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E

EagerPAM<O> - Class in elki.clustering.kmedoids

Variation of PAM that eagerly performs all swaps that yield an improvement during an iteration.

EagerPAM(Distance<? super O>, int, int, KMedoidsInitialization<O>) - Constructor for class elki.clustering.kmedoids.EagerPAM

Constructor.

EagerPAM.Instance - Class in elki.clustering.kmedoids

Instance for a single dataset.

EagerPAM.Par<O> - Class in elki.clustering.kmedoids

Parameterization class.

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

Edit-Distance measures

EditDistance - Class in elki.evaluation.clustering

Edit distance measures.

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

editDistanceFirst() - Method in class elki.evaluation.clustering.EditDistance

Get the editing distance to transform second clustering to first clustering (normalized, 0 = unequal)

editDistanceSecond() - Method in class elki.evaluation.clustering.EditDistance

Get the editing distance to transform second clustering to first clustering (normalized, 0 = unequal)

editFirst - Variable in class elki.evaluation.clustering.EditDistance

Edit operations for first clustering to second clustering.

editOperationsBaseline - Variable in class elki.evaluation.clustering.EditDistance

Baseline for edit operations

editOperationsBaseline() - Method in class elki.evaluation.clustering.EditDistance

Get the baseline editing Operations (= total objects)

editOperationsFirst() - Method in class elki.evaluation.clustering.EditDistance

Get the editing operations required to transform first clustering to second clustering

editOperationsSecond() - Method in class elki.evaluation.clustering.EditDistance

Get the editing operations required to transform second clustering to first clustering

editSecond - Variable in class elki.evaluation.clustering.EditDistance

Edit operations for second clustering to first clustering.

ElkanKMeans<V extends elki.data.NumberVector> - Class in elki.clustering.kmeans

Elkan's fast k-means by exploiting the triangle inequality.

ElkanKMeans(NumberVectorDistance<? super V>, int, int, KMeansInitialization, boolean) - Constructor for class elki.clustering.kmeans.ElkanKMeans

Constructor.

ElkanKMeans.Instance - Class in elki.clustering.kmeans

Inner instance, storing state for a single data set.

ElkanKMeans.Par<V extends elki.data.NumberVector> - Class in elki.clustering.kmeans

Parameterization class.

elki.clustering - package elki.clustering

Clustering algorithms.

elki.clustering.affinitypropagation - package elki.clustering.affinitypropagation

Affinity Propagation (AP) clustering.

elki.clustering.biclustering - package elki.clustering.biclustering

Biclustering algorithms.

elki.clustering.correlation - package elki.clustering.correlation

Correlation clustering algorithms.

elki.clustering.dbscan - package elki.clustering.dbscan

DBSCAN and its generalizations.

elki.clustering.dbscan.parallel - package elki.clustering.dbscan.parallel

Parallel versions of Generalized DBSCAN.

elki.clustering.dbscan.predicates - package elki.clustering.dbscan.predicates

Neighbor and core predicated for Generalized DBSCAN.

elki.clustering.dbscan.util - package elki.clustering.dbscan.util

Utility classes for specialized DBSCAN implementations.

elki.clustering.em - package elki.clustering.em

Expectation-Maximization clustering algorithm for Gaussian Mixture Modeling (GMM).

elki.clustering.em.models - package elki.clustering.em.models

elki.clustering.hierarchical - package elki.clustering.hierarchical

Hierarchical agglomerative clustering (HAC).

elki.clustering.hierarchical.birch - package elki.clustering.hierarchical.birch

BIRCH clustering.

elki.clustering.hierarchical.extraction - package elki.clustering.hierarchical.extraction

Extraction of partitional clusterings from hierarchical results.

elki.clustering.hierarchical.linkage - package elki.clustering.hierarchical.linkage

Linkages for hierarchical clustering.

elki.clustering.kcenter - package elki.clustering.kcenter

K-center clustering.

elki.clustering.kmeans - package elki.clustering.kmeans

K-means clustering and variations.

elki.clustering.kmeans.initialization - package elki.clustering.kmeans.initialization

Initialization strategies for k-means.

elki.clustering.kmeans.initialization.betula - package elki.clustering.kmeans.initialization.betula

Initialization methods for BIRCH-based k-means and EM clustering.

elki.clustering.kmeans.parallel - package elki.clustering.kmeans.parallel

Parallelized implementations of k-means.

elki.clustering.kmeans.quality - package elki.clustering.kmeans.quality

Quality measures for k-Means results.

elki.clustering.kmeans.spherical - package elki.clustering.kmeans.spherical

Spherical k-means clustering and variations.

elki.clustering.kmedoids - package elki.clustering.kmedoids

K-medoids clustering (PAM).

elki.clustering.kmedoids.initialization - package elki.clustering.kmedoids.initialization

elki.clustering.meta - package elki.clustering.meta

Meta clustering algorithms, that get their result from other clusterings or external sources.

elki.clustering.onedimensional - package elki.clustering.onedimensional

Clustering algorithms for one-dimensional data.

elki.clustering.optics - package elki.clustering.optics

OPTICS family of clustering algorithms.

elki.clustering.silhouette - package elki.clustering.silhouette

Silhouette clustering algorithms.

elki.clustering.subspace - package elki.clustering.subspace

Axis-parallel subspace clustering algorithms.

elki.clustering.subspace.clique - package elki.clustering.subspace.clique

Helper classes for the CLIQUE algorithm.

elki.clustering.trivial - package elki.clustering.trivial

Trivial clustering algorithms: all in one, no clusters, label clusterings.

elki.data - package elki.data

elki.data.model - package elki.data.model

Cluster models classes for various algorithms.

elki.datasource.parser - package elki.datasource.parser

elki.evaluation.clustering - package elki.evaluation.clustering

Evaluation of clustering results.

elki.evaluation.clustering.extractor - package elki.evaluation.clustering.extractor

Classes to extract clusterings from hierarchical clustering.

elki.evaluation.clustering.internal - package elki.evaluation.clustering.internal

Internal evaluation measures for clusterings.

elki.evaluation.clustering.pairsegments - package elki.evaluation.clustering.pairsegments

Pair-segment analysis of multiple clusterings.

elki.index.preprocessed.fastoptics - package elki.index.preprocessed.fastoptics

Preprocessed index used by the FastOPTICS algorithm.

elki.index.tree.betula - package elki.index.tree.betula

BETULA clustering by aggregating the data into cluster features.

elki.index.tree.betula.distance - package elki.index.tree.betula.distance

Distance functions for BETULA and BIRCH.

elki.index.tree.betula.features - package elki.index.tree.betula.features

Different variants of Betula and BIRCH cluster features.

elki.result - package elki.result

elki.similarity.cluster - package elki.similarity.cluster

Similarity measures for comparing clusters.

EM<O,M extends MeanModel> - Class in elki.clustering.em

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

EM(int, double, EMClusterModelFactory<? super O, M>) - Constructor for class elki.clustering.em.EM

Constructor.

EM(int, double, EMClusterModelFactory<? super O, M>, int, boolean) - Constructor for class elki.clustering.em.EM

Constructor.

EM(int, double, EMClusterModelFactory<? super O, M>, int, double, boolean) - Constructor for class elki.clustering.em.EM

Constructor.

EM(int, double, EMClusterModelFactory<? super O, M>, int, int, double, boolean) - Constructor for class elki.clustering.em.EM

Constructor.

EM_DELTA_ID - Static variable in class elki.clustering.subspace.P3C.Par

Threshold when to stop EM iterations.

EM.Par<O,M extends MeanModel> - Class in elki.clustering.em

Parameterization class.

EMClusterModel<O,M extends Model> - Interface in elki.clustering.em.models

Models usable in EM clustering.

EMClusterModelFactory<O,M extends Model> - Interface in elki.clustering.em.models

Factory for initializing the EM models.

emDelta - Variable in class elki.clustering.subspace.P3C

Threshold when to stop EM iterations.

emDelta - Variable in class elki.clustering.subspace.P3C.Par

Threshold when to stop EM iterations.

EMModel - Class in elki.data.model

Cluster model of an EM cluster, providing a mean and a full covariance Matrix.

EMModel(double[], double[][]) - Constructor for class elki.data.model.EMModel

Constructor.

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

Active set size

end - Variable in class elki.clustering.kmeans.KDTreePruningKMeans.KDNode

End index of child nodes (exclusive).

endindex - Variable in class elki.clustering.optics.OPTICSXi.SteepArea

End index of steep area

endIndex - Variable in class elki.data.model.OPTICSModel

End index

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

Entropy-based measures

Entropy - Class in elki.evaluation.clustering

Entropy based measures, implemented using natural logarithms.

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

Constructor.

entropyFirst - Variable in class elki.evaluation.clustering.Entropy

Entropy in first

entropyFirst() - Method in class elki.evaluation.clustering.Entropy

Get the entropy of the first clustering (not normalized, 0 = equal).

entropyJoint - Variable in class elki.evaluation.clustering.Entropy

Joint entropy

entropyJoint() - Method in class elki.evaluation.clustering.Entropy

Get the joint entropy of both clusterings (not normalized, 0 = equal).

entropyPowers() - Method in class elki.evaluation.clustering.Entropy

Get Powers entropy (normalized, 0 = equal) Powers = 1 - NMI_Sum

entropySecond - Variable in class elki.evaluation.clustering.Entropy

Entropy in second

entropySecond() - Method in class elki.evaluation.clustering.Entropy

Get the entropy of the second clustering (not normalized, 0 = equal).

epsilon - Variable in class elki.clustering.correlation.COPAC.Settings

Epsilon value for GDBSCAN.

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

Query radius epsilon.

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

Holds the epsilon radius threshold.

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

Holds the epsilon radius threshold.

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

Holds the epsilon radius threshold.

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

Holds the epsilon radius threshold.

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

Range to query with.

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

Range to query with

epsilon - Variable in class elki.clustering.dbscan.predicates.EpsilonNeighborPredicate.Instance

Range to query with

epsilon - Variable in class elki.clustering.dbscan.predicates.SimilarityNeighborPredicate

Range to query with

epsilon - Variable in class elki.clustering.dbscan.predicates.SimilarityNeighborPredicate.Instance

Range to query with

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

Holds the maximum distance to search for objects (performance parameter)

epsilon - Variable in class elki.clustering.SNNClustering

Epsilon radius threshold.

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

Query radius parameter epsilon.

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

Maximum radius of the neighborhood to be considered.

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

Maximum radius of the neighborhood to be considered.

EPSILON_ID - Static variable in class elki.clustering.dbscan.DBSCAN.Par

Parameter to specify the maximum radius of the neighborhood to be considered, must be suitable to the distance function specified.

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

Parameter to specify the maximum distance between two vectors with equal preference vectors before considering them as parallel, must be a double equal to or greater than 0.

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

Maximum radius of the neighborhood to be considered.

EpsilonNeighborPredicate<O> - Class in elki.clustering.dbscan.predicates

The default DBSCAN and OPTICS neighbor predicate, using an epsilon-neighborhood.

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

Full constructor.

EpsilonNeighborPredicate.Instance - Class in elki.clustering.dbscan.predicates

Instance for a particular data set.

epsilonsq - Variable in class elki.clustering.dbscan.predicates.COPACNeighborPredicate

Squared value of epsilon.

equals(Object) - Method in class elki.clustering.optics.OPTICSHeapEntry

Indicates whether some other object is "equal to" this one.

equals(Object) - Method in class elki.data.Subspace

equals(Object) - Method in class elki.evaluation.clustering.pairsegments.Segment

ERiC - Class in elki.clustering.correlation

Performs correlation clustering on the data partitioned according to local correlation dimensionality and builds a hierarchy of correlation clusters that allows multiple inheritance from the clustering result.

ERiC(ERiC.Settings) - Constructor for class elki.clustering.correlation.ERiC

Constructor.

ERiC.Par - Class in elki.clustering.correlation

Parameterization class.

ERiC.Settings - Class in elki.clustering.correlation

Class to wrap the ERiC settings.

ERiCNeighborPredicate - Class in elki.clustering.dbscan.predicates

ERiC neighborhood predicate.

ERiCNeighborPredicate(ERiC.Settings) - Constructor for class elki.clustering.dbscan.predicates.ERiCNeighborPredicate

Constructor.

ERiCNeighborPredicate.Instance - Class in elki.clustering.dbscan.predicates

Instance for a particular data set.

ERiCNeighborPredicate.Par - Class in elki.clustering.dbscan.predicates

Parameterization class.

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

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

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

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

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

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

estimateLogDensity(ClusterFeature) - Method in interface elki.clustering.em.models.BetulaClusterModel

Estimate the log likelihood of a clustering feature.

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

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

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

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

Estimate the log likelihood of a vector.

estimateThreshold(CFTree.TreeNode) - Method in class elki.clustering.hierarchical.birch.CFTree

estimateThreshold(CFNode<L>, ArrayList<L>, double[]) - Method in class elki.index.tree.betula.CFTree

EuclideanDistanceCriterion - Class in elki.clustering.hierarchical.birch

Distance criterion.

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

EuclideanSphericalElkanKMeans<V extends elki.data.NumberVector> - Class in elki.clustering.kmeans.spherical

Elkan's fast k-means by exploiting the triangle inequality in the corresponding Euclidean space.

EuclideanSphericalElkanKMeans(int, int, KMeansInitialization, boolean) - Constructor for class elki.clustering.kmeans.spherical.EuclideanSphericalElkanKMeans

Constructor.

EuclideanSphericalElkanKMeans.Instance - Class in elki.clustering.kmeans.spherical

Inner instance, storing state for a single data set.

EuclideanSphericalElkanKMeans.Par<V extends elki.data.NumberVector> - Class in elki.clustering.kmeans.spherical

Parameterization class.

EuclideanSphericalHamerlyKMeans<V extends elki.data.NumberVector> - Class in elki.clustering.kmeans.spherical

A spherical k-Means algorithm based on Hamerly's fast k-means by exploiting the triangle inequality in the corresponding Euclidean space.

EuclideanSphericalHamerlyKMeans(int, int, KMeansInitialization, boolean) - Constructor for class elki.clustering.kmeans.spherical.EuclideanSphericalHamerlyKMeans

Constructor.

EuclideanSphericalHamerlyKMeans.Instance - Class in elki.clustering.kmeans.spherical

Inner instance, storing state for a single data set.

EuclideanSphericalHamerlyKMeans.Par<V extends elki.data.NumberVector> - Class in elki.clustering.kmeans.spherical

Parameterization class.

EuclideanSphericalSimplifiedElkanKMeans<V extends elki.data.NumberVector> - Class in elki.clustering.kmeans.spherical

A spherical k-Means algorithm based on Hamerly's fast k-means by exploiting the triangle inequality in the corresponding Euclidean space.

EuclideanSphericalSimplifiedElkanKMeans(int, int, KMeansInitialization, boolean) - Constructor for class elki.clustering.kmeans.spherical.EuclideanSphericalSimplifiedElkanKMeans

Constructor.

EuclideanSphericalSimplifiedElkanKMeans.Instance - Class in elki.clustering.kmeans.spherical

Inner instance, storing state for a single data set.

EuclideanSphericalSimplifiedElkanKMeans.Par<V extends elki.data.NumberVector> - Class in elki.clustering.kmeans.spherical

Parameterization class.

evaluateClustering(Database, Relation<? extends NumberVector>, Clustering<?>) - Method in class elki.evaluation.clustering.internal.ClusterRadius

Evaluate a single clustering.

evaluateClustering(Relation<? extends NumberVector>, Clustering<?>) - Method in class elki.evaluation.clustering.internal.ConcordantPairsGammaTau

Evaluate a single clustering.

evaluateClustering(Relation<? extends NumberVector>, Clustering<?>) - Method in class elki.evaluation.clustering.internal.DaviesBouldinIndex

Evaluate a single clustering.

evaluateClustering(Relation<? extends NumberVector>, Clustering<?>) - Method in class elki.evaluation.clustering.internal.PBMIndex

Evaluate a single clustering.

evaluateClustering(Relation<? extends NumberVector>, Clustering<?>) - Method in class elki.evaluation.clustering.internal.SimplifiedSilhouette

Evaluate a single clustering.

evaluateClustering(Relation<? extends NumberVector>, Clustering<?>) - Method in class elki.evaluation.clustering.internal.SquaredErrors

Evaluate a single clustering.

evaluateClustering(Relation<? extends NumberVector>, Clustering<?>) - Method in class elki.evaluation.clustering.internal.VarianceRatioCriterion

Evaluate a single clustering.

evaluateClustering(Relation<? extends O>, DistanceQuery<O>, Clustering<?>) - Method in class elki.evaluation.clustering.internal.CIndex

Evaluate a single clustering.

evaluateClustering(Relation<O>, Clustering<?>) - Method in class elki.evaluation.clustering.internal.DBCV

Evaluate a single clustering.

evaluateClustering(Relation<O>, DistanceQuery<O>, Clustering<?>) - Method in class elki.evaluation.clustering.internal.Silhouette

Evaluate a single clustering.

EvaluateClustering - Class in elki.evaluation.clustering

Evaluate a clustering result by comparing it to an existing cluster label.

EvaluateClustering(ClusteringAlgorithm<?>, boolean, boolean) - Constructor for class elki.evaluation.clustering.EvaluateClustering

Constructor.

EvaluateClustering.Par - Class in elki.evaluation.clustering

Parameterization class.

EvaluateClustering.ScoreResult - Class in elki.evaluation.clustering

Result object for outlier score judgements.

evaluateClusters(ArrayList<PROCLUS.PROCLUSCluster>, long[][], Relation<? extends NumberVector>) - Method in class elki.clustering.subspace.PROCLUS

Evaluates the quality of the clusters.

evaluateRanking(ScoreEvaluation, Cluster<?>, DoubleDBIDList) - Static method in class elki.evaluation.clustering.EvaluateClustering

Evaluate given a cluster (of positive elements) and a scoring list.

evaluteResult(Database, Clustering<?>, Clustering<?>) - Method in class elki.evaluation.clustering.EvaluateClustering

Evaluate a clustering result.

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

Parameter to produce more precise final assignments

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

Perform exact cluster assignments

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

Perform the slower exact assignment step.

excessOfMass() - Method in class elki.clustering.hierarchical.extraction.HDBSCANHierarchyExtraction.TempCluster

Excess of mass measure.

expandCluster(int, WritableIntegerDataStore, KNNSearcher<DBIDRef>, DBIDs, double, FiniteProgress) - Method in class elki.clustering.dbscan.LSDBC

Set-based expand cluster implementation.

expandCluster(DBIDRef, int, WritableIntegerDataStore, ModifiableDoubleDBIDList, ArrayModifiableDBIDs, RangeSearcher<DBIDRef>, FiniteProgress) - Method in class elki.clustering.dbscan.GriDBSCAN.Instance

Set-based expand cluster implementation.

expandCluster(DBIDRef, int, WritableIntegerDataStore, T, ArrayModifiableDBIDs, FiniteProgress) - Method in class elki.clustering.dbscan.GeneralizedDBSCAN.Instance

Set-based expand cluster implementation.

expandCluster(DBIDRef, ArrayModifiableDBIDs) - Method in class elki.clustering.dbscan.DBSCAN.Instance

DBSCAN-function expandCluster.

expandCluster(SimilarityQuery<O>, DBIDRef, FiniteProgress, IndefiniteProgress) - Method in class elki.clustering.SNNClustering

DBSCAN-function expandCluster adapted to SNN criterion.

expandClusterOrder(DBIDRef) - Method in class elki.clustering.optics.OPTICSHeap.Instance

OPTICS-function expandClusterOrder.

expandClusterOrder(DBIDRef) - Method in class elki.clustering.optics.OPTICSList.Instance

OPTICS-function expandClusterOrder.

expandClusterOrder(DBID, ClusterOrder, DistanceQuery<V>, FiniteProgress) - Method in class elki.clustering.optics.FastOPTICS

OPTICS algorithm for processing a point, but with different density estimates

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

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

Add the current DBID to the cluster order, and expand its neighbors if minPts and similar conditions are satisfied.

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

expectedMutualInformation - Variable in class elki.evaluation.clustering.Entropy

Expected mutual information

expectedMutualInformation() - Method in class elki.evaluation.clustering.Entropy

Get the expected mutual information.

ExponionKMeans<V extends elki.data.NumberVector> - Class in elki.clustering.kmeans

Newlings's Exponion k-means algorithm, exploiting the triangle inequality.

ExponionKMeans(NumberVectorDistance<? super V>, int, int, KMeansInitialization, boolean) - Constructor for class elki.clustering.kmeans.ExponionKMeans

Constructor.

ExponionKMeans.Instance - Class in elki.clustering.kmeans

Inner instance, storing state for a single data set.

ExponionKMeans.Par<V extends elki.data.NumberVector> - Class in elki.clustering.kmeans

Parameterization class.

ExternalClustering - Class in elki.clustering.meta

Read an external clustering result from a file, such as produced by ClusteringVectorDumper.

ExternalClustering(URI) - Constructor for class elki.clustering.meta.ExternalClustering

Constructor.

ExternalClustering.Par - Class in elki.clustering.meta

Parameterization class

extractClusters() - Method in class elki.clustering.hierarchical.extraction.AbstractCutDendrogram.Instance

Extract all clusters from the pi-lambda-representation.

extractClusters(ClusterOrder, double, int) - Method in class elki.clustering.optics.OPTICSXi

Extract clusters from a cluster order result.

extractCorrelationClusters(Clustering<Model>, Relation<? extends NumberVector>, int, ERiCNeighborPredicate.Instance) - Method in class elki.clustering.correlation.ERiC

Extracts the correlation clusters and noise from the copac result and returns a mapping of correlation dimension to maps of clusters within this correlation dimension.

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