyExtraction.Instance: Add a singleton object, as point or cluster.
addToplevelCluster(Cluster<M>) - Method in class elki.data.Clustering: Add a cluster to the clustering.
addToStatistics(ClusteringFeature) - Method in class elki.clustering.hierarchical.birch.ClusteringFeature: Merge an other clustering features.
addToStatistics(NumberVector) - Method in class elki.clustering.hierarchical.birch.ClusteringFeature: Add a number vector to the current node.
addToStatistics(NumberVector) - Method in class elki.index.tree.betula.features.BIRCHCF
addToStatistics(NumberVector) - Method in interface elki.index.tree.betula.features.ClusterFeature: Add NumberVector to CF
addToStatistics(NumberVector) - Method in class elki.index.tree.betula.features.VIIFeature
addToStatistics(NumberVector) - Method in class elki.index.tree.betula.features.VVIFeature
addToStatistics(NumberVector) - Method in class elki.index.tree.betula.features.VVVFeature
addToStatistics(BIRCHCF) - Method in class elki.index.tree.betula.features.BIRCHCF
addToStatistics(ClusterFeature) - Method in class elki.index.tree.betula.features.BIRCHCF
addToStatistics(ClusterFeature) - Method in interface elki.index.tree.betula.features.ClusterFeature: Add other CF to CF
addToStatistics(ClusterFeature) - Method in class elki.index.tree.betula.features.VIIFeature
addToStatistics(ClusterFeature) - Method in class elki.index.tree.betula.features.VVIFeature
addToStatistics(ClusterFeature) - Method in class elki.index.tree.betula.features.VVVFeature
addToStatistics(VIIFeature) - Method in class elki.index.tree.betula.features.VIIFeature
addToStatistics(VVIFeature) - Method in class elki.index.tree.betula.features.VVIFeature
addToStatistics(VVVFeature) - Method in class elki.index.tree.betula.features.VVVFeature
adjust(double[][], double[], int) - Method in class elki.clustering.correlation.HiCO: Inserts the specified vector into the given orthonormal matrix v at column corrDim.
adjustedArithmeticMI() - Method in class elki.evaluation.clustering.Entropy: Get the adjusted mutual information using the arithmetic version.
adjustedGeometricMI() - Method in class elki.evaluation.clustering.Entropy: Get the adjusted mutual information using the geometric version.
adjustedJointMI() - Method in class elki.evaluation.clustering.Entropy: Get the adjusted mutual information using the joint version.
adjustedMaxMI() - Method in class elki.evaluation.clustering.Entropy: Get the adjusted mutual information using the max version.
adjustedMinMI() - Method in class elki.evaluation.clustering.Entropy: Get the adjusted mutual information using the min version.
adjustedRandIndex() - Method in class elki.evaluation.clustering.PairCounting: Computes the adjusted Rand index (ARI).
adjustedSymmetricGini() - Method in class elki.evaluation.clustering.ClusterContingencyTable: Compute the adjusted average Gini for each cluster (in both clusterings - symmetric).
advance() - Method in class elki.clustering.hierarchical.birch.CFTree.LeafIterator
advance() - Method in class elki.index.tree.betula.CFTree.LeafIterator
AffinityPropagation<O> - Class in elki.clustering.affinitypropagation: Cluster analysis by affinity propagation.
AffinityPropagation(AffinityPropagationInitialization<O>, double, int, int) - Constructor for class elki.clustering.affinitypropagation.AffinityPropagation: Constructor.
AffinityPropagationInitialization<O> - Interface in elki.clustering.affinitypropagation: Initialization methods for affinity propagation.
AFKMC2 - Class in elki.clustering.kmeans.initialization: AFK-MC² initialization
AFKMC2(int, RandomFactory) - Constructor for class elki.clustering.kmeans.initialization.AFKMC2: Constructor.
AFKMC2.Instance - Class in elki.clustering.kmeans.initialization: Abstract instance implementing the weight handling.
AFKMC2.Par - Class in elki.clustering.kmeans.initialization: Parameterization class.
aggregate - Variable in class elki.clustering.hierarchical.extraction.HDBSCANHierarchyExtraction.TempCluster: Mass aggregate.
aggregateStats(Relation<? extends NumberVector>, DBIDArrayIter, int) - Method in class elki.clustering.em.KDTreeEM.KDTree: Aggregate the statistics for a leaf node.
AGNES<O> - Class in elki.clustering.hierarchical: Hierarchical Agglomerative Clustering (HAC) or Agglomerative Nesting (AGNES) is a classic hierarchical clustering algorithm.
AGNES(Distance<? super O>, Linkage) - Constructor for class elki.clustering.hierarchical.AGNES: Constructor.
AGNES.Instance - Class in elki.clustering.hierarchical: Main worker instance of AGNES.
AkaikeInformationCriterion - Class in elki.clustering.kmeans.quality: Akaike Information Criterion (AIC).
AkaikeInformationCriterion() - Constructor for class elki.clustering.kmeans.quality.AkaikeInformationCriterion
AkaikeInformationCriterionXMeans - Class in elki.clustering.kmeans.quality: Akaike Information Criterion (AIC).
AkaikeInformationCriterionXMeans() - Constructor for class elki.clustering.kmeans.quality.AkaikeInformationCriterionXMeans
algorithm - Variable in class elki.clustering.hierarchical.extraction.AbstractCutDendrogram: Clustering algorithm to run to obtain the hierarchy.
algorithm - Variable in class elki.clustering.hierarchical.extraction.AbstractCutDendrogram.Par: The hierarchical clustering algorithm to run.
algorithm - Variable in class elki.clustering.hierarchical.extraction.ClustersWithNoiseExtraction: Clustering algorithm to run to obtain the hierarchy.
algorithm - Variable in class elki.clustering.hierarchical.extraction.ClustersWithNoiseExtraction.Par: The hierarchical clustering algorithm to run.
algorithm - Variable in class elki.clustering.hierarchical.extraction.HDBSCANHierarchyExtraction: Clustering algorithm to run to obtain the hierarchy.
algorithm - Variable in class elki.clustering.hierarchical.extraction.HDBSCANHierarchyExtraction.Par: The hierarchical clustering algorithm to run.
algorithm - Variable in class elki.clustering.hierarchical.extraction.SimplifiedHierarchyExtraction: Clustering algorithm to run to obtain the hierarchy.
algorithm - Variable in class elki.clustering.hierarchical.extraction.SimplifiedHierarchyExtraction.Par: The hierarchical clustering algorithm to run.
ALL - Static variable in interface elki.clustering.biclustering.ChengAndChurch.CellVisitor: Different modes of operation.
allM - Variable in class elki.clustering.biclustering.ChengAndChurch.BiclusterCandidate: Mean of the current bicluster.
alpha - Variable in class elki.clustering.biclustering.ChengAndChurch: The parameter for multiple node deletion.
alpha - Variable in class elki.clustering.correlation.HiCO.Par: Alpha parameter
alpha - Variable in class elki.clustering.correlation.ORCLUS: Holds the value of ORCLUS.Par.ALPHA_ID.
alpha - Variable in class elki.clustering.correlation.ORCLUS.Par: Cluster reduction factor
alpha - Variable in class elki.clustering.dbscan.LSDBC: Alpha parameter.
alpha - Variable in class elki.clustering.dbscan.LSDBC.Par: Alpha parameter.
alpha - Variable in class elki.clustering.hierarchical.linkage.FlexibleBetaLinkage: Alpha parameter, derived from beta.
alpha - Variable in class elki.clustering.kmeans.initialization.SphericalAFKMC2: Parameter to balance distance vs. uniform sampling.
alpha - Variable in class elki.clustering.kmeans.initialization.SphericalAFKMC2.Instance: Parameter to balance distance vs. uniform sampling.
alpha - Variable in class elki.clustering.kmeans.initialization.SphericalAFKMC2.Par: Parameter to balance distance vs. uniform sampling.
alpha - Variable in class elki.clustering.kmeans.initialization.SphericalKMeansPlusPlus: Parameter to balance distance vs. uniform sampling.
alpha - Variable in class elki.clustering.kmeans.initialization.SphericalKMeansPlusPlus.Instance: Parameter to balance distance vs. uniform sampling.
alpha - Variable in class elki.clustering.kmeans.initialization.SphericalKMeansPlusPlus.Par: Parameter to balance distance vs. uniform sampling.
alpha - Variable in class elki.clustering.subspace.DOC: Relative density threshold parameter alpha.
alpha - Variable in class elki.clustering.subspace.DOC.Par: Relative density threshold parameter Alpha.
alpha - Variable in class elki.clustering.subspace.HiSC: Holds the maximum diversion allowed.
alpha - Variable in class elki.clustering.subspace.HiSC.Par: The maximum absolute variance along a coordinate axis.
alpha - Variable in class elki.clustering.subspace.P3C: Alpha threshold for testing.
alpha - Variable in class elki.clustering.subspace.P3C.Par: Parameter for the chi squared test threshold.
ALPHA_ID - Static variable in class elki.clustering.correlation.HiCO.Par: The threshold for 'strong' eigenvectors: the 'strong' eigenvectors explain a portion of at least alpha of the total variance.
ALPHA_ID - Static variable in class elki.clustering.correlation.ORCLUS.Par: Parameter to specify the factor for reducing the number of current clusters in each iteration, must be an integer greater than 0 and less than 1.
ALPHA_ID - Static variable in class elki.clustering.dbscan.LSDBC.Par: Parameter for the maximum density difference.
ALPHA_ID - Static variable in class elki.clustering.kmeans.initialization.SphericalAFKMC2.Par: Alpha parameter, usually 1.5
ALPHA_ID - Static variable in class elki.clustering.kmeans.initialization.SphericalKMeansPlusPlus.Par: Alpha parameter, usually 1.5
ALPHA_ID - Static variable in class elki.clustering.subspace.DOC.Par: Relative density threshold parameter Alpha.
ALPHA_ID - Static variable in class elki.clustering.subspace.HiSC.Par: The maximum absolute variance along a coordinate axis.
ALPHA_THRESHOLD_ID - Static variable in class elki.clustering.subspace.P3C.Par: Parameter for the chi squared test threshold.
AlternateRefinement<O> - Class in elki.clustering.kmedoids.initialization: Meta-Initialization for k-medoids by performing one (or many) k-means-style iteration.
AlternateRefinement(KMedoidsInitialization<O>, int) - Constructor for class elki.clustering.kmedoids.initialization.AlternateRefinement: Constructor.
AlternateRefinement.Par<O> - Class in elki.clustering.kmedoids.initialization: Parameterization class.
AlternatingKMedoids<O> - Class in elki.clustering.kmedoids: A k-medoids clustering algorithm, implemented as EM-style batch algorithm; known in literature as the "alternate" method.
AlternatingKMedoids(Distance<? super O>, int, int, KMedoidsInitialization<O>) - Constructor for class elki.clustering.kmedoids.AlternatingKMedoids: Constructor.
AlternatingKMedoids.Par<V> - Class in elki.clustering.kmedoids: Parameterization class.
an1 - Variable in class elki.clustering.kmeans.HartiganWongKMeans.Instance: Weights for adding/removing points from a cluster.
an2 - Variable in class elki.clustering.kmeans.HartiganWongKMeans.Instance: Weights for adding/removing points from a cluster.
analyseDimWidth(Relation<? extends NumberVector>) - Method in class elki.clustering.em.KDTreeEM: Helper method to retrieve the widths of all data in all dimensions.
Anderberg<O> - Class in elki.clustering.hierarchical: This is a modification of the classic AGNES algorithm for hierarchical clustering using a nearest-neighbor heuristic for acceleration.
Anderberg(Distance<? super O>, Linkage) - Constructor for class elki.clustering.hierarchical.Anderberg: Constructor.
Anderberg.Instance - Class in elki.clustering.hierarchical: Main worker instance of Anderberg's algorithm.
AnnulusKMeans<V extends elki.data.NumberVector> - Class in elki.clustering.kmeans: Annulus k-means algorithm.
AnnulusKMeans(NumberVectorDistance<? super V>, int, int, KMeansInitialization, boolean) - Constructor for class elki.clustering.kmeans.AnnulusKMeans: Constructor.
AnnulusKMeans.Instance - Class in elki.clustering.kmeans: Inner instance, storing state for a single data set.
AnnulusKMeans.Par<V extends elki.data.NumberVector> - Class in elki.clustering.kmeans: Parameterization class.
append - Variable in class elki.result.ClusteringVectorDumper: Always append to the output file.
append - Variable in class elki.result.ClusteringVectorDumper.Par: Always append to the output file.
APPEND_ID - Static variable in class elki.result.ClusteringVectorDumper.Par: Append flag.
approximatelyLinearDependent(PCAFilteredResult, PCAFilteredResult) - Method in class elki.clustering.dbscan.predicates.ERiCNeighborPredicate.Instance: Returns true, if the strong eigenvectors of the two specified PCAs span up the same space.
areas - Variable in class elki.clustering.optics.OPTICSXi.SteepAreaResult: Storage
arithmeticNMI() - Method in class elki.evaluation.clustering.Entropy: Get the arithmetic averaged normalized mutual information.
AsClusterFeature - Interface in elki.index.tree.betula.features: Get the clustering feature representation.
assign(Relation<? extends NumberVector>, List<ORCLUS.ORCLUSCluster>) - Method in class elki.clustering.correlation.ORCLUS: Creates a partitioning of the database by assigning each object to its closest seed.
assign(HashMap<String, DBIDs>, String, DBIDRef) - Method in class elki.clustering.trivial.ByLabelClustering: Assigns the specified id to the labelMap according to its label
assign(HashMap<String, DBIDs>, String, DBIDRef) - Method in class elki.clustering.trivial.ByLabelHierarchicalClustering: Assigns the specified id to the labelMap according to its label
assigned - Variable in class elki.clustering.subspace.clique.CLIQUEUnit: Flag that indicates if this unit is already assigned to a cluster.
assignment - Variable in class elki.clustering.kmeans.AbstractKMeans.Instance: A mapping of elements to cluster ids.
assignment - Variable in class elki.clustering.kmeans.parallel.KMeansProcessor: Assignment storage.
assignment - Variable in class elki.clustering.kmeans.parallel.KMeansProcessor.Instance: Cluster assignment storage.
assignment - Variable in class elki.clustering.kmedoids.CLARANS.Assignment: Cluster mapping.
assignment - Variable in class elki.clustering.kmedoids.PAM.Instance: Cluster mapping.
assignment - Variable in class elki.clustering.kmedoids.SingleAssignmentKMedoids.Instance: Cluster mapping.
assignment - Variable in class elki.clustering.silhouette.FastMSC.Instance: Distances and nearest medoids.
assignment - Variable in class elki.clustering.silhouette.FastMSC.Instance2: Output cluster mapping.
assignment - Variable in class elki.clustering.silhouette.PAMSIL.Instance: Cluster mapping.
Assignment - Interface in elki.clustering.dbscan.util: Point assignment.
Assignment(DistanceQuery<?>, DBIDs, int) - Constructor for class elki.clustering.kmedoids.CLARANS.Assignment: Constructor.
Assignment(DistanceQuery<?>, DBIDs, int) - Constructor for class elki.clustering.kmedoids.FastCLARANS.Assignment: Constructor.
assignPoints(ArrayDBIDs, long[][], Relation<? extends NumberVector>) - Method in class elki.clustering.subspace.PROCLUS: Assigns the objects to the clusters.
assignProbabilitiesToInstances(Relation<? extends NumberVector>, List<? extends BetulaClusterModel>, WritableDataStore<double[]>) - Method in class elki.clustering.em.BetulaGMM: Assigns the current probability values to the instances in the database and compute the expectation value of the current mixture of distributions.
assignProbabilitiesToInstances(Relation<? extends O>, List<? extends EMClusterModel<? super O, ?>>, WritableDataStore<double[]>, WritableDoubleDataStore) - Static method in class elki.clustering.em.EM: Assigns the current probability values to the instances in the database and compute the expectation value of the current mixture of distributions.
assignProbabilitiesToInstances(Relation<V>, double[][], WritableDataStore<double[]>) - Method in class elki.clustering.kmeans.FuzzyCMeans: Calculates the weights of all points and clusters.
assignProbabilitiesToInstances(ArrayList<? extends ClusterFeature>, List<? extends BetulaClusterModel>, Map<ClusterFeature, double[]>) - Method in class elki.clustering.em.BetulaGMM: Assigns the current probability values to the instances in the database and compute the expectation value of the current mixture of distributions.
assignProbabilitiesToInstances(ArrayList<? extends ClusterFeature>, List<? extends BetulaClusterModel>, Map<ClusterFeature, double[]>) - Method in class elki.clustering.em.BetulaGMMWeighted
assignRemainingToNearestCluster(ArrayDBIDs, DBIDs, DBIDs, WritableIntegerDataStore, DistanceQuery<?>) - Static method in class elki.clustering.kmedoids.CLARA: Returns a list of clusters.
assignToNearestCluster() - Method in class elki.clustering.kmeans.AbstractKMeans.Instance: Assign each object to the nearest cluster.
assignToNearestCluster() - Method in class elki.clustering.kmeans.AnnulusKMeans.Instance
assignToNearestCluster() - Method in class elki.clustering.kmeans.CompareMeans.Instance
assignToNearestCluster() - Method in class elki.clustering.kmeans.ElkanKMeans.Instance
assignToNearestCluster() - Method in class elki.clustering.kmeans.ExponionKMeans.Instance
assignToNearestCluster() - Method in class elki.clustering.kmeans.HamerlyKMeans.Instance
assignToNearestCluster() - Method in class elki.clustering.kmeans.KMeansMinusMinus.Instance: Returns a list of clusters.
assignToNearestCluster() - Method in class elki.clustering.kmeans.ShallotKMeans.Instance
assignToNearestCluster() - Method in class elki.clustering.kmeans.SimplifiedElkanKMeans.Instance
assignToNearestCluster() - Method in class elki.clustering.kmeans.SortMeans.Instance
assignToNearestCluster() - Method in class elki.clustering.kmeans.spherical.EuclideanSphericalElkanKMeans.Instance
assignToNearestCluster() - Method in class elki.clustering.kmeans.spherical.EuclideanSphericalHamerlyKMeans.Instance
assignToNearestCluster() - Method in class elki.clustering.kmeans.spherical.EuclideanSphericalSimplifiedElkanKMeans.Instance
assignToNearestCluster() - Method in class elki.clustering.kmeans.spherical.SphericalElkanKMeans.Instance
assignToNearestCluster() - Method in class elki.clustering.kmeans.spherical.SphericalHamerlyKMeans.Instance
assignToNearestCluster() - Method in class elki.clustering.kmeans.spherical.SphericalKMeans.Instance
assignToNearestCluster() - Method in class elki.clustering.kmeans.spherical.SphericalSimplifiedElkanKMeans.Instance
assignToNearestCluster() - Method in class elki.clustering.kmeans.spherical.SphericalSimplifiedHamerlyKMeans.Instance
assignToNearestCluster() - Method in class elki.clustering.kmeans.YinYangKMeans.Instance: Reassign objects, but avoid unnecessary computations based on their bounds.
assignToNearestCluster() - Method in class elki.clustering.kmedoids.CLARANS.Assignment: Assign each point to the nearest medoid.
assignToNearestCluster(int[], double[][], double[][], ClusteringFeature[], int[]) - Method in class elki.clustering.hierarchical.birch.BIRCHLloydKMeans: Assign each element to nearest cluster.
assignToNearestCluster(int[], double[][], ArrayList<? extends ClusterFeature>, int[]) - Method in class elki.clustering.kmeans.BetulaLloydKMeans: Assign each element to nearest cluster.
assignToNearestCluster(ArrayDBIDs) - Method in class elki.clustering.kmedoids.FastPAM1.Instance: Returns a list of clusters.
assignToNearestCluster(ArrayDBIDs) - Method in class elki.clustering.kmedoids.PAM.Instance: Assign each object to the nearest cluster, return the cost.
assignToNearestCluster(ArrayDBIDs) - Method in class elki.clustering.silhouette.FastMSC.Instance: Assign each object to the nearest cluster.
assignToNearestCluster(ArrayDBIDs) - Method in class elki.clustering.silhouette.FastMSC.Instance2: Assign each object to the nearest cluster.
assignToNearestCluster(ArrayDBIDs) - Method in class elki.clustering.silhouette.PAMSIL.Instance: Assign each object to the nearest cluster.
assignToNearestCluster(ArrayModifiableDBIDs) - Method in class elki.clustering.kmedoids.SingleAssignmentKMedoids.Instance: Assign each object to the nearest cluster, return the cost.
assignToNearestCluster(DBIDArrayIter, DBIDs, DistanceQuery<?>, WritableIntegerDataStore, double[]) - Static method in class elki.clustering.kmedoids.initialization.AlternateRefinement: Compute the initial cluster assignment.
assignUnassigned(Relation<? extends NumberVector>, WritableDataStore<double[]>, List<MultivariateGaussianModel>, ModifiableDBIDs) - Method in class elki.clustering.subspace.P3C: Assign unassigned objects to best candidate based on shortest Mahalanobis distance.
assignVar(int, DBIDVar) - Method in class elki.clustering.hierarchical.ClusterMergeHistory: Access the i'th singleton via a variable.
attachToRelation(Relation<?>, IntArrayList, ArrayList<String>) - Method in class elki.clustering.meta.ExternalClustering: Build a clustering from the file result.
autorun(Database) - Method in interface elki.clustering.ClusteringAlgorithm
autorun(Database) - Method in class elki.clustering.dbscan.GeneralizedDBSCAN
autorun(Database) - Method in class elki.clustering.dbscan.parallel.ParallelGeneralizedDBSCAN
autorun(Database) - Method in class elki.clustering.hierarchical.extraction.ClustersWithNoiseExtraction
autorun(Database) - Method in class elki.clustering.hierarchical.extraction.HDBSCANHierarchyExtraction
autorun(Database) - Method in class elki.clustering.hierarchical.extraction.SimplifiedHierarchyExtraction
autorun(Database) - Method in interface elki.clustering.hierarchical.HierarchicalClusteringAlgorithm
autorun(Database) - Method in class elki.clustering.hierarchical.OPTICSToHierarchical
autorun(Database) - Method in class elki.clustering.meta.ExternalClustering: Run the algorithm.
autorun(Database) - Method in interface elki.clustering.optics.OPTICSTypeAlgorithm
autorun(Database) - Method in class elki.clustering.optics.OPTICSXi
autorun(Database) - Method in class elki.clustering.trivial.ByLabelClustering
autorun(Database) - Method in class elki.clustering.trivial.ByLabelHierarchicalClustering
autorun(Database) - Method in class elki.clustering.trivial.ByLabelOrAllInOneClustering
AverageInterclusterDistance - Class in elki.clustering.hierarchical.birch: Average intercluster distance.
AverageInterclusterDistance - Class in elki.index.tree.betula.distance: Average intercluster distance.
AverageInterclusterDistance() - Constructor for class elki.clustering.hierarchical.birch.AverageInterclusterDistance
AverageInterclusterDistance() - Constructor for class elki.index.tree.betula.distance.AverageInterclusterDistance
AverageInterclusterDistance.Par - Class in elki.clustering.hierarchical.birch: Parameterization class.
AverageInterclusterDistance.Par - Class in elki.index.tree.betula.distance: Parameterization class.
AverageIntraclusterDistance - Class in elki.clustering.hierarchical.birch: Average intracluster distance.
AverageIntraclusterDistance - Class in elki.index.tree.betula.distance: Average intracluster distance.
AverageIntraclusterDistance() - Constructor for class elki.clustering.hierarchical.birch.AverageIntraclusterDistance
AverageIntraclusterDistance() - Constructor for class elki.index.tree.betula.distance.AverageIntraclusterDistance
AverageIntraclusterDistance.Par - Class in elki.clustering.hierarchical.birch: Parameterization class.
AverageIntraclusterDistance.Par - Class in elki.index.tree.betula.distance: Parameterization class.
averageSymmetricGini() - Method in class elki.evaluation.clustering.ClusterContingencyTable: Compute the average Gini for each cluster (in both clusterings - symmetric).
avgDistance(double[], DBIDs, Relation<? extends NumberVector>, int) - Method in class elki.clustering.subspace.PROCLUS: Computes the average distance of the objects to the centroid along the specified dimension.

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