S-Index (ELKI: Environment for DeveLoping KDD-Applications Supported by Index-Structures)

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S

sample(double) - Method in class elki.clustering.kmeans.initialization.AFKMC2.Instance
sample(double) - Method in class elki.clustering.kmeans.initialization.KMC2.Instance: Weighted sampling.
SAMPLE - elki.clustering.onedimensional.KNNKernelDensityMinimaClustering.Mode
sampleFirst(ClusterFeature, List<? extends AsClusterFeature>, Random) - Method in class elki.clustering.kmeans.initialization.betula.CFKPlusPlusLeaves: Sample the first cluster center.
SampleKMeans<V extends elki.data.NumberVector> - Class in elki.clustering.kmeans.initialization: Initialize k-means by running k-means on a sample of the data set only.
SampleKMeans(RandomFactory, KMeans<V, ?>, double) - Constructor for class elki.clustering.kmeans.initialization.SampleKMeans: Constructor.
SAMPLESIZE_ID - Static variable in class elki.clustering.kmedoids.CLARA.Par: The sample size.
SAMPLESIZE_ID - Static variable in class elki.clustering.kmedoids.FastCLARA.Par: The sample size.
SAMPLESIZE_ID - Static variable in class elki.clustering.kmedoids.FasterCLARA.Par: The sample size.
sampling - Variable in class elki.clustering.kmedoids.CLARA.Par: Sampling rate.
sampling - Variable in class elki.clustering.kmedoids.CLARA: Sampling rate.
sampling - Variable in class elki.clustering.kmedoids.FastCLARA.Par: Sampling rate.
sampling - Variable in class elki.clustering.kmedoids.FastCLARA: Sampling rate.
sampling - Variable in class elki.clustering.kmedoids.FasterCLARA.Par: Sampling rate.
sampling - Variable in class elki.clustering.kmedoids.FasterCLARA: Sampling rate.
SAMPLINGL_ID - Static variable in class elki.clustering.correlation.LMCLUS.Par: Sampling intensity level
samplingLevel - Variable in class elki.clustering.correlation.LMCLUS.Par: Sampling level
samplingLevel - Variable in class elki.clustering.correlation.LMCLUS: Number of sampling rounds to find a good split
ScoreResult(ClusterContingencyTable) - Constructor for class elki.evaluation.clustering.EvaluateClustering.ScoreResult: Constructor.
scratch - Variable in class elki.clustering.silhouette.PAMSIL.Instance: Scratch cluster mapping.
second - Variable in class elki.clustering.kmeans.AnnulusKMeans.Instance: Second nearest cluster.
second - Variable in class elki.clustering.kmeans.ShallotKMeans.Instance: Second nearest cluster.
second - Variable in class elki.clustering.kmedoids.CLARANS.Assignment: Distance to the second nearest medoid.
second - Variable in class elki.clustering.kmedoids.PAM.Instance: Distance to the second nearest medoid.
secondary - Variable in class elki.clustering.kmeans.HartiganWongKMeans.Instance: Second nearest cluster.
secondid - Variable in class elki.clustering.kmedoids.CLARANS.Assignment: Medoid id of the second closest.
SEED_ID - Static variable in class elki.clustering.correlation.ORCLUS.Par: Parameter to specify the random generator seed.
SEED_ID - Static variable in class elki.clustering.kmeans.initialization.betula.AbstractCFKMeansInitialization.Par: Parameter to specify the random generator seed.
SEED_ID - Static variable in interface elki.clustering.kmeans.KMeans: Parameter to specify the random generator seed.
SEED_ID - Static variable in class elki.clustering.subspace.PROCLUS.Par: Parameter to specify the random generator seed.
Segment - Class in elki.evaluation.clustering.pairsegments: A segment represents a set of pairs that share the same clustering properties.
Segment(int) - Constructor for class elki.evaluation.clustering.pairsegments.Segment: Constructor.
Segment(int[]) - Constructor for class elki.evaluation.clustering.pairsegments.Segment: Constructor.
segments - Variable in class elki.evaluation.clustering.pairsegments.Segments: The actual segments
Segments - Class in elki.evaluation.clustering.pairsegments: Creates segments of two or more clusterings.
Segments(List<Clustering<?>>) - Constructor for class elki.evaluation.clustering.pairsegments.Segments: Initialize segments.
selectColumn(int, boolean) - Method in class elki.clustering.biclustering.ChengAndChurch.BiclusterCandidate: Select or deselect a column.
SELECTED - Static variable in interface elki.clustering.biclustering.ChengAndChurch.CellVisitor: Different modes of operation.
selectivity(double) - Method in class elki.clustering.subspace.clique.CLIQUEUnit: Returns the selectivity of this unit, which is defined as the fraction of total feature vectors contained in this unit.
selectRow(int, boolean) - Method in class elki.clustering.biclustering.ChengAndChurch.BiclusterCandidate: Select or deselect a row.
SELFPAIR_ID - Static variable in class elki.evaluation.clustering.EvaluateClustering.Par: Parameter flag to disable self-pairing
selfPairing - Variable in class elki.evaluation.clustering.ClusterContingencyTable: Self pairing
selfPairing - Variable in class elki.evaluation.clustering.EvaluateClustering.Par: Use self-pairing in pair-counting measures
selfPairing - Variable in class elki.evaluation.clustering.EvaluateClustering: Use self-pairing in pair-counting measures
sensitivityThreshold - Variable in class elki.clustering.correlation.LMCLUS: The current threshold value calculated by the findSeperation Method.
sep - Variable in class elki.clustering.kmeans.HamerlyKMeans.Instance: Separation of means / distance moved.
sep - Variable in class elki.clustering.kmeans.SimplifiedElkanKMeans.Instance: Cluster separation
sep - Variable in class elki.clustering.kmeans.spherical.EuclideanSphericalHamerlyKMeans.Instance: Separation of means / distance moved.
sep - Variable in class elki.clustering.kmeans.spherical.EuclideanSphericalSimplifiedElkanKMeans.Instance: Cluster separation
Separation() - Constructor for class elki.clustering.correlation.LMCLUS.Separation
seq - Variable in class elki.clustering.hierarchical.extraction.HDBSCANHierarchyExtraction.TempCluster: Merge id of the cluster for prototype identification.
seq - Variable in class elki.clustering.hierarchical.extraction.SimplifiedHierarchyExtraction.TempCluster: Merge id of the cluster for prototype identification.
setChild(int, AsClusterFeature) - Method in class elki.index.tree.betula.CFNode: Set child with index i to CF cf
setChild(AsClusterFeature) - Method in class elki.index.tree.betula.CFNode: Add a child without statistics
setCovarianceMatrix(double[][]) - Method in class elki.data.model.EMModel
setDimension(int) - Method in class elki.data.model.DimensionModel: Set cluster dimensionality
setDistance(NumberVectorDistance<? super V>) - Method in class elki.clustering.kmeans.AbstractKMeans
setDistance(NumberVectorDistance<? super V>) - Method in class elki.clustering.kmeans.BestOfMultipleKMeans
setDistance(NumberVectorDistance<? super V>) - Method in class elki.clustering.kmeans.BisectingKMeans
setDistance(NumberVectorDistance<? super V>) - Method in interface elki.clustering.kmeans.KMeans: Set the distance function to use.
setIDs(DBIDs) - Method in class elki.data.Cluster: Access group object
setInitialClusters(List<? extends Cluster<? extends MeanModel>>) - Method in class elki.clustering.kmeans.initialization.Predefined: Set the initial means.
setInitializer(KMeansInitialization) - Method in class elki.clustering.kmeans.AbstractKMeans
setInitializer(KMeansInitialization) - Method in class elki.clustering.kmeans.BestOfMultipleKMeans
setInitializer(KMeansInitialization) - Method in class elki.clustering.kmeans.BisectingKMeans
setInitializer(KMeansInitialization) - Method in interface elki.clustering.kmeans.KMeans: Set the initialization method.
setInitialMeans(double[][]) - Method in class elki.clustering.kmeans.initialization.Predefined: Set the initial means.
setInitialMeans(List<double[]>) - Method in class elki.clustering.kmeans.initialization.Predefined: Set the initial means.
setInvertedRows(DBIDs) - Method in class elki.data.model.BiclusterWithInversionsModel: Sets the ids of the inverted rows.
setK(int) - Method in class elki.clustering.kmeans.AbstractKMeans
setK(int) - Method in class elki.clustering.kmeans.BestOfMultipleKMeans
setK(int) - Method in class elki.clustering.kmeans.BisectingKMeans
setK(int) - Method in interface elki.clustering.kmeans.KMeans: Set the value of k.
setLes(LinearEquationSystem) - Method in class elki.data.model.LinearEquationModel: Assign new Linear Equation System.
SetMatchingPurity - Class in elki.evaluation.clustering: Set matching purity measures.
SetMatchingPurity(ClusterContingencyTable) - Constructor for class elki.evaluation.clustering.SetMatchingPurity: Constructor.
setModel(M) - Method in class elki.data.Cluster: Access model object
setName(String) - Method in class elki.data.Cluster: Set Cluster name
setNoise(boolean) - Method in class elki.data.Cluster: Setter for noise flag.
setPCAResult(PCAFilteredResult) - Method in class elki.data.model.CorrelationModel: Assign new PCA result
setSize(int, int) - Method in class elki.clustering.hierarchical.ClusterMergeHistoryBuilder: Set the cluster size of an object.
settings - Variable in class elki.clustering.correlation.COPAC.Par: COPAC settings.
settings - Variable in class elki.clustering.correlation.COPAC: Settings class.
settings - Variable in class elki.clustering.correlation.ERiC.Par: The settings to use.
settings - Variable in class elki.clustering.correlation.ERiC: ERiC Settings.
settings - Variable in class elki.clustering.correlation.FourC.Par: Settings storage.
settings - Variable in class elki.clustering.correlation.FourC.Settings.Par: Settings storage.
settings - Variable in class elki.clustering.dbscan.predicates.COPACNeighborPredicate.Par: COPAC settings.
settings - Variable in class elki.clustering.dbscan.predicates.COPACNeighborPredicate: COPAC parameters
settings - Variable in class elki.clustering.dbscan.predicates.ERiCNeighborPredicate.Par: ERiC settings.
settings - Variable in class elki.clustering.dbscan.predicates.ERiCNeighborPredicate: ERiC parameters
settings - Variable in class elki.clustering.dbscan.predicates.FourCCorePredicate.Instance: The PreDeCon settings class.
settings - Variable in class elki.clustering.dbscan.predicates.FourCCorePredicate.Par: The PreDeCon settings class.
settings - Variable in class elki.clustering.dbscan.predicates.FourCCorePredicate: The PreDeCon settings class.
settings - Variable in class elki.clustering.dbscan.predicates.FourCNeighborPredicate.Par: 4C settings.
settings - Variable in class elki.clustering.dbscan.predicates.FourCNeighborPredicate: 4C settings class.
settings - Variable in class elki.clustering.dbscan.predicates.PreDeConCorePredicate.Instance: The PreDeCon settings class.
settings - Variable in class elki.clustering.dbscan.predicates.PreDeConCorePredicate.Par: The PreDeCon settings class.
settings - Variable in class elki.clustering.dbscan.predicates.PreDeConCorePredicate: The PreDeCon settings class.
settings - Variable in class elki.clustering.dbscan.predicates.PreDeConNeighborPredicate.Par: PreDeCon settings.
settings - Variable in class elki.clustering.dbscan.predicates.PreDeConNeighborPredicate: PreDeCon settings class.
settings - Variable in class elki.clustering.subspace.PreDeCon.Par: PreDeConSettings.
settings - Variable in class elki.clustering.subspace.PreDeCon.Settings.Par: Settings to build.
Settings() - Constructor for class elki.clustering.correlation.COPAC.Settings
Settings() - Constructor for class elki.clustering.correlation.ERiC.Settings
Settings() - Constructor for class elki.clustering.correlation.FourC.Settings
Settings() - Constructor for class elki.clustering.subspace.PreDeCon.Settings
setWeight(double) - Method in class elki.clustering.em.models.DiagonalGaussianModel
setWeight(double) - Method in interface elki.clustering.em.models.EMClusterModel: Set the cluster weight.
setWeight(double) - Method in class elki.clustering.em.models.MultivariateGaussianModel
setWeight(double) - Method in class elki.clustering.em.models.SphericalGaussianModel
setWeight(double) - Method in class elki.clustering.em.models.TextbookMultivariateGaussianModel
setWeight(double) - Method in class elki.clustering.em.models.TextbookSphericalGaussianModel
setWeight(double) - Method in class elki.clustering.em.models.TwoPassMultivariateGaussianModel
ShallotKMeans<V extends elki.data.NumberVector> - Class in elki.clustering.kmeans: Borgelt's Shallot k-means algorithm, exploiting the triangle inequality.
ShallotKMeans(NumberVectorDistance<? super V>, int, int, KMeansInitialization, boolean) - Constructor for class elki.clustering.kmeans.ShallotKMeans: Constructor.
ShallotKMeans.Instance - Class in elki.clustering.kmeans: Inner instance, storing state for a single data set.
ShallotKMeans.Par<V extends elki.data.NumberVector> - Class in elki.clustering.kmeans: Parameterization class.
shrinkActiveSet(int[], int, int) - Static method in class elki.clustering.hierarchical.AGNES.Instance: Shrink the active set: if the last x objects are all merged, we can reduce the working size accordingly.
shuffle(ArrayModifiableDBIDs, int, int, Random) - Static method in class elki.clustering.kmedoids.initialization.LAB: Partial Fisher-Yates shuffle.
Signature(int[], DBIDs) - Constructor for class elki.clustering.subspace.P3C.Signature: Constructor.
silhouette(IntegerDataStore, int) - Method in class elki.clustering.silhouette.PAMSIL.Instance: Evaluate the average Silhouette of the current cluster assignment
Silhouette<O> - Class in elki.evaluation.clustering.internal: Compute the silhouette of a data set.
Silhouette(Distance<? super O>, boolean) - Constructor for class elki.evaluation.clustering.internal.Silhouette: Constructor.
Silhouette(Distance<? super O>, NoiseHandling, boolean) - Constructor for class elki.evaluation.clustering.internal.Silhouette: Constructor.
SILHOUETTE_NAME - Static variable in class elki.evaluation.clustering.internal.Silhouette: Name of the silhouette result.
Silhouette.Par<O> - Class in elki.evaluation.clustering.internal: Parameterization class.
silhouettes - Variable in class elki.clustering.silhouette.PAMSIL.Instance: Store the per-point silhouette scores for plotting.
silhouetteScores() - Method in class elki.clustering.silhouette.FastMSC.Instance: Get the silhouette scores per point (must be run() first)
silhouetteScores() - Method in class elki.clustering.silhouette.FastMSC.Instance2: Get the silhouette scores per point (must be run() first)
silhouetteScores() - Method in class elki.clustering.silhouette.PAMSIL.Instance
simFunc - Variable in class elki.clustering.dbscan.predicates.SimilarityNeighborPredicate: Similarity function to use
similarity - Variable in class elki.clustering.affinitypropagation.SimilarityBasedInitializationWithMedian: Similarity function.
similarity(double[], double[]) - Method in class elki.clustering.kmeans.spherical.SphericalKMeans.Instance: Compute the similarity of two objects (and count this operation).
similarity(Cluster<?>, Cluster<?>) - Method in class elki.similarity.cluster.ClusterIntersectionSimilarity
similarity(Cluster<?>, Cluster<?>) - Method in class elki.similarity.cluster.ClusterJaccardSimilarity
similarity(Clustering<?>, Clustering<?>) - Method in class elki.similarity.cluster.ClusteringAdjustedRandIndexSimilarity
similarity(Clustering<?>, Clustering<?>) - Method in class elki.similarity.cluster.ClusteringBCubedF1Similarity
similarity(Clustering<?>, Clustering<?>) - Method in class elki.similarity.cluster.ClusteringFowlkesMallowsSimilarity
similarity(Clustering<?>, Clustering<?>) - Method in class elki.similarity.cluster.ClusteringRandIndexSimilarity
similarity(NumberVector, double[]) - Method in class elki.clustering.kmeans.spherical.SphericalKMeans.Instance: Compute the similarity of two objects (and count this operation).
similarity(NumberVector, DBIDRef) - Method in class elki.clustering.kmeans.initialization.SphericalAFKMC2.Instance: Compute the distance of two objects.
similarity(NumberVector, DBIDRef) - Method in class elki.clustering.kmeans.initialization.SphericalKMeansPlusPlus.Instance: Compute the distance of two objects.
SimilarityBasedInitializationWithMedian<O> - Class in elki.clustering.affinitypropagation: Similarity based initialization.
SimilarityBasedInitializationWithMedian(Similarity<? super O>, double) - Constructor for class elki.clustering.affinitypropagation.SimilarityBasedInitializationWithMedian: Constructor.
similarityFunction - Variable in class elki.clustering.SNNClustering: The similarity function for the shared nearest neighbor similarity.
SimilarityNeighborPredicate<O> - Class in elki.clustering.dbscan.predicates: The DBSCAN neighbor predicate for a Similarity, using all neighbors with a minimum similarity.
SimilarityNeighborPredicate(double, Similarity<? super O>) - Constructor for class elki.clustering.dbscan.predicates.SimilarityNeighborPredicate: Full constructor.
SimilarityNeighborPredicate.Instance - Class in elki.clustering.dbscan.predicates: Instance for a particular data set.
SimplePrototypeModel<V> - Class in elki.data.model: Cluster model that stores a prototype for each cluster.
SimplePrototypeModel(V) - Constructor for class elki.data.model.SimplePrototypeModel: Constructor with prototype
SimplifiedElkanKMeans<V extends elki.data.NumberVector> - Class in elki.clustering.kmeans: Simplified version of Elkan's k-means by exploiting the triangle inequality.
SimplifiedElkanKMeans(NumberVectorDistance<? super V>, int, int, KMeansInitialization, boolean) - Constructor for class elki.clustering.kmeans.SimplifiedElkanKMeans: Constructor.
SimplifiedElkanKMeans.Instance - Class in elki.clustering.kmeans: Inner instance, storing state for a single data set.
SimplifiedElkanKMeans.Par<V extends elki.data.NumberVector> - Class in elki.clustering.kmeans: Parameterization class.
SimplifiedHierarchyExtraction - Class in elki.clustering.hierarchical.extraction: Extraction of simplified cluster hierarchies, as proposed in HDBSCAN.
SimplifiedHierarchyExtraction(HierarchicalClusteringAlgorithm, int) - Constructor for class elki.clustering.hierarchical.extraction.SimplifiedHierarchyExtraction: Constructor.
SimplifiedHierarchyExtraction.Instance - Class in elki.clustering.hierarchical.extraction: Instance for a single data set.
SimplifiedHierarchyExtraction.Par - Class in elki.clustering.hierarchical.extraction: Parameterization class.
SimplifiedHierarchyExtraction.TempCluster - Class in elki.clustering.hierarchical.extraction: Temporary cluster.
SimplifiedHierarchyExtractionEvaluator - Class in elki.evaluation.clustering.extractor: Extract clusters from a hierarchical clustering, during the evaluation phase.
SimplifiedHierarchyExtractionEvaluator(SimplifiedHierarchyExtraction) - Constructor for class elki.evaluation.clustering.extractor.SimplifiedHierarchyExtractionEvaluator: Constructor.
SimplifiedHierarchyExtractionEvaluator.Par - Class in elki.evaluation.clustering.extractor: Parameterization class.
simplifiedPSI - Variable in class elki.evaluation.clustering.PairSetsIndex: Simplified PSI (with e = 1)
simplifiedPSI() - Method in class elki.evaluation.clustering.PairSetsIndex: Get the simplified PSI value using e = 1
SimplifiedSilhouette - Class in elki.evaluation.clustering.internal: Compute the simplified silhouette of a data set.
SimplifiedSilhouette(NumberVectorDistance<?>, NoiseHandling, boolean) - Constructor for class elki.evaluation.clustering.internal.SimplifiedSilhouette: Constructor.
SimplifiedSilhouette.Par - Class in elki.evaluation.clustering.internal: Parameterization class.
simplify - Variable in class elki.clustering.hierarchical.extraction.AbstractCutDendrogram.Par: Produce a simpler result by adding single points directly into the merge cluster.
simplify - Variable in class elki.clustering.hierarchical.extraction.AbstractCutDendrogram: Produce a simpler result by adding single points directly into the merge cluster.
singleAssignment(Relation<?>) - Method in class elki.clustering.trivial.ByLabelClustering: Assigns the objects of the database to single clusters according to their labels.
SingleAssignmentKMeans<V extends elki.data.NumberVector> - Class in elki.clustering.kmeans: Pseudo-k-means variations, that assigns each object to the nearest center.
SingleAssignmentKMeans(NumberVectorDistance<? super V>, int, KMeansInitialization) - Constructor for class elki.clustering.kmeans.SingleAssignmentKMeans: Constructor.
SingleAssignmentKMeans.Instance - Class in elki.clustering.kmeans: Inner instance, storing state for a single data set.
SingleAssignmentKMeans.Par<V extends elki.data.NumberVector> - Class in elki.clustering.kmeans: Parameterization class.
SingleAssignmentKMedoids<O> - Class in elki.clustering.kmedoids: K-medoids clustering by using the initialization only, then assigning each object to the nearest neighbor.
SingleAssignmentKMedoids(Distance<? super O>, int, KMedoidsInitialization<O>) - Constructor for class elki.clustering.kmedoids.SingleAssignmentKMedoids: Constructor.
SingleAssignmentKMedoids.Instance - Class in elki.clustering.kmedoids: Instance for a single dataset.
SingleAssignmentKMedoids.Par<O> - Class in elki.clustering.kmedoids: Parameterization class.
SingleLinkage - Class in elki.clustering.hierarchical.linkage: Single-linkage ("minimum") clustering method.
SingleLinkage() - Constructor for class elki.clustering.hierarchical.linkage.SingleLinkage: Deprecated.
use the static instance SingleLinkage.STATIC instead.
SingleLinkage.Par - Class in elki.clustering.hierarchical.linkage: Class parameterizer.
singleNodeDeletion(double[][], ChengAndChurch.BiclusterCandidate) - Method in class elki.clustering.biclustering.ChengAndChurch: Algorithm 1 of Cheng and Church:
SINGULARITY_CHEAT - Static variable in class elki.clustering.em.models.DiagonalGaussianModel: Constant to avoid zero values.
SINGULARITY_CHEAT - Static variable in class elki.clustering.em.models.MultivariateGaussianModel: Constant to avoid singular matrixes.
SINGULARITY_CHEAT - Static variable in class elki.clustering.em.models.SphericalGaussianModel: Constant to avoid zero values.
size - Variable in class elki.clustering.hierarchical.ClusterDistanceMatrix: Number of rows/columns.
size - Variable in class elki.index.tree.betula.CFDistanceMatrix: Number of entries
size() - Method in class elki.clustering.dbscan.predicates.COPACNeighborPredicate.COPACModel
size() - Method in class elki.clustering.hierarchical.ClusterMergeHistory: Number of elements clustered.
size() - Method in class elki.clustering.optics.ClusterOrder: Size.
size() - Method in class elki.data.Cluster: Delegate to database object group.
size() - Method in class elki.evaluation.clustering.pairsegments.Segments: Get the number of segments
size(ArrayDBIDs) - Method in class elki.clustering.hierarchical.AbstractHDBSCAN.HDBSCANAdapter
size1 - Variable in class elki.evaluation.clustering.ClusterContingencyTable: Number of clusters.
size2 - Variable in class elki.evaluation.clustering.ClusterContingencyTable: Number of clusters.
sizes - Variable in class elki.clustering.hierarchical.ClusterMergeHistory: Cluster size storage.
sizes - Variable in class elki.clustering.kmeans.parallel.KMeansProcessor.Instance: (Partial) cluster sizes
sizes - Variable in class elki.clustering.kmeans.parallel.KMeansProcessor: (Partial) cluster sizes
sizeTolerance - Static variable in class elki.index.preprocessed.fastoptics.RandomProjectedNeighborsAndDensities: Sets used for neighborhood computation should be about minSplitSize Sets are still used if they deviate by less (1+/- sizeTolerance)
SLINK<O> - Class in elki.clustering.hierarchical: Implementation of the efficient Single-Link Algorithm SLINK of R.
SLINK(Distance<? super O>) - Constructor for class elki.clustering.hierarchical.SLINK: Constructor.
SLINK.Par<O> - Class in elki.clustering.hierarchical: Parameterization class.
SLINKHDBSCANLinearMemory<O> - Class in elki.clustering.hierarchical: Linear memory implementation of HDBSCAN clustering based on SLINK.
SLINKHDBSCANLinearMemory(Distance<? super O>, int) - Constructor for class elki.clustering.hierarchical.SLINKHDBSCANLinearMemory: Constructor.
slinkstep3(DBIDRef, DBIDArrayIter, int, WritableDBIDDataStore, WritableDoubleDataStore, WritableDoubleDataStore) - Method in class elki.clustering.hierarchical.SLINK: Third step: Determine the values for P and L
slinkstep4(DBIDRef, DBIDArrayIter, int, WritableDBIDDataStore, WritableDoubleDataStore) - Method in class elki.clustering.hierarchical.SLINK: Fourth step: Actualize the clusters if necessary
smFFirst - Variable in class elki.evaluation.clustering.SetMatchingPurity: Result cache
smFSecond - Variable in class elki.evaluation.clustering.SetMatchingPurity: Result cache
smInversePurity - Variable in class elki.evaluation.clustering.SetMatchingPurity: Result cache
smp - Variable in class elki.evaluation.clustering.ClusterContingencyTable: Set matching purity measures
smPurity - Variable in class elki.evaluation.clustering.SetMatchingPurity: Result cache
SNNClustering<O> - Class in elki.clustering: Shared nearest neighbor clustering.
SNNClustering(SharedNearestNeighborSimilarity<O>, int, int) - Constructor for class elki.clustering.SNNClustering: Constructor.
soft - Variable in class elki.clustering.em.BetulaGMM.Par: Retain soft assignments.
soft - Variable in class elki.clustering.em.BetulaGMM: Retain soft assignments.
soft - Variable in class elki.clustering.em.EM.Par: Retain soft assignments?
soft - Variable in class elki.clustering.em.EM: Retain soft assignments.
soft - Variable in class elki.clustering.em.KDTreeEM.Par: Retain soft assignments?
soft - Variable in class elki.clustering.em.KDTreeEM: Retain soft assignments.
soft - Variable in class elki.clustering.kmeans.FuzzyCMeans.Par: retain soft assignments
soft - Variable in class elki.clustering.kmeans.FuzzyCMeans: Retain soft assignments.
SOFT_ID - Static variable in class elki.clustering.em.EM.Par: Parameter to specify the saving of soft assignments
SOFT_ID - Static variable in class elki.clustering.em.KDTreeEM.Par: Parameter to specify the saving of soft assignments
SOFT_ID - Static variable in class elki.clustering.kmeans.FuzzyCMeans.Par: Parameter to retain soft assignments
SOFT_TYPE - Static variable in class elki.clustering.em.BetulaGMM: Soft assignment result type.
SOFT_TYPE - Static variable in class elki.clustering.em.EM: Soft assignment result type.
SOFT_TYPE - Static variable in class elki.clustering.em.KDTreeEM: Soft assignment result type.
SOFT_TYPE - Static variable in class elki.clustering.kmeans.FuzzyCMeans: Soft assignment result type.
solver - Variable in class elki.clustering.em.KDTreeEM: Solver for quadratic problems
sorted - Variable in class elki.clustering.em.KDTreeEM: kd-tree object order
sorted - Variable in class elki.clustering.kmeans.KDTreePruningKMeans.Instance: The tree stored as ArrayModifiableDBIDs
SortMeans<V extends elki.data.NumberVector> - Class in elki.clustering.kmeans: Sort-Means: Accelerated k-means by exploiting the triangle inequality and pairwise distances of means to prune candidate means (with sorting).
SortMeans(NumberVectorDistance<? super V>, int, int, KMeansInitialization) - Constructor for class elki.clustering.kmeans.SortMeans: Constructor.
SortMeans.Instance - Class in elki.clustering.kmeans: Inner instance, storing state for a single data set.
SortMeans.Par<V extends elki.data.NumberVector> - Class in elki.clustering.kmeans: Parameterization class.
sparseMeans(List<? extends DBIDs>, double[][], Relation<? extends SparseNumberVector>) - Static method in class elki.clustering.kmeans.AbstractKMeans: Returns the mean vectors of the given clusters in the given database.
sparsePlusEquals(double[], SparseNumberVector) - Static method in class elki.clustering.kmeans.AbstractKMeans: Similar to VMath.plusEquals, but for sparse number vectors.
sparsePlusMinusEquals(double[], double[], SparseNumberVector) - Static method in class elki.clustering.kmeans.AbstractKMeans: Add to one, remove from another.
spec - Variable in class elki.clustering.subspace.P3C.Signature: Subspace specification
SphericalAFKMC2 - Class in elki.clustering.kmeans.initialization: Spherical K-Means++ initialization with markov chains.
SphericalAFKMC2(int, double, RandomFactory) - Constructor for class elki.clustering.kmeans.initialization.SphericalAFKMC2: Constructor.
SphericalAFKMC2.Instance - Class in elki.clustering.kmeans.initialization: Abstract instance implementing the weight handling.
SphericalAFKMC2.Par - Class in elki.clustering.kmeans.initialization: Parameterization class.
SphericalElkanKMeans<V extends elki.data.NumberVector> - Class in elki.clustering.kmeans.spherical: Elkan's fast k-means by exploiting the triangle inequality.
SphericalElkanKMeans(int, int, KMeansInitialization, boolean) - Constructor for class elki.clustering.kmeans.spherical.SphericalElkanKMeans: Constructor.
SphericalElkanKMeans.Instance - Class in elki.clustering.kmeans.spherical: Inner instance, storing state for a single data set.
SphericalElkanKMeans.Par<V extends elki.data.NumberVector> - Class in elki.clustering.kmeans.spherical: Parameterization class.
SphericalGaussianModel - Class in elki.clustering.em.models: Simple spherical Gaussian cluster (scaled identity matrixes).
SphericalGaussianModel(double, double[]) - Constructor for class elki.clustering.em.models.SphericalGaussianModel: Constructor.
SphericalGaussianModel(double, double[], double) - Constructor for class elki.clustering.em.models.SphericalGaussianModel: Constructor.
SphericalGaussianModelFactory - Class in elki.clustering.em.models: Factory for EM with multivariate gaussian models using a single variance.
SphericalGaussianModelFactory(KMeansInitialization) - Constructor for class elki.clustering.em.models.SphericalGaussianModelFactory: Constructor.
SphericalHamerlyKMeans<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.
SphericalHamerlyKMeans(int, int, KMeansInitialization, boolean) - Constructor for class elki.clustering.kmeans.spherical.SphericalHamerlyKMeans: Constructor.
SphericalHamerlyKMeans.Instance - Class in elki.clustering.kmeans.spherical: Inner instance, storing state for a single data set.
SphericalHamerlyKMeans.Par<V extends elki.data.NumberVector> - Class in elki.clustering.kmeans.spherical: Parameterization class.
SphericalKMeans<V extends elki.data.NumberVector> - Class in elki.clustering.kmeans.spherical: The standard spherical k-means algorithm.
SphericalKMeans(int, int, KMeansInitialization) - Constructor for class elki.clustering.kmeans.spherical.SphericalKMeans: Constructor.
SphericalKMeans.Instance - Class in elki.clustering.kmeans.spherical: Instance for a particular data set.
SphericalKMeans.Par<V extends elki.data.NumberVector> - Class in elki.clustering.kmeans.spherical: Parameterization class.
SphericalKMeansPlusPlus<O> - Class in elki.clustering.kmeans.initialization: Spherical K-Means++ initialization for k-means.
SphericalKMeansPlusPlus(double, RandomFactory) - Constructor for class elki.clustering.kmeans.initialization.SphericalKMeansPlusPlus: Constructor.
SphericalKMeansPlusPlus.Instance - Class in elki.clustering.kmeans.initialization: Abstract instance implementing the weight handling.
SphericalKMeansPlusPlus.Par<V> - Class in elki.clustering.kmeans.initialization: Parameterization class.
SphericalSimplifiedElkanKMeans<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.
SphericalSimplifiedElkanKMeans(int, int, KMeansInitialization, boolean) - Constructor for class elki.clustering.kmeans.spherical.SphericalSimplifiedElkanKMeans: Constructor.
SphericalSimplifiedElkanKMeans.Instance - Class in elki.clustering.kmeans.spherical: Inner instance, storing state for a single data set.
SphericalSimplifiedElkanKMeans.Par<V extends elki.data.NumberVector> - Class in elki.clustering.kmeans.spherical: Parameterization class.
SphericalSimplifiedHamerlyKMeans<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.
SphericalSimplifiedHamerlyKMeans(int, int, KMeansInitialization, boolean) - Constructor for class elki.clustering.kmeans.spherical.SphericalSimplifiedHamerlyKMeans: Constructor.
SphericalSimplifiedHamerlyKMeans.Instance - Class in elki.clustering.kmeans.spherical: Inner instance, storing state for a single data set.
SphericalSimplifiedHamerlyKMeans.Par<V extends elki.data.NumberVector> - Class in elki.clustering.kmeans.spherical: Parameterization class.
SphericalSingleAssignmentKMeans<V extends elki.data.NumberVector> - Class in elki.clustering.kmeans.spherical: Pseudo-k-Means variations, that assigns each object to the nearest center.
SphericalSingleAssignmentKMeans(int, KMeansInitialization) - Constructor for class elki.clustering.kmeans.spherical.SphericalSingleAssignmentKMeans: Constructor.
SphericalSingleAssignmentKMeans.Instance - Class in elki.clustering.kmeans.spherical: Instance for a particular data set.
SphericalSingleAssignmentKMeans.Par<V extends elki.data.NumberVector> - Class in elki.clustering.kmeans.spherical: Parameterization class.
split - Variable in class elki.clustering.kmeans.KDTreePruningKMeans.Par: Splitting strategy.
split - Variable in class elki.clustering.kmeans.KDTreePruningKMeans: Splitting strategy.
split(CFTree.TreeNode, ClusteringFeature) - Method in class elki.clustering.hierarchical.birch.CFTree: Split an overfull node.
split(CFNode<L>, AsClusterFeature) - Method in class elki.index.tree.betula.CFTree: Split an overfull node.
Split() - Constructor for enum elki.clustering.kmeans.KDTreePruningKMeans.Split
SPLIT_ID - Static variable in class elki.clustering.kmeans.KDTreePruningKMeans.Par: Option ID for the splitting strategy.
SPLIT_ID - Static variable in class elki.index.tree.betula.CFTree.Factory.Par: Option ID for threshold heuristic.
splitByDistance(ArrayModifiableDBIDs, int, int, DoubleDataStore, Random) - Method in class elki.index.preprocessed.fastoptics.RandomProjectedNeighborsAndDensities: Split the data set by distances.
splitCentroid(Cluster<? extends MeanModel>, Relation<V>) - Method in class elki.clustering.kmeans.GMeans
splitCentroid(Cluster<? extends MeanModel>, Relation<V>) - Method in class elki.clustering.kmeans.XMeans: Split an existing centroid into two initial centers.
splitCluster(Cluster<M>, Relation<V>) - Method in class elki.clustering.kmeans.GMeans
splitCluster(Cluster<M>, Relation<V>) - Method in class elki.clustering.kmeans.XMeans: Conditionally splits the clusters based on the information criterion.
splitInitializer - Variable in class elki.clustering.kmeans.XMeans: Initializer for k-means.
splitRandomly(ArrayModifiableDBIDs, int, int, DoubleDataStore, Random) - Method in class elki.index.preprocessed.fastoptics.RandomProjectedNeighborsAndDensities: Split the data set randomly.
splitsets - Variable in class elki.index.preprocessed.fastoptics.RandomProjectedNeighborsAndDensities: sets that resulted from recursive split of entire point set
splitupNoSort(ArrayModifiableDBIDs, int, int, int, Random) - Method in class elki.index.preprocessed.fastoptics.RandomProjectedNeighborsAndDensities: Recursively splits entire point set until the set is below a threshold
sqabsorption(NumberVector, ClusterFeature) - Method in class elki.index.tree.betula.CFTree: Updates statistics and calculates distance between a Number Vector and a Cluster Feature based on selected criteria.
sqabsorption(ClusterFeature, ClusterFeature) - Method in class elki.index.tree.betula.CFTree: Updates statistics and calculates distance between two Cluster Features based on selected criteria.
sqdistance(NumberVector, ClusterFeature) - Method in class elki.index.tree.betula.CFTree: Updates statistics and calculates distance between a Number Vector and a Cluster Feature based on selected criteria.
sqdistance(ClusterFeature, ClusterFeature) - Method in class elki.index.tree.betula.CFTree: Updates statistics and calculates distance between two Cluster Features based on selected criteria.
sqrtdistance(double[], double[]) - Method in class elki.clustering.kmeans.AbstractKMeans.Instance: Compute the distance (and count the distance computations).
sqrtdistance(NumberVector, double[]) - Method in class elki.clustering.kmeans.AbstractKMeans.Instance: Compute the distance (and count the distance computations).
sqrtdistance(NumberVector, double[]) - Method in class elki.clustering.kmeans.spherical.SphericalKMeans.Instance
sqrtdistance(NumberVector, NumberVector) - Method in class elki.clustering.kmeans.AbstractKMeans.Instance: Compute the distance (and count the distance computations).
sqrtdistance(NumberVector, NumberVector) - Method in class elki.clustering.kmeans.spherical.SphericalKMeans.Instance
squaredCriterion(ClusteringFeature, ClusteringFeature) - Method in interface elki.clustering.hierarchical.birch.BIRCHAbsorptionCriterion: Quality when merging two CFs.
squaredCriterion(ClusteringFeature, ClusteringFeature) - Method in class elki.clustering.hierarchical.birch.DiameterCriterion
squaredCriterion(ClusteringFeature, ClusteringFeature) - Method in class elki.clustering.hierarchical.birch.EuclideanDistanceCriterion
squaredCriterion(ClusteringFeature, ClusteringFeature) - Method in class elki.clustering.hierarchical.birch.RadiusCriterion
squaredCriterion(ClusteringFeature, NumberVector) - Method in interface elki.clustering.hierarchical.birch.BIRCHAbsorptionCriterion: Quality of a CF when adding a data point
squaredCriterion(ClusteringFeature, NumberVector) - Method in class elki.clustering.hierarchical.birch.DiameterCriterion
squaredCriterion(ClusteringFeature, NumberVector) - Method in class elki.clustering.hierarchical.birch.EuclideanDistanceCriterion
squaredCriterion(ClusteringFeature, NumberVector) - Method in class elki.clustering.hierarchical.birch.RadiusCriterion
squaredDistance(ClusteringFeature, ClusteringFeature) - Method in class elki.clustering.hierarchical.birch.AverageInterclusterDistance
squaredDistance(ClusteringFeature, ClusteringFeature) - Method in class elki.clustering.hierarchical.birch.AverageIntraclusterDistance
squaredDistance(ClusteringFeature, ClusteringFeature) - Method in interface elki.clustering.hierarchical.birch.BIRCHDistance: Distance between two clustering features.
squaredDistance(ClusteringFeature, ClusteringFeature) - Method in class elki.clustering.hierarchical.birch.CentroidEuclideanDistance
squaredDistance(ClusteringFeature, ClusteringFeature) - Method in class elki.clustering.hierarchical.birch.CentroidManhattanDistance
squaredDistance(ClusteringFeature, ClusteringFeature) - Method in class elki.clustering.hierarchical.birch.VarianceIncreaseDistance
squaredDistance(NumberVector, ClusteringFeature) - Method in class elki.clustering.hierarchical.birch.AverageInterclusterDistance
squaredDistance(NumberVector, ClusteringFeature) - Method in class elki.clustering.hierarchical.birch.AverageIntraclusterDistance
squaredDistance(NumberVector, ClusteringFeature) - Method in interface elki.clustering.hierarchical.birch.BIRCHDistance: Distance of a vector to a clustering feature.
squaredDistance(NumberVector, ClusteringFeature) - Method in class elki.clustering.hierarchical.birch.CentroidEuclideanDistance
squaredDistance(NumberVector, ClusteringFeature) - Method in class elki.clustering.hierarchical.birch.CentroidManhattanDistance
squaredDistance(NumberVector, ClusteringFeature) - Method in class elki.clustering.hierarchical.birch.VarianceIncreaseDistance
squaredDistance(NumberVector, ClusterFeature) - Method in class elki.index.tree.betula.distance.AverageInterclusterDistance
squaredDistance(NumberVector, ClusterFeature) - Method in class elki.index.tree.betula.distance.AverageIntraclusterDistance
squaredDistance(NumberVector, ClusterFeature) - Method in class elki.index.tree.betula.distance.BIRCHAverageInterclusterDistance
squaredDistance(NumberVector, ClusterFeature) - Method in class elki.index.tree.betula.distance.BIRCHAverageIntraclusterDistance
squaredDistance(NumberVector, ClusterFeature) - Method in class elki.index.tree.betula.distance.BIRCHRadiusDistance
squaredDistance(NumberVector, ClusterFeature) - Method in class elki.index.tree.betula.distance.BIRCHVarianceIncreaseDistance
squaredDistance(NumberVector, ClusterFeature) - Method in class elki.index.tree.betula.distance.CentroidEuclideanDistance
squaredDistance(NumberVector, ClusterFeature) - Method in class elki.index.tree.betula.distance.CentroidManhattanDistance
squaredDistance(NumberVector, ClusterFeature) - Method in interface elki.index.tree.betula.distance.CFDistance: Distance of a vector to a clustering feature.
squaredDistance(NumberVector, ClusterFeature) - Method in class elki.index.tree.betula.distance.RadiusDistance
squaredDistance(NumberVector, ClusterFeature) - Method in class elki.index.tree.betula.distance.VarianceIncreaseDistance
squaredDistance(ClusterFeature, ClusterFeature) - Method in class elki.index.tree.betula.distance.AverageInterclusterDistance
squaredDistance(ClusterFeature, ClusterFeature) - Method in class elki.index.tree.betula.distance.AverageIntraclusterDistance
squaredDistance(ClusterFeature, ClusterFeature) - Method in class elki.index.tree.betula.distance.BIRCHAverageInterclusterDistance
squaredDistance(ClusterFeature, ClusterFeature) - Method in class elki.index.tree.betula.distance.BIRCHAverageIntraclusterDistance
squaredDistance(ClusterFeature, ClusterFeature) - Method in class elki.index.tree.betula.distance.BIRCHRadiusDistance
squaredDistance(ClusterFeature, ClusterFeature) - Method in class elki.index.tree.betula.distance.BIRCHVarianceIncreaseDistance
squaredDistance(ClusterFeature, ClusterFeature) - Method in class elki.index.tree.betula.distance.CentroidEuclideanDistance
squaredDistance(ClusterFeature, ClusterFeature) - Method in class elki.index.tree.betula.distance.CentroidManhattanDistance
squaredDistance(ClusterFeature, ClusterFeature) - Method in interface elki.index.tree.betula.distance.CFDistance: Distance between two clustering features.
squaredDistance(ClusterFeature, ClusterFeature) - Method in class elki.index.tree.betula.distance.RadiusDistance
squaredDistance(ClusterFeature, ClusterFeature) - Method in class elki.index.tree.betula.distance.VarianceIncreaseDistance
SquaredErrors - Class in elki.evaluation.clustering.internal: Evaluate a clustering by reporting the squared errors (SSE, SSQ), as used by k-means.
SquaredErrors(NumberVectorDistance<?>, NoiseHandling) - Constructor for class elki.evaluation.clustering.internal.SquaredErrors: Constructor.
SquaredErrors.Par - Class in elki.evaluation.clustering.internal: Parameterization class.
SquaredEuclideanWeight - Class in elki.clustering.kmeans.initialization.betula: Use the squared Euclidean distance only for distance measurement.
SquaredEuclideanWeight() - Constructor for class elki.clustering.kmeans.initialization.betula.SquaredEuclideanWeight
squaredWeight(ClusterFeature, ClusterFeature) - Method in interface elki.clustering.kmeans.initialization.betula.CFInitWeight: Distance between two clustering features.
squaredWeight(ClusterFeature, ClusterFeature) - Method in class elki.clustering.kmeans.initialization.betula.InterclusterWeight
squaredWeight(ClusterFeature, ClusterFeature) - Method in class elki.clustering.kmeans.initialization.betula.SquaredEuclideanWeight
squaredWeight(ClusterFeature, ClusterFeature) - Method in class elki.clustering.kmeans.initialization.betula.VarianceWeight
ss - Variable in class elki.clustering.hierarchical.birch.ClusteringFeature: Sum of squares (see original thesis, this is a scalar).
ss - Variable in class elki.index.tree.betula.features.BIRCHCF: Sum of squares (see original thesis, this is a scalar).
ssd - Variable in class elki.index.tree.betula.features.VIIFeature: Sum of Squared Deviations.
ssd - Variable in class elki.index.tree.betula.features.VVIFeature: Sum of Squared Deviations.
ssd - Variable in class elki.index.tree.betula.features.VVVFeature: Sum of Squared Deviations.
SSQ - elki.clustering.kmeans.KDTreePruningKMeans.Split: Split to minimize the sum-of-squares of the partitions.
start - Variable in class elki.clustering.kmeans.KDTreePruningKMeans.KDNode: First index of child nodes.
startindex - Variable in class elki.clustering.optics.OPTICSXi.SteepArea: Start index of steep area
startIndex - Variable in class elki.data.model.OPTICSModel: Start index
STATIC - Static variable in class elki.clustering.hierarchical.birch.AverageInterclusterDistance: Static instance.
STATIC - Static variable in class elki.clustering.hierarchical.birch.AverageIntraclusterDistance: Static instance.
STATIC - Static variable in class elki.clustering.hierarchical.birch.CentroidEuclideanDistance: Static instance.
STATIC - Static variable in class elki.clustering.hierarchical.birch.CentroidManhattanDistance: Static instance.
STATIC - Static variable in class elki.clustering.hierarchical.birch.DiameterCriterion: Static instance.
STATIC - Static variable in class elki.clustering.hierarchical.birch.EuclideanDistanceCriterion: Static instance.
STATIC - Static variable in class elki.clustering.hierarchical.birch.RadiusCriterion: Static instance.
STATIC - Static variable in class elki.clustering.hierarchical.birch.VarianceIncreaseDistance: Static instance.
STATIC - Static variable in class elki.clustering.hierarchical.linkage.CentroidLinkage: Static instance of class.
STATIC - Static variable in class elki.clustering.hierarchical.linkage.CompleteLinkage: Static instance of class.
STATIC - Static variable in class elki.clustering.hierarchical.linkage.GroupAverageLinkage: Static instance of class.
STATIC - Static variable in class elki.clustering.hierarchical.linkage.MedianLinkage: Static instance of class.
STATIC - Static variable in class elki.clustering.hierarchical.linkage.MinimumVarianceLinkage: Static instance of class.
STATIC - Static variable in class elki.clustering.hierarchical.linkage.SingleLinkage: Static instance of class.
STATIC - Static variable in class elki.clustering.hierarchical.linkage.WardLinkage: Static instance of class.
STATIC - Static variable in class elki.clustering.hierarchical.linkage.WeightedAverageLinkage: Static instance of class.
STATIC - Static variable in class elki.index.tree.betula.distance.AverageInterclusterDistance: Static instance.
STATIC - Static variable in class elki.index.tree.betula.distance.AverageIntraclusterDistance: Static instance.
STATIC - Static variable in class elki.index.tree.betula.distance.BIRCHAverageInterclusterDistance: Static instance.
STATIC - Static variable in class elki.index.tree.betula.distance.BIRCHAverageIntraclusterDistance: Static instance.
STATIC - Static variable in class elki.index.tree.betula.distance.BIRCHRadiusDistance: Static instance.
STATIC - Static variable in class elki.index.tree.betula.distance.BIRCHVarianceIncreaseDistance: Static instance.
STATIC - Static variable in class elki.index.tree.betula.distance.CentroidEuclideanDistance: Static instance.
STATIC - Static variable in class elki.index.tree.betula.distance.CentroidManhattanDistance: Static instance.
STATIC - Static variable in class elki.index.tree.betula.distance.RadiusDistance: Static instance.
STATIC - Static variable in class elki.index.tree.betula.distance.VarianceIncreaseDistance: Static instance.
STATIC - Static variable in class elki.index.tree.betula.features.BIRCHCF.Factory: Static instance.
STATIC - Static variable in class elki.index.tree.betula.features.VIIFeature.Factory: Static instance.
STATIC - Static variable in class elki.index.tree.betula.features.VVIFeature.Factory: Static instance.
STATIC - Static variable in class elki.index.tree.betula.features.VVVFeature.Factory: Static instance.
STATIC - Static variable in class elki.similarity.cluster.ClusteringAdjustedRandIndexSimilarity: Static instance.
STATIC - Static variable in class elki.similarity.cluster.ClusteringBCubedF1Similarity: Static instance.
STATIC - Static variable in class elki.similarity.cluster.ClusteringFowlkesMallowsSimilarity: Static instance.
STATIC - Static variable in class elki.similarity.cluster.ClusteringRandIndexSimilarity: Static instance.
STATIC - Static variable in class elki.similarity.cluster.ClusterIntersectionSimilarity: Static instance.
STATIC - Static variable in class elki.similarity.cluster.ClusterJaccardSimilarity: Static instance.
SteepArea(int, int, double) - Constructor for class elki.clustering.optics.OPTICSXi.SteepArea: Constructor.
SteepAreaResult(Collection<OPTICSXi.SteepArea>) - Constructor for class elki.clustering.optics.OPTICSXi.SteepAreaResult: Constructor.
steepDown(double) - Method in class elki.clustering.optics.OPTICSXi.SteepScanPosition: Test for a steep down area.
SteepDownArea(int, int, double, double) - Constructor for class elki.clustering.optics.OPTICSXi.SteepDownArea: Constructor
SteepScanPosition(ClusterOrder) - Constructor for class elki.clustering.optics.OPTICSXi.SteepScanPosition: Constructor.
steepUp(double) - Method in class elki.clustering.optics.OPTICSXi.SteepScanPosition: Test for a steep up point.
SteepUpArea(int, int, double) - Constructor for class elki.clustering.optics.OPTICSXi.SteepUpArea: Constructor
step2(DBIDRef, DBIDArrayIter, int, DistanceQuery<? super O>, WritableDoubleDataStore) - Method in class elki.clustering.hierarchical.SLINK: Second step: Determine the pairwise distances from all objects in the pointer representation to the new object with the specified id.
step2(DBIDRef, DBIDs, DistanceQuery<? super O>, DoubleDataStore, WritableDoubleDataStore) - Method in class elki.clustering.hierarchical.SLINKHDBSCANLinearMemory: Second step: Determine the pairwise distances from all objects in the pointer representation to the new object with the specified id.
step2primitive(DBIDRef, DBIDArrayIter, int, Relation<? extends O>, PrimitiveDistance<? super O>, WritableDoubleDataStore) - Method in class elki.clustering.hierarchical.SLINK: Second step: Determine the pairwise distances from all objects in the pointer representation to the new object with the specified id.
step3(DBIDRef, WritableDBIDDataStore, WritableDoubleDataStore, DBIDs, WritableDoubleDataStore) - Method in class elki.clustering.hierarchical.SLINKHDBSCANLinearMemory: Third step: Determine the values for P and L
step4(DBIDRef, WritableDBIDDataStore, WritableDoubleDataStore, DBIDs) - Method in class elki.clustering.hierarchical.SLINKHDBSCANLinearMemory: Fourth step: Actualize the clusters if necessary
storage - Variable in class elki.clustering.dbscan.predicates.AbstractRangeQueryNeighborPredicate.Instance: Model storage.
STORE_IDS_ID - Static variable in class elki.clustering.BetulaLeafPreClustering.Par: Option to store ids rather than reassigning.
STORE_IDS_ID - Static variable in class elki.clustering.kmeans.BetulaLloydKMeans.Par: Option to store ids rather than reassigning.
storeIds - Variable in class elki.clustering.BetulaLeafPreClustering.Par: Store ids
storeIds - Variable in class elki.clustering.BetulaLeafPreClustering: Store ids
storeIds - Variable in class elki.clustering.kmeans.BetulaLloydKMeans.Par: Store ids
storeIds - Variable in class elki.clustering.kmeans.BetulaLloydKMeans: Store ids
strictAdd(int, double, int) - Method in class elki.clustering.hierarchical.ClusterMergeHistoryBuilder: Add a merge to the pointer representation.
strictAdd(int, double, int, DBIDRef) - Method in class elki.clustering.hierarchical.ClusterMergeHistoryBuilder: Add an element to the pointer representation.
strongNeighbors(NumberVector, NumberVector, PCAFilteredResult, PCAFilteredResult) - Method in class elki.clustering.dbscan.predicates.ERiCNeighborPredicate.Instance: Computes the distance between two given DatabaseObjects according to this distance function.
SUBCLU<V extends elki.data.NumberVector> - Class in elki.clustering.subspace: Implementation of the SUBCLU algorithm, an algorithm to detect arbitrarily shaped and positioned clusters in subspaces.
SUBCLU(DimensionSelectingSubspaceDistance<V>, double, int, int) - Constructor for class elki.clustering.subspace.SUBCLU: Constructor.
SUBCLU.Par<V extends elki.data.NumberVector> - Class in elki.clustering.subspace: Parameterization class.
subspace - Variable in class elki.data.model.SubspaceModel: The subspace of the cluster.
Subspace - Class in elki.data: Represents a subspace of the original data space.
Subspace(int) - Constructor for class elki.data.Subspace: Creates a new one-dimensional subspace of the original data space.
Subspace(long[]) - Constructor for class elki.data.Subspace: Creates a new k-dimensional subspace of the original data space.
SubspaceClusteringAlgorithm<M extends SubspaceModel> - Interface in elki.clustering.subspace: Interface for subspace clustering algorithms that use a model derived from SubspaceModel, that can then be post-processed for outlier detection.
SubspaceModel - Class in elki.data.model: Model for Subspace Clusters.
SubspaceModel(Subspace, double[]) - Constructor for class elki.data.model.SubspaceModel: Creates a new SubspaceModel for the specified subspace with the given cluster mean.
sum - Variable in class elki.clustering.em.KDTreeEM.KDTree: Sum of contained vectors
sum - Variable in class elki.clustering.kmeans.KDTreePruningKMeans.KDNode: Sum of all points associated with this node
sumdev() - Method in class elki.index.tree.betula.features.BIRCHCF
sumdev() - Method in interface elki.index.tree.betula.features.ClusterFeature: Returns the total sum of Deviations.
sumdev() - Method in class elki.index.tree.betula.features.VIIFeature
sumdev() - Method in class elki.index.tree.betula.features.VVIFeature
sumdev() - Method in class elki.index.tree.betula.features.VVVFeature
sumOfSquaredDev() - Method in class elki.index.tree.betula.features.VIIFeature: Sum of Squared Deviations.
sumOfSquaredDev(int) - Method in class elki.index.tree.betula.features.VVIFeature: Sum of Squared Deviations.
sumOfSquares(NumberVector) - Static method in class elki.clustering.hierarchical.birch.ClusteringFeature: Compute the sum of squares of a vector.
sumOfSquares(NumberVector) - Static method in class elki.index.tree.betula.features.BIRCHCF: Compute the sum of squares of a vector.
sumOfSquaresOfSums() - Method in class elki.clustering.hierarchical.birch.ClusteringFeature: Sum over all dimensions of squares of linear sums.
sumOfSquaresOfSums() - Method in class elki.index.tree.betula.features.BIRCHCF: Sum over all dimensions of squares of linear sums.
sumOfSumOfSquares() - Method in class elki.clustering.hierarchical.birch.ClusteringFeature: Sum over all dimensions of sums of squares.
sumOfSumOfSquares() - Method in class elki.index.tree.betula.features.BIRCHCF: Sum over all dimensions of sums of squares.
sums - Variable in class elki.clustering.kmeans.HamerlyKMeans.Instance: Sum aggregate for the new mean.
sums - Variable in class elki.clustering.kmeans.SimplifiedElkanKMeans.Instance: Sum aggregate for the new mean.
sums - Variable in class elki.clustering.kmeans.spherical.EuclideanSphericalHamerlyKMeans.Instance: Sum aggregate for the new mean.
sums - Variable in class elki.clustering.kmeans.spherical.EuclideanSphericalSimplifiedElkanKMeans.Instance: Sum aggregate for the new mean.
sums - Variable in class elki.clustering.kmeans.spherical.SphericalHamerlyKMeans.Instance: Sum aggregate for the new mean.
sums - Variable in class elki.clustering.kmeans.spherical.SphericalSimplifiedElkanKMeans.Instance: Sum aggregate for the new mean.
sums - Variable in class elki.clustering.kmeans.spherical.SphericalSimplifiedHamerlyKMeans.Instance: Sum aggregate for the new mean.
sums - Variable in class elki.clustering.kmeans.YinYangKMeans.Instance: Current cluster sum.
sumSq - Variable in class elki.clustering.em.KDTreeEM.KDTree: Sum over all squared elements (x^T * x), needed for covariance calculation

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