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F

f1Measure() - Method in class elki.evaluation.clustering.BCubed

Get the BCubed F1-Measure

f1Measure() - Method in class elki.evaluation.clustering.EditDistance

Get the edit distance F1-Measure

f1Measure() - Method in class elki.evaluation.clustering.PairCounting

Get the pair-counting F1-Measure.

f1Measure() - Method in class elki.evaluation.clustering.SetMatchingPurity

Get the set matching F1-Measure

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

Cluster feature factory

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

Cluster feature factory

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

Cluster feature factory

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

Factory() - Constructor for class elki.index.tree.betula.features.VIIFeature.Factory

Factory() - Constructor for class elki.index.tree.betula.features.VVIFeature.Factory

Factory() - Constructor for class elki.index.tree.betula.features.VVVFeature.Factory

Factory(BIRCHDistance, BIRCHAbsorptionCriterion, double, int, double) - Constructor for class elki.clustering.hierarchical.birch.CFTree.Factory

Constructor.

Factory(ClusterFeature.Factory<L>, CFDistance, CFDistance, double, int, double, CFTree.Threshold) - Constructor for class elki.index.tree.betula.CFTree.Factory

Constructor.

FarthestPoints<O> - Class in elki.clustering.kmeans.initialization

K-Means initialization by repeatedly choosing the farthest point (by the minimum distance to earlier points).

FarthestPoints(RandomFactory, boolean) - Constructor for class elki.clustering.kmeans.initialization.FarthestPoints

Constructor.

FarthestPoints.Par<O> - Class in elki.clustering.kmeans.initialization

Parameterization class.

FarthestSumPoints<O> - Class in elki.clustering.kmeans.initialization

K-Means initialization by repeatedly choosing the farthest point (by the sum of distances to previous objects).

FarthestSumPoints(RandomFactory, boolean) - Constructor for class elki.clustering.kmeans.initialization.FarthestSumPoints

Constructor.

FarthestSumPoints.Par<V> - Class in elki.clustering.kmeans.initialization

Parameterization class.

FastCLARA<V> - Class in elki.clustering.kmedoids

Clustering Large Applications (CLARA) with the FastPAM improvements, to increase scalability in the number of clusters.

FastCLARA(Distance<? super V>, int, int, KMedoidsInitialization<V>, double, int, double, boolean, RandomFactory) - Constructor for class elki.clustering.kmedoids.FastCLARA

Constructor.

FastCLARA.Par<V> - Class in elki.clustering.kmedoids

Parameterization class.

FastCLARANS<V> - Class in elki.clustering.kmedoids

A faster variation of CLARANS, that can explore O(k) as many swaps at a similar cost by considering all medoids for each candidate non-medoid.

FastCLARANS(Distance<? super V>, int, int, double, RandomFactory) - Constructor for class elki.clustering.kmedoids.FastCLARANS

Constructor.

FastCLARANS.Assignment - Class in elki.clustering.kmedoids

Assignment state.

FastCLARANS.Par<V> - Class in elki.clustering.kmedoids

Parameterization class.

FastDOC - Class in elki.clustering.subspace

The heuristic variant of the DOC algorithm, FastDOC

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

Constructor.

FastDOC.Par - Class in elki.clustering.subspace

Parameterization class.

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

Clustering Large Applications (CLARA) with the FastPAM improvements, to increase scalability in the number of clusters.

FasterCLARA(Distance<? super O>, int, int, KMedoidsInitialization<O>, int, double, boolean, RandomFactory) - Constructor for class elki.clustering.kmedoids.FasterCLARA

Constructor.

FasterCLARA.Par<V> - Class in elki.clustering.kmedoids

Parameterization class.

FasterMSC<O> - Class in elki.clustering.silhouette

Fast and Eager Medoid Silhouette Clustering.

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

Constructor.

FasterMSC.Instance - Class in elki.clustering.silhouette

FasterMSC clustering instance for a particular data set.

FasterMSC.Instance2 - Class in elki.clustering.silhouette

FasterMSC clustering instance for k=2, simplified.

FasterMSC.Par<O> - Class in elki.clustering.silhouette

Parameterization class.

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

Variation of FastPAM that eagerly performs any swap that yields an improvement during an iteration.

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

Constructor.

FasterPAM.Instance - Class in elki.clustering.kmedoids

Instance for a single dataset.

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

Parameterization class.

FastMSC<O> - Class in elki.clustering.silhouette

Fast Medoid Silhouette Clustering.

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

Constructor.

FastMSC.Instance - Class in elki.clustering.silhouette

FastMSC clustering instance for a particular data set.

FastMSC.Instance2 - Class in elki.clustering.silhouette

Simplified FastMSC clustering instance for k=2.

FastMSC.Par<O> - Class in elki.clustering.silhouette

Parameterization class.

FastMSC.Record - Class in elki.clustering.silhouette

Data stored per point.

FastOPTICS<V extends elki.data.NumberVector> - Class in elki.clustering.optics

FastOPTICS algorithm (Fast approximation of OPTICS)

FastOPTICS(int, RandomProjectedNeighborsAndDensities) - Constructor for class elki.clustering.optics.FastOPTICS

Constructor.

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

FastPAM: An improved version of PAM, that is usually O(k) times faster.

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

Constructor.

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

Constructor.

FastPAM.Instance - Class in elki.clustering.kmedoids

Instance for a single dataset.

FastPAM.Par<V> - Class in elki.clustering.kmedoids

Parameterization class.

FastPAM1<O> - Class in elki.clustering.kmedoids

FastPAM1: A version of PAM that is O(k) times faster, i.e., now in O((n-k)²).

FastPAM1(Distance<? super O>, int, int, KMedoidsInitialization<O>) - Constructor for class elki.clustering.kmedoids.FastPAM1

Constructor.

FastPAM1.Instance - Class in elki.clustering.kmedoids

Instance for a single dataset.

FastPAM1.Par<V> - Class in elki.clustering.kmedoids

Parameterization class.

fastswap - Variable in class elki.clustering.kmedoids.FastPAM.Instance

Tolerance for fast swapping behavior (may perform worse swaps).

fasttol - Variable in class elki.clustering.kmedoids.FastPAM

Tolerance for fast swapping behavior (may perform worse swaps).

fasttol - Variable in class elki.clustering.kmedoids.FastPAM.Par

Tolerance for fast swapping behavior (may perform worse swaps).

FASTTOL_ID - Static variable in class elki.clustering.kmedoids.FastPAM.Par

Tolerance for performing additional swaps.

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

Cluster features parameter.

file - Variable in class elki.clustering.meta.ExternalClustering

The file to be reparsed.

file - Variable in class elki.clustering.meta.ExternalClustering.Par

The file to be reparsed

FILE_ID - Static variable in class elki.clustering.meta.ExternalClustering.Par

Parameter that specifies the name of the file to be re-parsed.

fillDensities(KNNSearcher<DBIDRef>, DBIDs, WritableDoubleDataStore) - Method in class elki.clustering.dbscan.LSDBC

Collect all densities into an array for sorting.

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

Eigenpair filter.

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

Filter for Eigenvectors.

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

Filter for selecting eigenvectors

filter - Variable in class elki.clustering.dbscan.predicates.FourCNeighborPredicate

Filter for selecting eigenvectors.

finalAssignment(List<Pair<double[], long[]>>, Relation<? extends NumberVector>) - Method in class elki.clustering.subspace.PROCLUS

Refinement step to assign the objects to the final clusters.

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

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

Finalize a cluster model.

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

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

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

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

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

finalizeCluster(HDBSCANHierarchyExtraction.TempCluster, Clustering<DendrogramModel>, WritableDoubleDataStore, Cluster<DendrogramModel>, boolean) - Method in class elki.clustering.hierarchical.extraction.HDBSCANHierarchyExtraction.Instance

Make the cluster for the given object

finalizeEStep(double, double) - Method in class elki.clustering.em.models.DiagonalGaussianModel

finalizeEStep(double, double) - Method in interface elki.clustering.em.models.EMClusterModel

Finalize the E step.

finalizeEStep(double, double) - Method in class elki.clustering.em.models.MultivariateGaussianModel

finalizeEStep(double, double) - Method in class elki.clustering.em.models.SphericalGaussianModel

finalizeEStep(double, double) - Method in class elki.clustering.em.models.TextbookMultivariateGaussianModel

finalizeEStep(double, double) - Method in class elki.clustering.em.models.TextbookSphericalGaussianModel

finalizeEStep(double, double) - Method in class elki.clustering.em.models.TwoPassMultivariateGaussianModel

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

Finalize the first pass of the E step.

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

Finish computation of the mean.

findAndEvaluateThreshold(DoubleDynamicHistogram) - Method in class elki.clustering.correlation.LMCLUS

Evaluate the histogram to find a suitable threshold

findBasis(Relation<? extends NumberVector>, ORCLUS.ORCLUSCluster, int) - Method in class elki.clustering.correlation.ORCLUS

Finds the basis of the subspace of dimensionality dim for the specified cluster.

findBest(double[], double[], int[], int) - Static method in class elki.clustering.hierarchical.Anderberg.Instance

Find the best in a row of the triangular matrix.

findBest(ArrayModifiableDBIDs, DBIDArrayMIter, DBIDVar) - Method in class elki.clustering.optics.OPTICSList.Instance

Find the minimum in the candidates array.

findBestSwap(DBIDRef, double[]) - Method in class elki.clustering.silhouette.FastMSC.Instance

Compute the loss change when choosing j as new medoid.

findBestSwap(DBIDRef, double[]) - Method in class elki.clustering.silhouette.FastMSC.Instance2

Compute the loss change when choosing j as new medoid.

findBestSwaps(DBIDArrayIter, ArrayModifiableDBIDs, double[], double[], double[]) - Method in class elki.clustering.kmedoids.FastPAM.Instance

Find the best swaps.

findDenseSubspaceCandidates(Relation<? extends NumberVector>, List<CLIQUESubspace>) - Method in class elki.clustering.subspace.CLIQUE

Determines the k-dimensional dense subspace candidates from the specified (k-1)-dimensional dense subspaces.

findDenseSubspaces(Relation<? extends NumberVector>, List<CLIQUESubspace>) - Method in class elki.clustering.subspace.CLIQUE

Determines the k-dimensional dense subspaces and performs a pruning if this option is chosen.

findDimensions(ArrayDBIDs, Relation<? extends NumberVector>, DistanceQuery<? extends NumberVector>, RangeSearcher<DBIDRef>) - Method in class elki.clustering.subspace.PROCLUS

Determines the set of correlated dimensions for each medoid in the specified medoid set.

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

Refinement step that determines the set of correlated dimensions for each cluster centroid.

findLeaf(CFTree.TreeNode, NumberVector) - Method in class elki.clustering.hierarchical.birch.CFTree

Find the leaf of a cluster, to get the final cluster assignment.

findLeaf(NumberVector) - Method in class elki.clustering.hierarchical.birch.CFTree

Find the leaf of a cluster, to get the final cluster assignment.

findLeaf(NumberVector) - Method in class elki.index.tree.betula.CFTree

Find the leaf of a cluster, to get the final cluster assignment.

findLeaf(CFNode<L>, NumberVector) - Method in class elki.index.tree.betula.CFTree

Find the leaf of a cluster, to get the final cluster assignment.

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

Find the maximum distance of one object to a set.

findMedoid(DBIDs, DistanceQuery<?>, int, DBIDArrayMIter, double, WritableDoubleDataStore, WritableDoubleDataStore, WritableDoubleDataStore) - Static method in class elki.clustering.kmedoids.initialization.GreedyG

Find the best medoid of a given fixed set.

findMedoid(DBIDs, DistanceQuery<?>, IntegerDataStore, int, DBIDArrayMIter, double[]) - Static method in class elki.clustering.kmedoids.initialization.AlternateRefinement

Find the best medoid of a given fixed set.

findMedoid(DistanceQuery<?>, DBIDs, DBIDArrayMIter) - Static method in class elki.clustering.hierarchical.MedoidLinkage.Instance

Find the prototypes.

findMerge() - Method in class elki.clustering.hierarchical.AGNES.Instance

Perform the next merge step in AGNES.

findMerge() - Method in class elki.clustering.hierarchical.Anderberg.Instance

Perform the next merge step.

findMerge() - Method in class elki.clustering.hierarchical.HACAM.Instance

findMerge() - Method in class elki.clustering.hierarchical.MedoidLinkage.Instance

findMerge() - Method in class elki.clustering.hierarchical.MiniMax.Instance

findMerge() - Method in class elki.clustering.hierarchical.MiniMaxAnderberg.Instance

findNeighbors(DBIDRef, long[], ArrayModifiableDBIDs, Relation<? extends NumberVector>) - Method in class elki.clustering.subspace.DOC

Find the neighbors of point q in the given subspace

findOneDimensionalDenseSubspaceCandidates(Relation<? extends NumberVector>) - Method in class elki.clustering.subspace.CLIQUE

Determines the one-dimensional dense subspace candidates by making a pass over the database.

findOneDimensionalDenseSubspaces(Relation<? extends NumberVector>) - Method in class elki.clustering.subspace.CLIQUE

Determines the one dimensional dense subspaces and performs a pruning if this option is chosen.

findOutliers(Relation<? extends NumberVector>, List<MultivariateGaussianModel>, ArrayList<P3C.ClusterCandidate>, ModifiableDBIDs) - Method in class elki.clustering.subspace.P3C

Performs outlier detection by testing the Mahalanobis distance of each point in a cluster against the critical value of the ChiSquared distribution with as many degrees of freedom as the cluster has relevant attributes.

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

Find the prototypes.

findPrototype(DistanceQuery<?>, DBIDs, DBIDs, DBIDVar, double) - Static method in class elki.clustering.hierarchical.MiniMax.Instance

Find the prototypes.

findPrototypeSingleton(DistanceQuery<?>, DBIDs, DBIDRef, DBIDVar) - Static method in class elki.clustering.hierarchical.HACAM.Instance

Find the prototypes.

findPrototypeSingleton(DistanceQuery<?>, DBIDs, DBIDRef, DBIDVar) - Static method in class elki.clustering.hierarchical.MiniMax.Instance

Find the prototypes.

findSeparation(Relation<? extends NumberVector>, DBIDs, int, Random) - Method in class elki.clustering.correlation.LMCLUS

This method samples a number of linear manifolds an tries to determine which the one with the best cluster is.

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

Returns the shared nearest neighbors of the specified query object in the given database.

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

Find the splitting point in the merge history.

findSplit() - Method in class elki.clustering.hierarchical.extraction.CutDendrogramByHeight.Instance

findSplit() - Method in class elki.clustering.hierarchical.extraction.CutDendrogramByNumberOfClusters.Instance

findUnlinked(int, int, int[]) - Static method in class elki.clustering.hierarchical.NNChain.Instance

Find an unlinked object.

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

FIRST_UNIFORM_ID - Static variable in class elki.clustering.kmeans.initialization.betula.CFKPlusPlusLeaves.Par

Choose the first center based on variance contribution.

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

Choose the first center based on variance contribution.

FirstK<O> - Class in elki.clustering.kmeans.initialization

Initialize K-means by using the first k objects as initial means.

FirstK() - Constructor for class elki.clustering.kmeans.initialization.FirstK

Constructor.

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

Parameterization class.

firstPassE(NumberVector, double) - Method in class elki.clustering.em.models.TwoPassMultivariateGaussianModel

First pass: update the mean only.

firstPassE(O, double) - Method in interface elki.clustering.em.models.EMClusterModel

First run in the E step.

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

Choose the first center uniformly from the leaves.

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

Choose the first center based on variance contribution.

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

Choose the first center uniformly from the cluster features.

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

Choose the first center uniformly from the cluster feature.

FlexibleBetaLinkage - Class in elki.clustering.hierarchical.linkage

Flexible-beta linkage as proposed by Lance and Williams.

FlexibleBetaLinkage(double) - Constructor for class elki.clustering.hierarchical.linkage.FlexibleBetaLinkage

Constructor.

FlexibleBetaLinkage.Par - Class in elki.clustering.hierarchical.linkage

Parameterization class.

fMeasure(double) - Method in class elki.evaluation.clustering.PairCounting

Get the pair-counting F-Measure

fMeasureFirst() - Method in class elki.evaluation.clustering.SetMatchingPurity

Get the Van Rijsbergen’s F measure (asymmetric) for first clustering

fMeasureSecond() - Method in class elki.evaluation.clustering.SetMatchingPurity

Get the Van Rijsbergen’s F measure (asymmetric) for second clustering

FORCE_LABEL_ID - Static variable in class elki.result.ClusteringVectorDumper.Par

Force label parameter.

forceLabel - Variable in class elki.result.ClusteringVectorDumper

Optional label to force for this output.

forceLabel - Variable in class elki.result.ClusteringVectorDumper.Par

Optional label to force for this output.

FourC - Class in elki.clustering.correlation

4C identifies local subgroups of data objects sharing a uniform correlation.

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

Constructor.

FourC.Par - Class in elki.clustering.correlation

Parameterization class.

FourC.Settings - Class in elki.clustering.correlation

Class wrapping the 4C parameter settings.

FourC.Settings.Par - Class in elki.clustering.correlation

Parameterization class for 4C settings.

FourCCorePredicate - Class in elki.clustering.dbscan.predicates

The 4C core point predicate.

FourCCorePredicate(FourC.Settings) - Constructor for class elki.clustering.dbscan.predicates.FourCCorePredicate

Default constructor.

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

Instance for a particular data set.

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

Parameterization class

FourCNeighborPredicate - Class in elki.clustering.dbscan.predicates

4C identifies local subgroups of data objects sharing a uniform correlation.

FourCNeighborPredicate(FourC.Settings) - Constructor for class elki.clustering.dbscan.predicates.FourCNeighborPredicate

Constructor.

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

Instance for a particular data set.

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

Parameterization class.

fowlkesMallows() - Method in class elki.evaluation.clustering.PairCounting

Computes the pair-counting Fowlkes-mallows (flat only, non-hierarchical!)

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

Fuzzy Clustering developed by Dunn and revisited by Bezdek

FuzzyCMeans(int, int, int, double, double, boolean, KMeansInitialization) - Constructor for class elki.clustering.kmeans.FuzzyCMeans

Constructor.

FuzzyCMeans.Par - Class in elki.clustering.kmeans

Parameterization class.

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