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B

bad - Variable in class elki.clustering.kmedoids.CLARA.CachedDistanceQuery

Number of uncacheable queries

BALLOON - elki.clustering.onedimensional.KNNKernelDensityMinimaClustering.Mode

bandwidth - Variable in class elki.clustering.NaiveMeanShiftClustering

Range of the kernel.

basis - Variable in class elki.clustering.correlation.LMCLUS.Separation

Basis of manifold

basis - Variable in class elki.clustering.correlation.ORCLUS.ORCLUSCluster

The matrix defining the subspace of this cluster.

BayesianInformationCriterion - Class in elki.clustering.kmeans.quality

Bayesian Information Criterion (BIC), also known as Schwarz criterion (SBC, SBIC) for the use with evaluating k-means results.

BayesianInformationCriterion() - Constructor for class elki.clustering.kmeans.quality.BayesianInformationCriterion

BayesianInformationCriterionXMeans - Class in elki.clustering.kmeans.quality

Bayesian Information Criterion (BIC), also known as Schwarz criterion (SBC, SBIC) for the use with evaluating k-means results.

BayesianInformationCriterionXMeans() - Constructor for class elki.clustering.kmeans.quality.BayesianInformationCriterionXMeans

BayesianInformationCriterionZhao - Class in elki.clustering.kmeans.quality

Different version of the BIC criterion.

BayesianInformationCriterionZhao() - Constructor for class elki.clustering.kmeans.quality.BayesianInformationCriterionZhao

bcubed - Variable in class elki.evaluation.clustering.ClusterContingencyTable

BCubed measures

BCubed - Class in elki.evaluation.clustering

BCubed measures for cluster evaluation.

BCubed(ClusterContingencyTable) - Constructor for class elki.evaluation.clustering.BCubed

Constructor.

bCubedPrecision - Variable in class elki.evaluation.clustering.BCubed

Result cache

bCubedRecall - Variable in class elki.evaluation.clustering.BCubed

Result cache

beginEStep() - Method in class elki.clustering.em.models.DiagonalGaussianModel

beginEStep() - Method in interface elki.clustering.em.models.EMClusterModel

Begin the E step.

beginEStep() - Method in class elki.clustering.em.models.MultivariateGaussianModel

beginEStep() - Method in class elki.clustering.em.models.SphericalGaussianModel

beginEStep() - Method in class elki.clustering.em.models.TextbookMultivariateGaussianModel

beginEStep() - Method in class elki.clustering.em.models.TextbookSphericalGaussianModel

beginEStep() - Method in class elki.clustering.em.models.TwoPassMultivariateGaussianModel

bestd - Variable in class elki.clustering.hierarchical.Anderberg.Instance

Cache: best distance

bestd - Variable in class elki.clustering.hierarchical.MiniMaxAnderberg.Instance

Cache: best distance

besti - Variable in class elki.clustering.hierarchical.Anderberg.Instance

Cache: index of best distance

besti - Variable in class elki.clustering.hierarchical.MiniMaxAnderberg.Instance

Cache: index of best distance

BestOfMultipleKMeans<V extends elki.data.NumberVector,M extends MeanModel> - Class in elki.clustering.kmeans

Run K-Means multiple times, and keep the best run.

BestOfMultipleKMeans(int, KMeans<V, M>, KMeansQualityMeasure<? super V>) - Constructor for class elki.clustering.kmeans.BestOfMultipleKMeans

Constructor.

bestSubspace(List<Subspace>, Subspace, TreeMap<Subspace, List<Cluster<Model>>>) - Method in class elki.clustering.subspace.SUBCLU

Determines the d-dimensional subspace of the (d+1) -dimensional candidate with minimal number of objects in the cluster.

beta - Variable in class elki.clustering.hierarchical.linkage.FlexibleBetaLinkage

Beta parameter

beta - Variable in class elki.clustering.hierarchical.linkage.FlexibleBetaLinkage.Par

Beta parameter

beta - Variable in class elki.clustering.subspace.DOC

Balancing parameter for importance of points vs. dimensions

beta - Variable in class elki.clustering.subspace.DOC.Par

Balancing parameter for importance of points vs. dimensions

BETA_ID - Static variable in class elki.clustering.hierarchical.linkage.FlexibleBetaLinkage.Par

Lance-Williams flexible beta parameter.

BETA_ID - Static variable in class elki.clustering.subspace.DOC.Par

Balancing parameter for importance of points vs. dimensions

BetulaClusterModel - Interface in elki.clustering.em.models

Models usable in Betula EM clustering.

BetulaClusterModelFactory<M extends BetulaClusterModel> - Interface in elki.clustering.em.models

Factory for initializing the EM models.

BetulaDiagonalGaussianModelFactory - Class in elki.clustering.em.models

Factory for EM with multivariate gaussian models using diagonal matrixes.

BetulaDiagonalGaussianModelFactory(AbstractCFKMeansInitialization) - Constructor for class elki.clustering.em.models.BetulaDiagonalGaussianModelFactory

Constructor.

BetulaDiagonalGaussianModelFactory.Par - Class in elki.clustering.em.models

Parameterization class

BetulaGMM - Class in elki.clustering.em

Clustering by expectation maximization (EM-Algorithm), also known as Gaussian Mixture Modeling (GMM), with optional MAP regularization.

BetulaGMM(CFTree.Factory<?>, double, int, int, boolean, BetulaClusterModelFactory<?>, double) - Constructor for class elki.clustering.em.BetulaGMM

Constructor.

BetulaGMM.Par - Class in elki.clustering.em

Parameterizer

BetulaGMMWeighted - Class in elki.clustering.em

Clustering by expectation maximization (EM-Algorithm), also known as Gaussian Mixture Modeling (GMM), with optional MAP regularization.

BetulaGMMWeighted(CFTree.Factory<?>, double, int, int, boolean, BetulaClusterModelFactory<?>, double) - Constructor for class elki.clustering.em.BetulaGMMWeighted

Constructor.

BetulaGMMWeighted.Par - Class in elki.clustering.em

Parameterizer

BetulaLeafPreClustering - Class in elki.clustering

BETULA-based clustering algorithm that simply treats the leafs of the CFTree as clusters.

BetulaLeafPreClustering(CFTree.Factory<?>, boolean) - Constructor for class elki.clustering.BetulaLeafPreClustering

Constructor.

BetulaLeafPreClustering.Par - Class in elki.clustering

Parameterization class.

BetulaLloydKMeans - Class in elki.clustering.kmeans

BIRCH/BETULA-based clustering algorithm that simply treats the leafs of the CFTree as clusters.

BetulaLloydKMeans(int, int, CFTree.Factory<?>, AbstractCFKMeansInitialization, boolean, boolean) - Constructor for class elki.clustering.kmeans.BetulaLloydKMeans

Constructor.

BetulaLloydKMeans.Par - Class in elki.clustering.kmeans

Parameterization class.

BetulaMultivariateGaussianModelFactory - Class in elki.clustering.em.models

Factory for EM with multivariate gaussian models using diagonal matrixes.

BetulaMultivariateGaussianModelFactory(AbstractCFKMeansInitialization) - Constructor for class elki.clustering.em.models.BetulaMultivariateGaussianModelFactory

Constructor.

BetulaMultivariateGaussianModelFactory.Par - Class in elki.clustering.em.models

Parameterization class

BetulaSphericalGaussianModelFactory - Class in elki.clustering.em.models

Factory for EM with multivariate gaussian models using a single variance.

BetulaSphericalGaussianModelFactory(AbstractCFKMeansInitialization) - Constructor for class elki.clustering.em.models.BetulaSphericalGaussianModelFactory

Constructor.

BetulaSphericalGaussianModelFactory.Par - Class in elki.clustering.em.models

Parameterization class

BiclusterCandidate(int, int) - Constructor for class elki.clustering.biclustering.ChengAndChurch.BiclusterCandidate

Constructor.

biclustering() - Method in class elki.clustering.biclustering.AbstractBiclustering

Run the actual biclustering algorithm.

biclustering() - Method in class elki.clustering.biclustering.ChengAndChurch

BiclusterModel - Class in elki.data.model

Wrapper class to provide the basic properties of a Bicluster.

BiclusterModel(int[]) - Constructor for class elki.data.model.BiclusterModel

Defines a new Bicluster for given parameters.

BiclusterWithInversionsModel - Class in elki.data.model

This code was factored out of the Bicluster class, since not all biclusters have inverted rows.

BiclusterWithInversionsModel(int[], DBIDs) - Constructor for class elki.data.model.BiclusterWithInversionsModel

BINS - Static variable in class elki.clustering.correlation.LMCLUS

Histogram resolution

BIRCHAbsorptionCriterion - Interface in elki.clustering.hierarchical.birch

BIRCH absorption criterion.

BIRCHAverageInterclusterDistance - Class in elki.index.tree.betula.distance

Average intercluster distance.

BIRCHAverageInterclusterDistance() - Constructor for class elki.index.tree.betula.distance.BIRCHAverageInterclusterDistance

BIRCHAverageInterclusterDistance.Par - Class in elki.index.tree.betula.distance

Parameterization class.

BIRCHAverageIntraclusterDistance - Class in elki.index.tree.betula.distance

Average intracluster distance.

BIRCHAverageIntraclusterDistance() - Constructor for class elki.index.tree.betula.distance.BIRCHAverageIntraclusterDistance

BIRCHAverageIntraclusterDistance.Par - Class in elki.index.tree.betula.distance

Parameterization class.

BIRCHCF - Class in elki.index.tree.betula.features

Clustering Feature of BIRCH, only for comparison

BIRCHCF(int) - Constructor for class elki.index.tree.betula.features.BIRCHCF

Constructor.

BIRCHCF.Factory - Class in elki.index.tree.betula.features

Factory for making cluster features.

BIRCHCF.Factory.Par - Class in elki.index.tree.betula.features

Parameterization class.

BIRCHDistance - Interface in elki.clustering.hierarchical.birch

Distance function for BIRCH clustering.

BIRCHKMeansPlusPlus - Class in elki.clustering.hierarchical.birch

K-Means++-like initialization for BIRCH k-means; this cannot be used to initialize regular k-means, use KMeansPlusPlus instead.

BIRCHKMeansPlusPlus(RandomFactory) - Constructor for class elki.clustering.hierarchical.birch.BIRCHKMeansPlusPlus

Constructor.

BIRCHKMeansPlusPlus.Par - Class in elki.clustering.hierarchical.birch

Parameterization class.

BIRCHLeafClustering - Class in elki.clustering.hierarchical.birch

BIRCH-based clustering algorithm that simply treats the leafs of the CFTree as clusters.

BIRCHLeafClustering(CFTree.Factory) - Constructor for class elki.clustering.hierarchical.birch.BIRCHLeafClustering

Constructor.

BIRCHLeafClustering.Par - Class in elki.clustering.hierarchical.birch

Parameterization class.

BIRCHLloydKMeans - Class in elki.clustering.hierarchical.birch

BIRCH-based clustering algorithm that simply treats the leafs of the CFTree as clusters.

BIRCHLloydKMeans(CFTree.Factory, int, int, BIRCHKMeansPlusPlus) - Constructor for class elki.clustering.hierarchical.birch.BIRCHLloydKMeans

Constructor.

BIRCHLloydKMeans.Par - Class in elki.clustering.hierarchical.birch

Parameterization class.

BIRCHRadiusDistance - Class in elki.index.tree.betula.distance

Average Radius (R) criterion.

BIRCHRadiusDistance() - Constructor for class elki.index.tree.betula.distance.BIRCHRadiusDistance

BIRCHRadiusDistance.Par - Class in elki.index.tree.betula.distance

Parameterization class

BIRCHVarianceIncreaseDistance - Class in elki.index.tree.betula.distance

Variance increase distance.

BIRCHVarianceIncreaseDistance() - Constructor for class elki.index.tree.betula.distance.BIRCHVarianceIncreaseDistance

BIRCHVarianceIncreaseDistance.Par - Class in elki.index.tree.betula.distance

Parameterization class.

BisectingKMeans<V extends elki.data.NumberVector,M extends MeanModel> - Class in elki.clustering.kmeans

The bisecting k-means algorithm works by starting with an initial partitioning into two clusters, then repeated splitting of the largest cluster to get additional clusters.

BisectingKMeans(int, KMeans<V, M>) - Constructor for class elki.clustering.kmeans.BisectingKMeans

Constructor.

Border - Class in elki.clustering.dbscan.util

Border point assignment.

Border(Core) - Constructor for class elki.clustering.dbscan.util.Border

Constructor.

borders - Variable in class elki.clustering.dbscan.GriDBSCAN.Instance

Border identifier objects (shared to conserve memory).

borders - Variable in class elki.clustering.dbscan.parallel.ParallelGeneralizedDBSCAN.Instance

Border objects (shared)

BOUNDED_MIDPOINT - elki.clustering.kmeans.KDTreePruningKMeans.Split

Prefer a split halfway between minimum and maximum, but bound the unbalancedness by a ratio of 1:3.

bounds - Variable in class elki.clustering.subspace.clique.CLIQUEUnit

The bounding values (min, max) for each dimension.

BRANCHING_ID - Static variable in class elki.clustering.hierarchical.birch.CFTree.Factory.Par

Branching factor.

BRANCHING_ID - Static variable in class elki.index.tree.betula.CFTree.Factory.Par

Branching factor.

branchingFactor - Variable in class elki.clustering.hierarchical.birch.CFTree.Factory

Maximum branching factor of CFTree.

branchingFactor - Variable in class elki.clustering.hierarchical.birch.CFTree.Factory.Par

Maximum branching factor of CFTree.

branchingFactor - Variable in class elki.index.tree.betula.CFTree.Factory

Maximum branching factor of CFTree.

branchingFactor - Variable in class elki.index.tree.betula.CFTree.Factory.Par

Maximum branching factor of CFTree.

breakNoiseClusters - Variable in class elki.evaluation.clustering.ClusterContingencyTable

Noise cluster handling

buf1 - Variable in class elki.datasource.parser.ClusteringVectorParser

Buffers, will be reused.

build(ClusterOrder, DBIDArrayIter) - Method in class elki.clustering.optics.OPTICSXi.ClusterHierarchyBuilder

Build the main clustering result.

BUILD<O> - Class in elki.clustering.kmedoids.initialization

PAM initialization for k-means (and of course, for PAM).

BUILD() - Constructor for class elki.clustering.kmedoids.initialization.BUILD

Constructor.

BUILD.Par<V> - Class in elki.clustering.kmedoids.initialization

Parameterization class.

builder - Variable in class elki.clustering.hierarchical.AGNES.Instance

Cluster result builder

buildFlat(int, FiniteProgress) - Method in class elki.clustering.hierarchical.extraction.AbstractCutDendrogram.Instance

Build a flat clustering.

buildGrid(Relation<V>, int, double[]) - Method in class elki.clustering.dbscan.GriDBSCAN.Instance

Build the data grid.

buildHierarchical(int, FiniteProgress) - Method in class elki.clustering.hierarchical.extraction.AbstractCutDendrogram.Instance

Build a hierarchical clustering.

buildHierarchy(Clustering<CorrelationModel>, List<List<Cluster<CorrelationModel>>>, ERiCNeighborPredicate.Instance) - Method in class elki.clustering.correlation.ERiC

buildInitialModels(Relation<? extends NumberVector>, int) - Method in class elki.clustering.em.models.DiagonalGaussianModelFactory

buildInitialModels(Relation<? extends NumberVector>, int) - Method in class elki.clustering.em.models.MultivariateGaussianModelFactory

buildInitialModels(Relation<? extends NumberVector>, int) - Method in class elki.clustering.em.models.SphericalGaussianModelFactory

buildInitialModels(Relation<? extends NumberVector>, int) - Method in class elki.clustering.em.models.TextbookMultivariateGaussianModelFactory

buildInitialModels(Relation<? extends NumberVector>, int) - Method in class elki.clustering.em.models.TextbookSphericalGaussianModelFactory

buildInitialModels(Relation<? extends NumberVector>, int) - Method in class elki.clustering.em.models.TwoPassMultivariateGaussianModelFactory

buildInitialModels(Relation<? extends O>, int) - Method in interface elki.clustering.em.models.EMClusterModelFactory

Build the initial models

buildInitialModels(List<? extends ClusterFeature>, int, CFTree<?>) - Method in interface elki.clustering.em.models.BetulaClusterModelFactory

Build the initial models.

buildInitialModels(List<? extends ClusterFeature>, int, CFTree<?>) - Method in class elki.clustering.em.models.BetulaDiagonalGaussianModelFactory

buildInitialModels(List<? extends ClusterFeature>, int, CFTree<?>) - Method in class elki.clustering.em.models.BetulaMultivariateGaussianModelFactory

buildInitialModels(List<? extends ClusterFeature>, int, CFTree<?>) - Method in class elki.clustering.em.models.BetulaSphericalGaussianModelFactory

buildLeafClusters(int, FiniteProgress) - Method in class elki.clustering.hierarchical.extraction.AbstractCutDendrogram.Instance

Prepare the leaf clusters by executing the first (size - 1 - split) merges.

buildMediansResult() - Method in class elki.clustering.kmeans.KMediansLloyd.Instance

buildResult() - Method in class elki.clustering.correlation.HiCO.Instance

buildResult() - Method in class elki.clustering.kmeans.AbstractKMeans.Instance

Build a standard k-means result, with known cluster variance sums.

buildResult() - Method in class elki.clustering.optics.GeneralizedOPTICS.Instance

Build the final result.

buildResult() - Method in class elki.clustering.subspace.HiSC.Instance

buildResult(boolean, Relation<? extends NumberVector>) - Method in class elki.clustering.kmeans.AbstractKMeans.Instance

Build the result, recomputing the cluster variance if varstat is set to true.

buildResult(ArrayDBIDs, int[]) - Method in class elki.clustering.affinitypropagation.AffinityPropagation

Build the clustering result.

buildResult(DBIDs, int) - Method in class elki.clustering.dbscan.GriDBSCAN.Instance

Assemble the clustering result.

buildResultWithNoise() - Method in class elki.clustering.kmeans.KMeansMinusMinus.Instance

buildTreeBoundedMidpoint(Relation<? extends NumberVector>, int, int, VectorUtil.SortDBIDsBySingleDimension) - Method in class elki.clustering.kmeans.KDTreePruningKMeans.Instance

Build the k-d-tree using bounded midpoint splitting.

buildTreeMedian(Relation<? extends NumberVector>, int, int, VectorUtil.SortDBIDsBySingleDimension) - Method in class elki.clustering.kmeans.KDTreePruningKMeans.Instance

Build the k-d-tree using median splitting.

buildTreeMidpoint(Relation<? extends NumberVector>, int, int) - Method in class elki.clustering.kmeans.KDTreePruningKMeans.Instance

Build the k-d-tree using midpoint splitting.

buildTreeSSQ(Relation<? extends NumberVector>, int, int, VectorUtil.SortDBIDsBySingleDimension) - Method in class elki.clustering.kmeans.KDTreePruningKMeans.Instance

Build the k-d-tree using a variance-based splitting strategy.

BY_COVERAGE - Static variable in class elki.clustering.subspace.clique.CLIQUESubspace

A partial comparator for CLIQUESubspaces based on their coverage.

BY_NAME_SORTER - Static variable in class elki.data.Cluster

A partial comparator for Clusters, based on their name.

ByLabelClustering - Class in elki.clustering.trivial

Pseudo clustering using labels.

ByLabelClustering() - Constructor for class elki.clustering.trivial.ByLabelClustering

Constructor without parameters

ByLabelClustering(boolean, Pattern) - Constructor for class elki.clustering.trivial.ByLabelClustering

Constructor.

ByLabelClustering.Par - Class in elki.clustering.trivial

Parameterization class.

ByLabelHierarchicalClustering - Class in elki.clustering.trivial

Pseudo clustering using labels.

ByLabelHierarchicalClustering() - Constructor for class elki.clustering.trivial.ByLabelHierarchicalClustering

Constructor without parameters

ByLabelOrAllInOneClustering - Class in elki.clustering.trivial

Trivial class that will try to cluster by label, and fall back to an "all-in-one" clustering.

ByLabelOrAllInOneClustering() - Constructor for class elki.clustering.trivial.ByLabelOrAllInOneClustering

Constructor.

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