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

D

d_zero - Variable in class elki.clustering.subspace.FastDOC

Holds the value of FastDOC.Par.D_ZERO_ID.

d_zero - Variable in class elki.clustering.subspace.FastDOC.Par

Stopping threshold for FastDOC.

D_ZERO_ID - Static variable in class elki.clustering.subspace.FastDOC.Par

Stopping threshold for FastDOC.

d1 - Variable in class elki.clustering.silhouette.FastMSC.Record

Distance to nearest medoid

d2 - Variable in class elki.clustering.silhouette.FastMSC.Record

Distance to second nearest medoid

d3 - Variable in class elki.clustering.silhouette.FastMSC.Record

Distance to third nearest medoid

data(int) - Method in class elki.datasource.parser.ClusteringVectorParser

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

Database for cloning neighbor predicates.

DaviesBouldinIndex - Class in elki.evaluation.clustering.internal

Compute the Davies-Bouldin index of a data set.

DaviesBouldinIndex(NumberVectorDistance<?>, NoiseHandling, double) - Constructor for class elki.evaluation.clustering.internal.DaviesBouldinIndex

Constructor.

DaviesBouldinIndex.Par - Class in elki.evaluation.clustering.internal

Parameterization class.

DBCV<O> - Class in elki.evaluation.clustering.internal

Compute the Density-Based Clustering Validation Index.

DBCV(Distance<? super O>) - Constructor for class elki.evaluation.clustering.internal.DBCV

Constructor.

DBCV.Par<O> - Class in elki.evaluation.clustering.internal

Parameterization class.

DBSCAN<O> - Class in elki.clustering.dbscan

Density-Based Clustering of Applications with Noise (DBSCAN), an algorithm to find density-connected sets in a database.

DBSCAN(Distance<? super O>, double, int) - Constructor for class elki.clustering.dbscan.DBSCAN

Constructor with parameters.

DBSCAN.Instance - Class in elki.clustering.dbscan

Instance for a single data set.

DBSCAN.Par<O> - Class in elki.clustering.dbscan

Parameterization class.

dc - Variable in class elki.clustering.CFSFDP

Distance cutoff.

dc - Variable in class elki.clustering.CFSFDP.Par

Distance cutoff.

DC_ID - Static variable in class elki.clustering.CFSFDP.Par

Distance cutoff parameter.

DEFAULT_ALPHA - Static variable in class elki.clustering.correlation.HiCO

The default value for HiCO.Par.ALPHA_ID.

DEFAULT_ALPHA - Static variable in class elki.clustering.subspace.HiSC.Par

The default value for alpha.

DEFAULT_DELTA - Static variable in class elki.clustering.correlation.FourC.Settings.Par

The default value for delta.

DEFAULT_DELTA - Static variable in class elki.clustering.correlation.HiCO

The default value for HiCO.Par.DELTA_ID.

defaultInitializer() - Method in class elki.clustering.kmedoids.FastPAM.Par

defaultInitializer() - Method in class elki.clustering.kmedoids.PAM.Par

Default initialization method.

defaultInitializer() - Method in class elki.clustering.silhouette.FastMSC.Par

defaultInitializer() - Method in class elki.clustering.silhouette.PAMSIL.Par

defaultRate() - Method in class elki.clustering.kmedoids.CLARANS.Par

Default sampling rate.

defaultRate() - Method in class elki.clustering.kmedoids.FastCLARANS.Par

defineBicluster(long[], long[]) - Method in class elki.clustering.biclustering.AbstractBiclustering

Defines a Bicluster as given by the included rows and columns.

defineBicluster(BitSet, BitSet) - Method in class elki.clustering.biclustering.AbstractBiclustering

Defines a Bicluster as given by the included rows and columns.

delta - Variable in class elki.clustering.biclustering.ChengAndChurch

Threshold for the score.

delta - Variable in class elki.clustering.correlation.ERiC.Settings

Parameter to specify the threshold for approximate linear dependency: the strong eigenvectors of q are approximately linear dependent from the strong eigenvectors p if the following condition holds for all strong eigenvectors q_i of q (lambda_q < lambda_p): q_i' * M^check_p * q_i <= delta^2, must be a double equal to or greater than 0.

delta - Variable in class elki.clustering.correlation.FourC.Settings

Delta parameter, for selecting strong Eigenvectors.

delta - Variable in class elki.clustering.correlation.HiCO.Par

Delta parameter

delta - Variable in class elki.clustering.em.BetulaGMM

Delta parameter

delta - Variable in class elki.clustering.em.BetulaGMM.Par

Stopping threshold

delta - Variable in class elki.clustering.em.EM

Delta parameter

delta - Variable in class elki.clustering.em.EM.Par

Stopping threshold

delta - Variable in class elki.clustering.em.KDTreeEM

Delta parameter

delta - Variable in class elki.clustering.em.KDTreeEM.Par

Stopping threshold

delta - Variable in class elki.clustering.kmeans.FuzzyCMeans

Delta parameter

delta - Variable in class elki.clustering.kmeans.FuzzyCMeans.Par

Stopping threshold

delta - Variable in class elki.clustering.subspace.PreDeCon.Settings

The threshold for small eigenvalues.

DELTA_ID - Static variable in class elki.clustering.correlation.ERiC.Par

Parameter to specify the threshold for approximate linear dependency: the strong eigenvectors of q are approximately linear dependent from the strong eigenvectors p if the following condition holds for all strong eigenvectors q_i of q (lambda_q < lambda_p): q_i' * M^check_p * q_i <= delta^2, must be a double equal to or greater than 0.

DELTA_ID - Static variable in class elki.clustering.correlation.HiCO.Par

Parameter to specify the threshold of a distance between a vector q and a given space that indicates that q adds a new dimension to the space, must be a double equal to or greater than 0.

DELTA_ID - Static variable in class elki.clustering.em.BetulaGMM.Par

Parameter to specify the termination criterion for maximization of E(M): E(M) - E(M') < em.delta, must be a double equal to or greater than 0.

DELTA_ID - Static variable in class elki.clustering.em.EM.Par

Parameter to specify the termination criterion for maximization of E(M): E(M) - E(M') < em.delta, must be a double equal to or greater than 0.

DELTA_ID - Static variable in class elki.clustering.em.KDTreeEM.Par

Parameter to specify the termination criterion for maximization of E(M): E(M) - E(M') < em.delta, must be a double equal to or greater than 0.

DELTA_ID - Static variable in class elki.clustering.kmeans.FuzzyCMeans.Par

Parameter to specify the termination criterion for maximization of E(M): E(M) - E(M') < fcm.delta, must be a double equal to or greater than 0.

DELTA_ID - Static variable in class elki.clustering.subspace.PreDeCon.Settings.Par

Parameter Delta: maximum variance allowed

deltasq - Variable in class elki.clustering.correlation.HiCO

Delta parameter

deltasq - Variable in class elki.clustering.dbscan.predicates.ERiCNeighborPredicate

Squared delta value.

DendrogramModel - Class in elki.data.model

Model for dendrograms, provides the height of this subtree.

DendrogramModel(double) - Constructor for class elki.data.model.DendrogramModel

Constructor.

denseMeans(List<? extends DBIDs>, double[][], Relation<? extends NumberVector>) - Static method in class elki.clustering.kmeans.AbstractKMeans

Returns the mean vectors of the given clusters in the given database.

densePlusEquals(double[], NumberVector) - Static method in class elki.clustering.kmeans.AbstractKMeans

Similar to VMath.plusEquals, but accepts a number vector.

densePlusMinusEquals(double[], double[], NumberVector) - Static method in class elki.clustering.kmeans.AbstractKMeans

Add to one, remove from another.

denseUnits - Variable in class elki.clustering.subspace.clique.CLIQUESubspace

The dense units belonging to this subspace.

depth - Variable in class elki.clustering.hierarchical.extraction.SimplifiedHierarchyExtraction.TempCluster

Current height.

depth - Variable in class elki.clustering.kmeans.initialization.betula.CFKPlusPlusTree.Par

Maximum depth to choose from.

DEPTH_ID - Static variable in class elki.clustering.kmeans.initialization.betula.CFKPlusPlusTree.Par

Depth of the trunk based stragegy.

determineClusters() - Method in class elki.clustering.subspace.clique.CLIQUESubspace

Determines all clusters in this subspace by performing a depth-first search algorithm to find connected dense units.

determineClusters(List<CLIQUESubspace>) - Method in class elki.clustering.subspace.CLIQUE

Determines the clusters in the specified dense subspaces.

determinePreferenceVector(DBIDRef, DBIDs) - Method in class elki.clustering.subspace.HiSC.Instance

Determines the preference vector according to the specified neighbor ids.

deviation(double[], double[][]) - Method in class elki.clustering.correlation.LMCLUS

Deviation from a manifold described by beta.

df - Variable in class elki.clustering.kmeans.AbstractKMeans.Instance

Distance function.

dfs(CLIQUEUnit, ModifiableDBIDs, CLIQUESubspace) - Method in class elki.clustering.subspace.clique.CLIQUESubspace

Depth-first search algorithm to find connected dense units in this subspace that build a cluster.

DiagonalGaussianModel - Class in elki.clustering.em.models

Simpler model for a single Gaussian cluster, without covariances.

DiagonalGaussianModel(double, double[]) - Constructor for class elki.clustering.em.models.DiagonalGaussianModel

Constructor.

DiagonalGaussianModel(double, double[], double[]) - Constructor for class elki.clustering.em.models.DiagonalGaussianModel

Constructor.

DiagonalGaussianModelFactory - Class in elki.clustering.em.models

Factory for EM with multivariate gaussian models using diagonal matrixes.

DiagonalGaussianModelFactory(KMeansInitialization) - Constructor for class elki.clustering.em.models.DiagonalGaussianModelFactory

Constructor.

DiameterCriterion - Class in elki.clustering.hierarchical.birch

Average Radius (R) criterion.

DiameterCriterion() - Constructor for class elki.clustering.hierarchical.birch.DiameterCriterion

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

Parameterization class

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

Dimensionality.

dim - Variable in class elki.clustering.onedimensional.KNNKernelDensityMinimaClustering

Dimension to use for clustering.

dim - Variable in class elki.clustering.onedimensional.KNNKernelDensityMinimaClustering.Par

Dimension to use for clustering.

DIM_ID - Static variable in class elki.clustering.onedimensional.KNNKernelDensityMinimaClustering.Par

Dimension to use for clustering.

dimension - Variable in class elki.data.model.DimensionModel

Number of dimensions

DIMENSION_COMPARATOR - Static variable in class elki.data.Subspace

A comparator for subspaces based on their involved dimensions.

dimensionality - Variable in class elki.data.Subspace

The dimensionality of this subspace.

dimensionality() - Method in class elki.clustering.subspace.clique.CLIQUEUnit

Get the dimensionality of this unit.

dimensionality() - Method in class elki.data.Subspace

Returns the dimensionality of this subspace.

dimensionality(DBIDRef) - Method in class elki.clustering.dbscan.predicates.COPACNeighborPredicate.Instance

Get the correlation dimensionality of a single object.

dimensionality(DBIDRef) - Method in class elki.clustering.dbscan.predicates.ERiCNeighborPredicate.Instance

Get the correlation dimensionality of a single object.

dimensionIsRelevant(int, Relation<? extends NumberVector>, DBIDs) - Method in class elki.clustering.subspace.DOC

Utility method to test if a given dimension is relevant as determined via a set of reference points (i.e. if the variance along the attribute is lower than the threshold).

DimensionModel - Class in elki.data.model

Cluster model additionally providing a cluster dimensionality.

DimensionModel(int) - Constructor for class elki.data.model.DimensionModel

Constructor

dimensions - Variable in class elki.clustering.subspace.P3C.ClusterCandidate

Selected dimensions

dimensions - Variable in class elki.clustering.subspace.PROCLUS.PROCLUSCluster

The correlated dimensions of this cluster.

dimensions - Variable in class elki.data.Subspace

The dimensions building this subspace.

dimensionsToString() - Method in class elki.data.Subspace

Returns a string representation of the dimensions of this subspace separated by comma.

dimensonsToString(String) - Method in class elki.data.Subspace

Returns a string representation of the dimensions of this subspace.

dimi - Variable in class elki.clustering.subspace.PROCLUS.DoubleIntInt

dimj - Variable in class elki.clustering.subspace.PROCLUS.DoubleIntInt

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

The dimensions involved in this subspace.

dist - Variable in class elki.clustering.biclustering.ChengAndChurch

Distribution to sample random replacement values from.

dist - Variable in class elki.clustering.hierarchical.extraction.HDBSCANHierarchyExtraction.TempCluster

Current height.

dist - Variable in class elki.clustering.kmeans.initialization.betula.CFKPlusPlusLeaves.Par

Distance function to use for initial means

dist - Variable in class elki.clustering.kmeans.initialization.betula.CFKPlusPlusTree

Distance function to use for initial means

dist - Variable in class elki.clustering.kmeans.initialization.betula.CFKPlusPlusTree.Par

Distance function to use for initial means

dist - Variable in class elki.index.tree.betula.CFTree

Cluster distance

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

BIRCH distance function to use

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

BIRCH distance function to use

distance - Variable in class elki.clustering.affinitypropagation.DistanceBasedInitializationWithMedian

Distance function.

distance - Variable in class elki.clustering.CanopyPreClustering

Distance function used.

distance - Variable in class elki.clustering.CFSFDP

Distance function used.

distance - Variable in class elki.clustering.CFSFDP.Par

The distance function to use.

distance - Variable in class elki.clustering.dbscan.DBSCAN

Distance function used.

distance - Variable in class elki.clustering.dbscan.DBSCAN.Par

The distance function to use.

distance - Variable in class elki.clustering.dbscan.GriDBSCAN

Distance function used.

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

Distance function used.

distance - Variable in class elki.clustering.dbscan.LSDBC

Distance function used.

distance - Variable in class elki.clustering.dbscan.LSDBC.Par

The distance function to use.

distance - Variable in class elki.clustering.dbscan.predicates.AbstractRangeQueryNeighborPredicate

Distance function to use.

distance - Variable in class elki.clustering.dbscan.predicates.EpsilonNeighborPredicate

Distance function to use

distance - Variable in class elki.clustering.hierarchical.AbstractHDBSCAN

Distance function used.

distance - Variable in class elki.clustering.hierarchical.AGNES

Distance function used.

distance - Variable in class elki.clustering.hierarchical.birch.CFTree

Distance function to use.

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

BIRCH distance function to use

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

BIRCH distance function to use

distance - Variable in class elki.clustering.hierarchical.CLINK.Par

The distance function to use.

distance - Variable in class elki.clustering.hierarchical.HACAM

Distance to use

distance - Variable in class elki.clustering.hierarchical.MedoidLinkage

The distance function to use.

distance - Variable in class elki.clustering.hierarchical.MiniMax

Distance function used.

distance - Variable in class elki.clustering.hierarchical.SLINK

Distance function used.

distance - Variable in class elki.clustering.hierarchical.SLINK.Par

The distance function to use.

distance - Variable in class elki.clustering.kcenter.GreedyKCenter

Distance function

distance - Variable in class elki.clustering.kcenter.GreedyKCenter.Par

Distance function to use

distance - Variable in class elki.clustering.kmeans.AbstractKMeans

Distance function used.

distance - Variable in class elki.clustering.kmeans.AbstractKMeans.Par

The distance function to use.

distance - Variable in class elki.clustering.kmeans.initialization.betula.CFKPlusPlusLeaves

Distance function

distance - Variable in class elki.clustering.kmeans.initialization.KMC2.Instance

Distance function

distance - Variable in class elki.clustering.kmeans.initialization.KMeansPlusPlus.NumberVectorInstance

Distance function

distance - Variable in class elki.clustering.kmeans.parallel.KMeansProcessor

Distance function.

distance - Variable in class elki.clustering.kmeans.parallel.KMeansProcessor.Instance

Distance function.

distance - Variable in class elki.clustering.kmedoids.AlternatingKMedoids

Distance function used.

distance - Variable in class elki.clustering.kmedoids.AlternatingKMedoids.Par

The distance function to use.

distance - Variable in class elki.clustering.kmedoids.CLARANS

Distance function used.

distance - Variable in class elki.clustering.kmedoids.CLARANS.Par

The distance function to use.

distance - Variable in class elki.clustering.kmedoids.PAM

Distance function used.

distance - Variable in class elki.clustering.kmedoids.PAM.Par

The distance function to use.

distance - Variable in class elki.clustering.Leader

Distance function used.

distance - Variable in class elki.clustering.NaiveMeanShiftClustering

Distance function used.

distance - Variable in class elki.clustering.optics.AbstractOPTICS

Distance function used.

distance - Variable in class elki.clustering.subspace.SUBCLU

The distance function to determine the distance between objects.

distance - Variable in class elki.clustering.subspace.SUBCLU.Par

The distance function to determine the distance between objects.

distance - Variable in class elki.evaluation.clustering.internal.CIndex

Distance function to use.

distance - Variable in class elki.evaluation.clustering.internal.CIndex.Par

Distance function to use.

distance - Variable in class elki.evaluation.clustering.internal.ClusterRadius

Distance function to use.

distance - Variable in class elki.evaluation.clustering.internal.ClusterRadius.Par

Distance function to use.

distance - Variable in class elki.evaluation.clustering.internal.ConcordantPairsGammaTau

Distance function to use.

distance - Variable in class elki.evaluation.clustering.internal.ConcordantPairsGammaTau.Par

Distance function to use.

distance - Variable in class elki.evaluation.clustering.internal.DaviesBouldinIndex

Distance function to use.

distance - Variable in class elki.evaluation.clustering.internal.DaviesBouldinIndex.Par

Distance function to use.

distance - Variable in class elki.evaluation.clustering.internal.DBCV

Distance function to use.

distance - Variable in class elki.evaluation.clustering.internal.DBCV.Par

Distance function to use.

distance - Variable in class elki.evaluation.clustering.internal.PBMIndex

Distance function to use.

distance - Variable in class elki.evaluation.clustering.internal.PBMIndex.Par

Distance function to use.

distance - Variable in class elki.evaluation.clustering.internal.Silhouette

Distance function to use.

distance - Variable in class elki.evaluation.clustering.internal.Silhouette.Par

Distance function to use.

distance - Variable in class elki.evaluation.clustering.internal.SimplifiedSilhouette

Distance function to use.

distance - Variable in class elki.evaluation.clustering.internal.SimplifiedSilhouette.Par

Distance function to use.

distance - Variable in class elki.evaluation.clustering.internal.SquaredErrors

Distance function to use.

distance - Variable in class elki.evaluation.clustering.internal.SquaredErrors.Par

Distance function to use.

distance(double[], double[]) - Method in class elki.clustering.hierarchical.birch.BIRCHKMeansPlusPlus

Calculates distance between two vectors.

distance(double[], double[]) - Method in class elki.clustering.hierarchical.birch.BIRCHLloydKMeans

Compute a distance (and count the distance computations).

distance(double[], double[]) - Method in class elki.clustering.kmeans.AbstractKMeans.Instance

Compute the squared distance (and count the distance computations).

distance(double[], double[]) - Method in class elki.clustering.kmeans.BetulaLloydKMeans

Updates statistics and calculates distance between two Objects based on selected criteria.

distance(double[], double[]) - Method in class elki.clustering.kmeans.FuzzyCMeans

Distance computation.

distance(double[], double[]) - Method in class elki.clustering.kmeans.spherical.SphericalKMeans.Instance

distance(double[], int, double[], int) - Method in class elki.clustering.hierarchical.linkage.CentroidLinkage

distance(double[], int, double[], int) - Method in interface elki.clustering.hierarchical.linkage.GeometricLinkage

Distance of two aggregated clusters.

distance(double[], int, double[], int) - Method in class elki.clustering.hierarchical.linkage.MedianLinkage

distance(double[], int, double[], int) - Method in class elki.clustering.hierarchical.linkage.WardLinkage

distance(Cluster<?>, Cluster<?>) - Method in class elki.similarity.cluster.ClusterIntersectionSimilarity

distance(Cluster<?>, Cluster<?>) - Method in class elki.similarity.cluster.ClusterJaccardSimilarity

distance(Clustering<?>, Clustering<?>) - Method in class elki.similarity.cluster.ClusteringAdjustedRandIndexSimilarity

distance(Clustering<?>, Clustering<?>) - Method in class elki.similarity.cluster.ClusteringBCubedF1Similarity

distance(Clustering<?>, Clustering<?>) - Method in class elki.similarity.cluster.ClusteringFowlkesMallowsSimilarity

distance(Clustering<?>, Clustering<?>) - Method in class elki.similarity.cluster.ClusteringRandIndexSimilarity

distance(NumberVector, double[]) - Method in class elki.clustering.hierarchical.birch.BIRCHLloydKMeans

Compute a distance (and count the distance computations).

distance(NumberVector, double[]) - Method in class elki.clustering.kmeans.AbstractKMeans.Instance

Compute the squared distance (and count the distance computations).

distance(NumberVector, double[]) - Method in class elki.clustering.kmeans.BetulaLloydKMeans

Updates statistics and calculates distance between two Objects based on selected criteria.

distance(NumberVector, double[]) - Method in class elki.clustering.kmeans.spherical.SphericalKMeans.Instance

distance(NumberVector, NumberVector) - Method in class elki.clustering.kmeans.AbstractKMeans.Instance

Compute the squared distance (and count the distance computations).

distance(NumberVector, NumberVector) - Method in class elki.clustering.kmeans.spherical.SphericalKMeans.Instance

distance(NumberVector, DBIDRef) - Method in class elki.clustering.kmeans.initialization.KMC2.Instance

Compute the distance of two objects.

distance(NumberVector, DBIDRef) - Method in class elki.clustering.kmeans.initialization.KMeansPlusPlus.NumberVectorInstance

distance(ArrayDBIDs, int, int) - Method in class elki.clustering.hierarchical.AbstractHDBSCAN.HDBSCANAdapter

distance(DBIDRef, DBIDRef) - Method in class elki.clustering.kmeans.initialization.KMeansPlusPlus.MedoidsInstance

distance(DBIDRef, DBIDRef) - Method in class elki.clustering.kmedoids.CLARA.CachedDistanceQuery

distance(DBIDRef, List<NumberVector>) - Method in class elki.clustering.kmeans.initialization.KMC2.Instance

Minimum distance to the current means.

distance(DBIDRef, List<NumberVector>) - Method in class elki.clustering.kmeans.initialization.SphericalAFKMC2.Instance

distance(DBIDRef, V) - Method in class elki.clustering.kmedoids.CLARA.CachedDistanceQuery

distance(T, DBIDRef) - Method in class elki.clustering.kmeans.initialization.KMeansPlusPlus.Instance

Compute the distance of two objects.

distance(V, double[]) - Method in class elki.clustering.kmeans.FuzzyCMeans

Distance computation.

distance(V, DBIDRef) - Method in class elki.clustering.kmedoids.CLARA.CachedDistanceQuery

distance(V, V) - Method in class elki.clustering.kmedoids.CLARA.CachedDistanceQuery

DISTANCE_FUNCTION_ID - Static variable in interface elki.clustering.kmeans.KMeans

OptionID for the distance function.

DISTANCE_FUNCTION_ID - Static variable in class elki.clustering.subspace.SUBCLU.Par

The distance function to determine the distance between objects.

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

Distance function parameter.

DISTANCE_ID - Static variable in class elki.evaluation.clustering.internal.CIndex.Par

Parameter for choosing the distance function.

DISTANCE_ID - Static variable in class elki.evaluation.clustering.internal.ClusterRadius.Par

Parameter for choosing the distance function.

DISTANCE_ID - Static variable in class elki.evaluation.clustering.internal.ConcordantPairsGammaTau.Par

Parameter for choosing the distance function.

DISTANCE_ID - Static variable in class elki.evaluation.clustering.internal.DaviesBouldinIndex.Par

Parameter for choosing the distance function.

DISTANCE_ID - Static variable in class elki.evaluation.clustering.internal.DBCV.Par

Parameter for choosing the distance function.

DISTANCE_ID - Static variable in class elki.evaluation.clustering.internal.PBMIndex.Par

Parameter for choosing the distance function.

DISTANCE_ID - Static variable in class elki.evaluation.clustering.internal.Silhouette.Par

Parameter for choosing the distance function.

DISTANCE_ID - Static variable in class elki.evaluation.clustering.internal.SquaredErrors.Par

Parameter for choosing the distance function.

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

Distance function parameter.

DistanceBasedInitializationWithMedian<O> - Class in elki.clustering.affinitypropagation

Distance based initialization.

DistanceBasedInitializationWithMedian(Distance<? super O>, double) - Constructor for class elki.clustering.affinitypropagation.DistanceBasedInitializationWithMedian

Constructor.

distanceComputations - Variable in class elki.index.preprocessed.fastoptics.RandomProjectedNeighborsAndDensities

Count the number of distance computations.

distances - Variable in class elki.clustering.hierarchical.ClusterMergeHistory

Distance to the parent object.

distanceSum(DistanceQuery<?>, DBIDIter, DBIDs, double, double) - Static method in class elki.clustering.hierarchical.HACAM.Instance

Find the maximum distance of one object to a set.

distq - Variable in class elki.clustering.hierarchical.AbstractHDBSCAN.HDBSCANAdapter

Distance query for exact distances.

distQ - Variable in class elki.clustering.kmeans.initialization.KMeansPlusPlus.MedoidsInstance

Distance query

distQ - Variable in class elki.clustering.kmedoids.CLARANS.Assignment

Distance function to use.

distQ - Variable in class elki.clustering.kmedoids.PAM.Instance

Distance function to use.

distQ - Variable in class elki.clustering.kmedoids.SingleAssignmentKMedoids.Instance

Distance function to use.

distQ - Variable in class elki.clustering.silhouette.FastMSC.Instance

Distance function to use.

distQ - Variable in class elki.clustering.silhouette.FastMSC.Instance2

Distance function to use.

distQ - Variable in class elki.clustering.silhouette.PAMSIL.Instance

Distance function to use.

diststat - Variable in class elki.clustering.kmeans.AbstractKMeans.Instance

Number of distance computations

diststat - Variable in class elki.clustering.kmeans.BetulaLloydKMeans

Number of distance caclulations

diststat - Variable in class elki.clustering.kmeans.initialization.KMC2.Instance

Count the number of distance computations.

diststat - Variable in class elki.clustering.kmeans.initialization.KMeansPlusPlus.Instance

Count the number of distance computations.

diststat - Variable in class elki.clustering.kmeans.initialization.SphericalKMeansPlusPlus.Instance

Count the number of distance computations.

diststat - Variable in class elki.index.tree.betula.CFTree

Number of distance calculations

dm0 - Variable in class elki.clustering.silhouette.FastMSC.Instance2

Distances to the first medoid.

dm1 - Variable in class elki.clustering.silhouette.FastMSC.Instance2

Distances to the second medoid.

dmin - Variable in class elki.clustering.hierarchical.extraction.HDBSCANHierarchyExtraction.TempCluster

Minimum height (densest object).

DOC - Class in elki.clustering.subspace

DOC is a sampling based subspace clustering algorithm.

DOC(double, double, double, RandomFactory) - Constructor for class elki.clustering.subspace.DOC

Constructor.

DOC.Par - Class in elki.clustering.subspace

Parameterization class.

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

Value domain.

doSwap(ArrayDBIDs, int, DBIDRef) - Method in class elki.clustering.silhouette.FastMSC.Instance

Assign each object to the nearest cluster when replacing one medoid.

doSwap(ArrayDBIDs, int, DBIDRef) - Method in class elki.clustering.silhouette.FastMSC.Instance2

Assign each object to the nearest cluster when replacing one medoid.

DoubleIntInt(double, int, int) - Constructor for class elki.clustering.subspace.PROCLUS.DoubleIntInt

doubleValue(int) - Method in interface elki.index.tree.betula.features.ClusterFeature

dq - Variable in class elki.clustering.hierarchical.HACAM.Instance

Distance query

dq - Variable in class elki.clustering.hierarchical.MedoidLinkage.Instance

Distance query

dq - Variable in class elki.clustering.hierarchical.MiniMax.Instance

Distance query function

dropfirst - Variable in class elki.clustering.kmeans.initialization.FarthestPoints

Discard the first vector.

dumpClusteringOutput(Appendable, Clustering<?>) - Method in class elki.result.ClusteringVectorDumper

Dump a single clustering result.

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