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

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K

k - Variable in class elki.algorithm.KNNDistancesSampler: Parameter k.
k - Variable in class elki.algorithm.KNNDistancesSampler.Par: Parameter k.
k - Variable in class elki.algorithm.KNNJoin: The k parameter.
k - Variable in class elki.algorithm.KNNJoin.Par: K parameter.
k - Variable in class elki.algorithm.statistics.AveragePrecisionAtK: The parameter k - the number of neighbors to retrieve.
k - Variable in class elki.algorithm.statistics.HopkinsStatisticClusteringTendency: Nearest neighbor to use.
k - Variable in class elki.algorithm.statistics.HopkinsStatisticClusteringTendency.Par: Nearest neighbor number.
k - Variable in class elki.application.benchmark.KNNBenchmark: Number of neighbors to retrieve.
k - Variable in class elki.application.benchmark.PrioritySearchBenchmark: Number of neighbors to retrieve.
k - Variable in class elki.application.benchmark.ValidateApproximativeKNNIndex: Number of neighbors to retrieve.
k - Variable in class elki.application.cache.CacheDoubleDistanceKNNLists: Number of neighbors to precompute.
k - Variable in class elki.application.cache.CacheDoubleDistanceKNNLists.Par: Number of neighbors to precompute.
k - Variable in class elki.application.experiments.ORLibBenchmark: Number of clusters override (optional)
k - Variable in class elki.application.experiments.ORLibBenchmark.Par: Number of clusters override (optional)
k - Variable in class elki.classification.KNNClassifier: Holds the value of @link #K_PARAM}.
k - Variable in class elki.clustering.AbstractProjectedClustering: The number of clusters to find
k - Variable in class elki.clustering.AbstractProjectedClustering.Par: The number of clusters to find
k - Variable in class elki.clustering.CFSFDP: Number of clusters to find.
k - Variable in class elki.clustering.CFSFDP.Par: Number of clusters to find.
k - Variable in class elki.clustering.correlation.COPAC.Settings: Neighborhood size.
k - Variable in class elki.clustering.correlation.ERiC.Settings: Neighborhood size.
k - Variable in class elki.clustering.correlation.HiCO: Number of neighbors to query.
k - Variable in class elki.clustering.correlation.HiCO.Par: Number of neighbors to query.
k - Variable in class elki.clustering.dbscan.LSDBC.Par: kNN parameter.
k - Variable in class elki.clustering.em.BetulaGMM: Number of cluster centers to initialize.
k - Variable in class elki.clustering.em.BetulaGMM.Par: k Parameter.
k - Variable in class elki.clustering.em.EM: Number of clusters
k - Variable in class elki.clustering.em.EM.Par: Number of clusters.
k - Variable in class elki.clustering.em.KDTreeEM: number of models
k - Variable in class elki.clustering.em.KDTreeEM.Par: Number of clusters.
k - Variable in class elki.clustering.hierarchical.birch.BIRCHLloydKMeans: Number of cluster centers to initialize.
k - Variable in class elki.clustering.hierarchical.birch.BIRCHLloydKMeans.Par: k Parameter.
k - Variable in class elki.clustering.kcenter.GreedyKCenter: number of clusters
k - Variable in class elki.clustering.kcenter.GreedyKCenter.Par: number of clusters
k - Variable in class elki.clustering.kmeans.AbstractKMeans.Instance: Number of clusters.
k - Variable in class elki.clustering.kmeans.AbstractKMeans: Number of cluster centers to initialize.
k - Variable in class elki.clustering.kmeans.AbstractKMeans.Par: k Parameter.
k - Variable in class elki.clustering.kmeans.BisectingKMeans: Desired value of k.
k - Variable in class elki.clustering.kmeans.FuzzyCMeans: Number of clusters
k - Variable in class elki.clustering.kmeans.FuzzyCMeans.Par: Number of clusters.
k - Variable in class elki.clustering.kmedoids.AlternatingKMedoids: Number of clusters to find.
k - Variable in class elki.clustering.kmedoids.AlternatingKMedoids.Par: The number of clusters to find
k - Variable in class elki.clustering.kmedoids.CLARANS: Number of clusters to find.
k - Variable in class elki.clustering.kmedoids.CLARANS.Par: Number of cluster centers to find.
k - Variable in class elki.clustering.kmedoids.PAM: The number of clusters to produce.
k - Variable in class elki.clustering.kmedoids.PAM.Par: The number of clusters to produce.
k - Variable in class elki.clustering.onedimensional.KNNKernelDensityMinimaClustering: Number of neighbors to use for bandwidth.
k - Variable in class elki.clustering.onedimensional.KNNKernelDensityMinimaClustering.Par: Number of neighbors to use for bandwidth.
k - Variable in class elki.clustering.subspace.HiSC: The number of nearest neighbors considered to determine the preference vector.
k - Variable in class elki.clustering.subspace.HiSC.Par: The number of nearest neighbors considered to determine the preference vector.
k - Variable in class elki.clustering.uncertain.UKMeans: Number of cluster centers to initialize.
k - Variable in class elki.clustering.uncertain.UKMeans.Par: Number of cluster centers to initialize.
k - Variable in class elki.data.projection.random.SimplifiedRandomHyperplaneProjectionFamily.SignedProjection: Output dimensionality
k - Variable in class elki.database.ids.integer.DoubleIntegerDBIDKNNHeap: k for this heap.
k - Variable in class elki.database.ids.integer.DoubleIntegerDBIDKNNList: The k value this list was generated for.
k - Variable in class elki.database.ids.integer.IntegerDBIDKNNSubList: Parameter k.
k - Variable in class elki.datasource.filter.transform.NumberVectorRandomFeatureSelectionFilter: Holds the desired cardinality of the subset of attributes selected for projection.
k - Variable in class elki.datasource.filter.transform.NumberVectorRandomFeatureSelectionFilter.Par: Number of attributes to select.
k - Variable in class elki.evaluation.scores.PrecisionAtKEvaluation: Parameter k.
k - Variable in class elki.evaluation.scores.PrecisionAtKEvaluation.Par: K parameter
k - Variable in class elki.index.idistance.InMemoryIDistanceIndex.Factory: Number of reference points
k - Variable in class elki.index.laesa.LAESA.Factory: Condition parameter
k - Variable in class elki.index.laesa.LAESA.Factory.Par: condition parameter
k - Variable in class elki.index.laesa.LAESA: Condition parameter
k - Variable in class elki.index.lsh.hashfamilies.AbstractProjectedHashFunctionFamily: The number of projections to use for each hash function.
k - Variable in class elki.index.lsh.hashfamilies.AbstractProjectedHashFunctionFamily.Par: The number of projections to use for each hash function.
k - Variable in class elki.index.lsh.hashfamilies.CosineHashFunctionFamily: The number of projections to use for each hash function.
k - Variable in class elki.index.lsh.hashfamilies.CosineHashFunctionFamily.Par: The number of projections to use for each hash function.
k - Variable in class elki.index.preprocessed.knn.AbstractMaterializeKNNPreprocessor.Factory: Holds the value of AbstractMaterializeKNNPreprocessor.Factory.K_ID.
k - Variable in class elki.index.preprocessed.knn.AbstractMaterializeKNNPreprocessor: The query k value.
k - Variable in class elki.math.statistics.distribution.ExpGammaDistribution: Alpha == k.
k - Variable in class elki.math.statistics.distribution.ExpGammaDistribution.Par: Alpha == k.
k - Variable in class elki.math.statistics.distribution.GammaDistribution: Alpha == k
k - Variable in class elki.math.statistics.distribution.GammaDistribution.Par: Parameters.
k - Variable in class elki.math.statistics.distribution.GeneralizedExtremeValueDistribution: Parameters (location, scale, shape)
k - Variable in class elki.math.statistics.distribution.GeneralizedExtremeValueDistribution.Par: Parameters.
k - Variable in class elki.math.statistics.distribution.LogGammaDistribution: Alpha == k.
k - Variable in class elki.math.statistics.distribution.LogGammaDistribution.Par: Parameters.
k - Variable in class elki.math.statistics.distribution.WeibullDistribution: Shape parameter k.
k - Variable in class elki.math.statistics.distribution.WeibullDistribution.Par: Parameters.
k - Variable in class elki.outlier.anglebased.FastABOD: Number of nearest neighbors.
k - Variable in class elki.outlier.anglebased.FastABOD.Par: Number of neighbors.
k - Variable in class elki.outlier.clustering.EMOutlier: Number of clusters
k - Variable in class elki.outlier.clustering.EMOutlier.Par: Number of clusters.
k - Variable in class elki.outlier.COP: Number of neighbors to be considered.
k - Variable in class elki.outlier.COP.Par: Number of neighbors to be considered.
k - Variable in class elki.outlier.distance.HilOut: Number of nearest neighbors
k - Variable in class elki.outlier.distance.KNNDD.Par: k parameter
k - Variable in class elki.outlier.distance.KNNOutlier.Par: k parameter
k - Variable in class elki.outlier.distance.KNNSOS: Number of neighbors (not including query point).
k - Variable in class elki.outlier.distance.KNNWeightOutlier.Par: k parameter
k - Variable in class elki.outlier.distance.LocalIsolationCoefficient.Par: k parameter
k - Variable in class elki.outlier.distance.parallel.KNNWeightProcessor.Instance: k Parameter
k - Variable in class elki.outlier.distance.parallel.KNNWeightProcessor: K parameter
k - Variable in class elki.outlier.distance.ReferenceBasedOutlierDetection: Holds the number of neighbors to use for density estimation.
k - Variable in class elki.outlier.distance.ReferenceBasedOutlierDetection.Par: Number of nearest neighbors.
k - Variable in class elki.outlier.intrinsic.ISOS: Number of neighbors (not including query point).
k - Variable in class elki.outlier.intrinsic.LID.Par: Number of neighbors to use for ID estimation.
k - Variable in class elki.outlier.lof.COF: The number of neighbors to query (including the query point!)
k - Variable in class elki.outlier.lof.INFLO.Par: Number of neighbors to use.
k - Variable in class elki.outlier.lof.LDOF.Par: Number of neighbors to use
k - Variable in class elki.outlier.meta.FeatureBagging: The parameters k for LOF.
k - Variable in class elki.outlier.meta.FeatureBagging.Par: The neighborhood size to use.
k - Variable in class elki.outlier.SimpleCOP.Par: Number of neighbors to be considered.
k - Variable in class elki.outlier.spatial.CTLuGLSBackwardSearchAlgorithm: Parameter k - neighborhood size
k - Variable in class elki.outlier.spatial.CTLuRandomWalkEC: Parameter k.
k - Variable in class elki.outlier.spatial.neighborhood.PrecomputedKNearestNeighborNeighborhood.Factory: parameter k
k - Variable in class elki.outlier.subspace.AbstractAggarwalYuOutlier: The target dimensionality.
k - Variable in class elki.outlier.subspace.AbstractAggarwalYuOutlier.Par: k Parameter.
k - Variable in class elki.parallel.processor.KDistanceProcessor.Instance: k Parameter
k - Variable in class elki.parallel.processor.KDistanceProcessor: K parameter
k - Variable in class elki.parallel.processor.KNNProcessor.Instance: k Parameter
k - Variable in class elki.parallel.processor.KNNProcessor: K parameter
k - Variable in class elki.utilities.scaling.outlier.OutlierGammaScaling: Gamma parameter k
k - Variable in class elki.utilities.scaling.outlier.TopKOutlierScaling: Number of outliers to keep.
k - Variable in class elki.utilities.scaling.outlier.TopKOutlierScaling.Par: Number of outliers to keep.
k - Variable in class tutorial.clustering.CFSFDP: Number of clusters to find.
k - Variable in class tutorial.clustering.CFSFDP.Par: Number of clusters to find.
k - Variable in class tutorial.clustering.SameSizeKMeans.Par: k Parameter.
k - Variable in class tutorial.outlier.DistanceStddevOutlier: Number of neighbors to get.
k_0 - Variable in class elki.index.tree.metrical.mtreevariants.mktrees.mkcop.ApproximationLine: The start value for k.
k_c - Variable in class elki.outlier.intrinsic.IDOS: kNN for the context set (ID computation).
k_c - Variable in class elki.outlier.intrinsic.IDOS.Par: kNN for the context set (ID computation).
k_i - Variable in class elki.clustering.AbstractProjectedClustering: Multiplier for the number of initial seeds
k_i - Variable in class elki.clustering.AbstractProjectedClustering.Par: Multiplier for the number of initial seeds
K_I_ID - Static variable in class elki.clustering.AbstractProjectedClustering.Par: Parameter to specify the multiplier for the initial number of seeds, must be an integer greater than 0.
K_ID - Static variable in class elki.algorithm.KNNDistancesSampler.Par: Parameter to specify the distance of the k-distant object to be assessed, must be an integer greater than 0.
K_ID - Static variable in class elki.algorithm.KNNJoin.Par: Parameter that specifies the k-nearest neighbors to be assigned, must be an integer greater than 0.
K_ID - Static variable in class elki.algorithm.statistics.HopkinsStatisticClusteringTendency.Par: Parameter for k.
K_ID - Static variable in class elki.application.cache.CacheDoubleDistanceKNNLists.Par: Parameter that specifies the number of neighbors to precompute.
K_ID - Static variable in class elki.clustering.AbstractProjectedClustering.Par: Parameter to specify the number of clusters to find, must be an integer greater than 0.
K_ID - Static variable in class elki.clustering.CFSFDP.Par: Number of clusters parameter
K_ID - Static variable in class elki.clustering.correlation.COPAC.Par: Size for the kNN neighborhood used in the PCA step of COPAC.
K_ID - Static variable in class elki.clustering.correlation.ERiC.Par: Size for the kNN neighborhood used in the PCA step of ERiC.
K_ID - Static variable in class elki.clustering.correlation.HiCO.Par: Optional parameter to specify the number of nearest neighbors considered in the PCA, must be an integer greater than 0.
K_ID - Static variable in class elki.clustering.dbscan.LSDBC.Par: Parameter for neighborhood size.
K_ID - Static variable in class elki.clustering.em.EM.Par: Parameter to specify the number of clusters to find.
K_ID - Static variable in class elki.clustering.em.KDTreeEM.Par: Parameter to specify the number of clusters to find.
K_ID - Static variable in class elki.clustering.hierarchical.extraction.ClustersWithNoiseExtraction.Par: The number of clusters to extract.
K_ID - Static variable in class elki.clustering.kcenter.GreedyKCenter.Par: Parameter to specify the number of clusters
K_ID - Static variable in class elki.clustering.kmeans.FuzzyCMeans.Par: Parameter to specify the number of clusters to find, must be an integer greater than 0.
K_ID - Static variable in interface elki.clustering.kmeans.KMeans: Parameter to specify the number of clusters to find, must be an integer greater than 0.
K_ID - Static variable in class elki.clustering.onedimensional.KNNKernelDensityMinimaClustering.Par: Number of neighbors for bandwidth estimation.
K_ID - Static variable in class elki.clustering.subspace.HiSC.Par: The number of nearest neighbors considered to determine the preference vector.
K_ID - Static variable in class elki.evaluation.scores.PrecisionAtKEvaluation.Par: Option ID for the k parameter.
K_ID - Static variable in class elki.index.laesa.LAESA.Factory.Par: Condition parameter.
K_ID - Static variable in class elki.index.preprocessed.knn.AbstractMaterializeKNNPreprocessor.Factory: Parameter to specify the number of nearest neighbors of an object to be materialized. must be an integer greater than 1.
K_ID - Static variable in class elki.index.tree.spatial.rstarvariants.rdknn.RdKNNTreeFactory: Parameter for k
K_ID - Static variable in class elki.math.statistics.distribution.ExpGammaDistribution.Par: k parameter, same as GammaDistribution.Par.K_ID.
K_ID - Static variable in class elki.math.statistics.distribution.GammaDistribution.Par: K parameter.
K_ID - Static variable in class elki.outlier.anglebased.FastABOD.Par: Parameter for the nearest neighbors.
K_ID - Static variable in class elki.outlier.COP.Par: Parameter to specify the number of nearest neighbors of an object to be considered for computing its score, must be an integer greater than 0.
K_ID - Static variable in class elki.outlier.distance.KNNDD.Par: Parameter to specify the k nearest neighbor
K_ID - Static variable in class elki.outlier.distance.KNNOutlier.Par: Parameter to specify the k nearest neighbor
K_ID - Static variable in class elki.outlier.distance.KNNWeightOutlier.Par: Parameter to specify the k nearest neighbor.
K_ID - Static variable in class elki.outlier.distance.LocalIsolationCoefficient.Par: Parameter to specify the k nearest neighbor.
K_ID - Static variable in class elki.outlier.distance.ReferenceBasedOutlierDetection.Par: The number of nearest neighbors of an object, to be considered for computing its REFOD_SCORE, must be an integer greater than 1.
K_ID - Static variable in class elki.outlier.intrinsic.LID.Par: Parameter for the number of neighbors.
K_ID - Static variable in class elki.outlier.lof.INFLO.Par: Parameter to specify the number of nearest neighbors of an object to be considered for computing its INFLO score.
K_ID - Static variable in class elki.outlier.lof.LDOF.Par: Parameter to specify the number of nearest neighbors of an object to be considered for computing its LDOF_SCORE, must be an integer greater than 1.
K_ID - Static variable in class elki.outlier.SimpleCOP.Par: Parameter to specify the number of nearest neighbors of an object to be considered for computing its COP_SCORE, must be an integer greater than 0.
K_ID - Static variable in class elki.outlier.subspace.AbstractAggarwalYuOutlier.Par: OptionID for the target dimensionality.
K_ID - Static variable in class elki.utilities.scaling.outlier.TopKOutlierScaling.Par: Parameter to specify the number of outliers to keep
K_ID - Static variable in class tutorial.clustering.CFSFDP.Par: Number of clusters parameter.
k_max - Variable in class elki.clustering.kmeans.XMeans: Effective number of clusters, minimum and maximum.
k_max - Variable in class elki.index.tree.metrical.mtreevariants.mktrees.MkTreeHeader: The maximum number k of reverse kNN queries to be supported.
k_max - Variable in class elki.index.tree.spatial.rstarvariants.rdknn.RdkNNSettings: Parameter k.
k_max - Variable in class elki.index.tree.spatial.rstarvariants.rdknn.RdKNNTreeHeader: The maximum number k of reverse kNN queries to be supported.
K_MAX_ID - Static variable in class elki.index.tree.metrical.mtreevariants.mktrees.AbstractMkTreeUnifiedFactory.Par: Parameter specifying the maximal number k of reverse k nearest neighbors to be supported, must be an integer greater than 0.
k_min - Variable in class elki.clustering.kmeans.XMeans: Effective number of clusters, minimum and maximum.
k_r - Variable in class elki.outlier.intrinsic.IDOS: kNN for the reference set.
k_r - Variable in class elki.outlier.intrinsic.IDOS.Par: kNN for the reference set.
K_S_CRITICAL001 - Static variable in class elki.outlier.subspace.OUTRES: Constant for Kolmogorov-Smirnov at alpha=0.01 (table value)
kappa - Variable in class elki.clustering.correlation.FourC.Settings: Kappa penalty parameter, to punish deviation in low-variance Eigenvectors.
kappa - Variable in class elki.clustering.subspace.PreDeCon.Settings: The kappa penality factor for deviations in preferred dimensions.
KAPPA - Static variable in class elki.visualization.visualizers.scatterplot.cluster.EMClusterVisualization.Instance: Kappa constant.
KAPPA_DEFAULT - Static variable in class elki.clustering.correlation.FourC.Settings.Par: Default for kappa parameter.
KAPPA_DEFAULT - Static variable in class elki.clustering.subspace.PreDeCon.Settings.Par: Default for kappa parameter.
KAPPA_ID - Static variable in class elki.clustering.correlation.FourC.Settings.Par: Parameter Kappa: penalty for deviations in preferred dimensions.
KAPPA_ID - Static variable in class elki.clustering.subspace.PreDeCon.Settings.Par: Parameter Kappa: penalty for deviations in preferred dimensions.
KappaDistribution - Class in elki.math.statistics.distribution: Kappa distribution, by Hosking.
KappaDistribution(double, double, double, double) - Constructor for class elki.math.statistics.distribution.KappaDistribution: Constructor.
KappaDistribution.Par - Class in elki.math.statistics.distribution: Parameterization class
KC_ID - Static variable in class elki.outlier.intrinsic.IDOS.Par: Parameter to specify the number of nearest neighbors of an object to be used for the GED computation.
kcomp - Variable in class elki.outlier.lof.LoOP: Comparison neighborhood size.
KDDCLIApplication - Class in elki.application: Basic command line application for Knowledge Discovery in Databases use cases.
KDDCLIApplication(KDDTask) - Constructor for class elki.application.KDDCLIApplication: Constructor.
KDDCLIApplication.Par - Class in elki.application: Parameterization class.
KDDTask - Class in elki: KDDTask encapsulates the common workflow of an unsupervised knowledge discovery task.
KDDTask(InputStep, AlgorithmStep, EvaluationStep, OutputStep, Collection<TrackedParameter>) - Constructor for class elki.KDDTask: Constructor.
KDDTask.Par - Class in elki: Parameterization class.
KDEOS<O> - Class in elki.outlier.lof: Generalized Outlier Detection with Flexible Kernel Density Estimates.
KDEOS(Distance<? super O>, int, int, KernelDensityFunction, double, double, int) - Constructor for class elki.outlier.lof.KDEOS: Constructor.
kdIndex - Variable in class elki.database.query.EmpiricalQueryOptimizer: k-d-tree index class.
kdist - Variable in class elki.database.ids.integer.DoubleIntegerDBIDKNNHeap: Current maximum value.
KDistanceProcessor - Class in elki.parallel.processor: Compute the kNN distance for each object.
KDistanceProcessor(int) - Constructor for class elki.parallel.processor.KDistanceProcessor: Constructor.
KDistanceProcessor.Instance - Class in elki.parallel.processor: Instance for precomputing the kNN.
kdists - Variable in class elki.outlier.lof.parallel.LRDProcessor: k-distance store
kdKNNSearch(int, int, int, O, KNNHeap, DBIDArrayIter, double[], double, double) - Method in class elki.index.tree.spatial.kd.MinimalisticMemoryKDTree.KDTreeKNNSearcher: Perform a kNN search on the k-d-tree.
kdKNNSearch(int, int, int, O, KNNHeap, DoubleDBIDListIter, double[], double, double) - Method in class elki.index.tree.spatial.kd.SmallMemoryKDTree.KDTreeKNNSearcher: Perform a kNN search on the k-d-tree.
kdKNNSearch(Object, O, KNNHeap, DBIDArrayIter, double[], double, double) - Method in class elki.index.tree.spatial.kd.MemoryKDTree.KDTreeKNNSearcher: Perform a kNN search on the k-d-tree.
KDNode(int, double, Object, Object) - Constructor for class elki.index.tree.spatial.kd.MemoryKDTree.KDNode: Constructor.
KDNode(Relation<? extends NumberVector>, DBIDArrayIter, int, int) - Constructor for class elki.clustering.kmeans.KDTreePruningKMeans.KDNode: Constructor.
kdRangeSearch(int, int, int, O, ModifiableDoubleDBIDList, DBIDArrayIter, double[], double, double) - Method in class elki.index.tree.spatial.kd.MinimalisticMemoryKDTree.KDTreeRangeSearcher: Perform a range search on the k-d-tree.
kdRangeSearch(int, int, int, O, ModifiableDoubleDBIDList, DoubleDBIDListIter, double[], double, double) - Method in class elki.index.tree.spatial.kd.SmallMemoryKDTree.KDTreeRangeSearcher: Perform a range search on the k-d-tree.
kdRangeSearch(Object, O, ModifiableDoubleDBIDList, DBIDArrayIter, double[], double, double) - Method in class elki.index.tree.spatial.kd.MemoryKDTree.KDTreeRangeSearcher: Perform a range search on the k-d-tree.
KDTree(Relation<? extends NumberVector>, ArrayModifiableDBIDs, int, int, double[], double) - Constructor for class elki.clustering.em.KDTreeEM.KDTree: Constructor for a KDTree with statistics needed for KDTreeEM calculation.
KDTreeEM - Class in elki.clustering.em: Clustering by expectation maximization (EM-Algorithm), also known as Gaussian Mixture Modeling (GMM), calculated on a kd-tree.
KDTreeEM(int, double, double, double, double, TextbookMultivariateGaussianModelFactory, int, int, boolean, boolean) - Constructor for class elki.clustering.em.KDTreeEM: Constructor.
KDTreeEM.KDTree - Class in elki.clustering.em: KDTree class with the statistics needed for EM clustering.
KDTreeEM.Par - Class in elki.clustering.em: Parameterization class.
KDTreeFilteringKMeans<V extends NumberVector> - Class in elki.clustering.kmeans: Filtering or "blacklisting" K-means with k-d-tree acceleration.
KDTreeFilteringKMeans(NumberVectorDistance<? super V>, int, int, KMeansInitialization, KDTreePruningKMeans.Split, int) - Constructor for class elki.clustering.kmeans.KDTreeFilteringKMeans: Constructor.
KDTreeFilteringKMeans.Instance - Class in elki.clustering.kmeans: Inner instance, storing state for a single data set.
KDTreeFilteringKMeans.Par<V extends NumberVector> - Class in elki.clustering.kmeans: Parameterization class.
KDTreeKNNSearcher(PartialDistance<? super O>) - Constructor for class elki.index.tree.spatial.kd.MemoryKDTree.KDTreeKNNSearcher: Constructor.
KDTreeKNNSearcher(PartialDistance<? super O>) - Constructor for class elki.index.tree.spatial.kd.MinimalisticMemoryKDTree.KDTreeKNNSearcher: Constructor.
KDTreeKNNSearcher(PartialDistance<? super O>) - Constructor for class elki.index.tree.spatial.kd.SmallMemoryKDTree.KDTreeKNNSearcher: Constructor.
KDTreePrioritySearcher(PrimitiveDistance<? super O>) - Constructor for class elki.index.tree.spatial.kd.MinimalisticMemoryKDTree.KDTreePrioritySearcher: Constructor.
KDTreePrioritySearcher(PrimitiveDistance<? super O>) - Constructor for class elki.index.tree.spatial.kd.SmallMemoryKDTree.KDTreePrioritySearcher: Constructor.
KDTreePrioritySearcher(PartialDistance<? super O>) - Constructor for class elki.index.tree.spatial.kd.MemoryKDTree.KDTreePrioritySearcher: Constructor.
KDTreePruningKMeans<V extends NumberVector> - Class in elki.clustering.kmeans: Pruning K-means with k-d-tree acceleration.
KDTreePruningKMeans(NumberVectorDistance<? super V>, int, int, KMeansInitialization, KDTreePruningKMeans.Split, int) - Constructor for class elki.clustering.kmeans.KDTreePruningKMeans: Constructor.
KDTreePruningKMeans.Instance - Class in elki.clustering.kmeans: Inner instance, storing state for a single data set.
KDTreePruningKMeans.KDNode - Class in elki.clustering.kmeans: Node of the k-d-tree used internally.
KDTreePruningKMeans.Par<V extends NumberVector> - Class in elki.clustering.kmeans: Parameterization class.
KDTreePruningKMeans.Split - Enum in elki.clustering.kmeans: Splitting strategies for constructing the k-d-tree.
KDTreeRangeSearcher(PartialDistance<? super O>) - Constructor for class elki.index.tree.spatial.kd.MemoryKDTree.KDTreeRangeSearcher: Constructor.
KDTreeRangeSearcher(PartialDistance<? super O>) - Constructor for class elki.index.tree.spatial.kd.MinimalisticMemoryKDTree.KDTreeRangeSearcher: Constructor.
KDTreeRangeSearcher(PartialDistance<? super O>) - Constructor for class elki.index.tree.spatial.kd.SmallMemoryKDTree.KDTreeRangeSearcher: Constructor.
keep - Variable in class elki.clustering.uncertain.RepresentativeUncertainClustering: Keep all samples (not only the representative results)
keep - Variable in class elki.clustering.uncertain.RepresentativeUncertainClustering.Par: Keep all samples (not only the representative results).
keep - Variable in class elki.datasource.filter.typeconversions.UncertainifyFilter: Flag to keep the original data.
keep - Variable in class elki.datasource.filter.typeconversions.UncertainifyFilter.Par: Flag to keep the original data.
keep - Variable in class elki.projection.AbstractProjectionAlgorithm: Keep the original data relation.
KEEP_ID - Static variable in class elki.datasource.filter.typeconversions.UncertainifyFilter.Par: Flag to keep the original data.
KEEP_ID - Static variable in class elki.projection.AbstractProjectionAlgorithm: Flag to keep the original projection
KEEP_SAMPLES_ID - Static variable in class elki.clustering.uncertain.RepresentativeUncertainClustering.Par: Flag to keep all samples.
keepfirst - Variable in class elki.clustering.kmeans.initialization.FarthestPoints.Par: Flag for discarding the first object chosen.
KEEPFIRST_ID - Static variable in class elki.clustering.kmeans.initialization.FarthestPoints.Par: Option ID to control the handling of the first object chosen.
keepmed - Variable in class elki.clustering.kmedoids.CLARA: Keep the previous medoids in the sample (see page 145).
keepmed - Variable in class elki.clustering.kmedoids.CLARA.Par: Keep the previous medoids in the sample.
keepmed - Variable in class elki.clustering.kmedoids.FastCLARA: Keep the previous medoids in the sample (see page 145).
keepmed - Variable in class elki.clustering.kmedoids.FastCLARA.Par: Keep the previous medoids in the sample.
keepmed - Variable in class elki.clustering.kmedoids.FasterCLARA: Keep the previous medoids in the sample (see page 145).
keepmed - Variable in class elki.clustering.kmedoids.FasterCLARA.Par: Keep the previous medoids in the sample.
keepsteep - Variable in class elki.clustering.optics.OPTICSXi: Keep the steep areas, for visualization.
keepsteep - Variable in class elki.clustering.optics.OPTICSXi.Par
KEEPSTEEP_ID - Static variable in class elki.clustering.optics.OPTICSXi.Par: Parameter to keep the steep areas
kernel - Variable in class elki.clustering.NaiveMeanShiftClustering: Density estimation kernel.
kernel - Variable in class elki.clustering.onedimensional.KNNKernelDensityMinimaClustering: Kernel density function.
kernel - Variable in class elki.clustering.onedimensional.KNNKernelDensityMinimaClustering.Par: Kernel density function.
kernel - Variable in class elki.clustering.svm.SupportVectorClustering: Kernel function.
kernel - Variable in class elki.outlier.lof.KDEOS: Kernel function to use for density estimation.
kernel - Variable in class elki.outlier.lof.LDF: Kernel density function
kernel - Variable in class elki.outlier.lof.SimpleKernelDensityLOF: Kernel density function
kernel - Variable in class elki.outlier.subspace.OUTRES.KernelDensityEstimator: Actual kernel in use
kernel - Variable in class elki.outlier.svm.LibSVMOneClassOutlierDetection: Kernel function in use.
kernel - Variable in class elki.outlier.svm.OCSVM: Kernel function.
kernel - Variable in class elki.outlier.svm.SVDD: Kernel function.
kernel - Variable in class elki.similarity.kernel.KernelMatrix: The kernel matrix
Kernel - Class in elki.svm.qmatrix
Kernel(DataSet) - Constructor for class elki.svm.qmatrix.Kernel
KERNEL - Static variable in class elki.math.statistics.kernelfunctions.BiweightKernelDensityFunction: Static instance.
KERNEL - Static variable in class elki.math.statistics.kernelfunctions.CosineKernelDensityFunction: Static instance.
KERNEL - Static variable in class elki.math.statistics.kernelfunctions.EpanechnikovKernelDensityFunction: Static instance.
KERNEL - Static variable in class elki.math.statistics.kernelfunctions.GaussianKernelDensityFunction: Static instance.
KERNEL - Static variable in class elki.math.statistics.kernelfunctions.TriangularKernelDensityFunction: Static instance.
KERNEL - Static variable in class elki.math.statistics.kernelfunctions.TricubeKernelDensityFunction: Static instance.
KERNEL - Static variable in class elki.math.statistics.kernelfunctions.TriweightKernelDensityFunction: Static instance.
KERNEL - Static variable in class elki.math.statistics.kernelfunctions.UniformKernelDensityFunction: Static instance.
KERNEL_FUNCTION_ID - Static variable in class elki.outlier.anglebased.ABOD.Par: Parameter for the kernel function.
KERNEL_ID - Static variable in class elki.clustering.onedimensional.KNNKernelDensityMinimaClustering.Par: Kernel function.
KernelDensityEstimator - Class in elki.math.statistics: Estimate density given an array of points.
KernelDensityEstimator(double[], double, double, KernelDensityFunction, int, double) - Constructor for class elki.math.statistics.KernelDensityEstimator: Initialize and execute kernel density estimation.
KernelDensityEstimator(double[], KernelDensityFunction, double) - Constructor for class elki.math.statistics.KernelDensityEstimator: Process an array of data
KernelDensityEstimator(Relation<? extends NumberVector>, double) - Constructor for class elki.outlier.subspace.OUTRES.KernelDensityEstimator: Constructor.
KernelDensityFunction - Interface in elki.math.statistics.kernelfunctions: Inner function of a kernel density estimator.
kernelFunction - Variable in class elki.outlier.anglebased.ABOD: Store the configured Kernel version.
kernelFunction - Variable in class elki.outlier.anglebased.ABOD.Par: Distance function.
KernelMatrix - Class in elki.similarity.kernel: Kernel matrix representation.
KernelMatrix(double[][]) - Constructor for class elki.similarity.kernel.KernelMatrix: Makes a new kernel matrix from matrix (with data copying).
KernelMatrix(SimilarityQuery<? super O>, Relation<? extends O>, DBIDs) - Constructor for class elki.similarity.kernel.KernelMatrix: Provides a new kernel matrix.
KernelMatrix(PrimitiveSimilarity<? super O>, Relation<? extends O>, DBIDs) - Constructor for class elki.similarity.kernel.KernelMatrix: Provides a new kernel matrix.
key - Variable in class elki.clustering.kmeans.AbstractKMeans.Instance: Key for statistics logging.
key - Variable in class elki.evaluation.clustering.internal.CIndex: Key for logging statistics.
key - Variable in class elki.evaluation.clustering.internal.ClusterRadius: Key for logging statistics.
key - Variable in class elki.evaluation.clustering.internal.ConcordantPairsGammaTau: Key for logging statistics.
key - Variable in class elki.evaluation.clustering.internal.DaviesBouldinIndex: Key for logging statistics.
key - Variable in class elki.evaluation.clustering.internal.PBMIndex: Key for logging statistics.
key - Static variable in class elki.evaluation.clustering.internal.Silhouette: Key for logging statistics.
key - Variable in class elki.evaluation.clustering.internal.SimplifiedSilhouette: Key for logging statistics.
key - Variable in class elki.evaluation.clustering.internal.SquaredErrors: Key for logging statistics.
key - Variable in class elki.evaluation.clustering.internal.VarianceRatioCriterion: Key for logging statistics.
key - Variable in class elki.evaluation.outlier.OutlierRankingEvaluation: Key prefix for statistics logging.
key - Variable in class elki.itemsetmining.FPGrowth.FPNode: Key, weight, and number of children.
key - Variable in class elki.logging.statistics.AbstractStatistic: Key to report the statistic with.
key(PlotItem, VisualizationTask) - Method in class elki.visualization.gui.overview.LayerMap: Helper function for building a key object
KEY - Static variable in class elki.clustering.em.EM: Key for statistics logging.
KEY - Static variable in class elki.clustering.kmeans.FuzzyCMeans: Key for statistics logging.
KEY - Static variable in class elki.clustering.kmedoids.AlternatingKMedoids: Key for statistics logging.
KEY - Static variable in class elki.clustering.kmedoids.EagerPAM: Key for loggers.
KEY - Static variable in class elki.clustering.kmedoids.FasterPAM: Key for statistics logging.
KEY - Static variable in class elki.clustering.kmedoids.FastPAM: Key for statistics logging.
KEY - Static variable in class elki.clustering.kmedoids.FastPAM1: Key for statistics logging.
KEY - Static variable in class elki.clustering.kmedoids.ReynoldsPAM: Key for statistics logging.
KEY - Static variable in class elki.clustering.uncertain.UKMeans: Key for statistics logging.
KEY - Static variable in interface elki.visualization.style.StyleLibrary: Key
KEY_CAPTION - Static variable in class elki.visualization.visualizers.visunproj.DendrogramVisualization.Instance: CSS class for key captions.
KEY_CAPTION - Static variable in class elki.visualization.visualizers.visunproj.KeyVisualization.Instance: CSS class for key captions.
KEY_ENTRY - Static variable in class elki.visualization.visualizers.visunproj.KeyVisualization.Instance: CSS class for key entries.
KEY_HIERLINE - Static variable in class elki.visualization.visualizers.visunproj.DendrogramVisualization.Instance: CSS class for hierarchy plot lines
KEY_HIERLINE - Static variable in class elki.visualization.visualizers.visunproj.KeyVisualization.Instance: CSS class for hierarchy plot lines
keymap - Variable in class elki.datasource.parser.SimpleTransactionParser: Map.
keymap - Variable in class elki.datasource.parser.TermFrequencyParser: Map.
keyPressed(KeyEvent) - Method in class elki.gui.util.ParameterTable.ClassListEditor
keyPressed(KeyEvent) - Method in class elki.gui.util.ParameterTable.DropdownEditor
keyPressed(KeyEvent) - Method in class elki.gui.util.ParameterTable.FileNameEditor
keyPressed(KeyEvent) - Method in class elki.gui.util.ParameterTable.Handler
keyPressed(KeyEvent) - Method in class elki.gui.util.TreePopup.Handler
keyReleased(KeyEvent) - Method in class elki.gui.util.ParameterTable.ClassListEditor
keyReleased(KeyEvent) - Method in class elki.gui.util.ParameterTable.DropdownEditor
keyReleased(KeyEvent) - Method in class elki.gui.util.ParameterTable.FileNameEditor
keyReleased(KeyEvent) - Method in class elki.gui.util.ParameterTable.Handler
keyReleased(KeyEvent) - Method in class elki.gui.util.TreePopup.Handler
keys - Variable in class elki.outlier.lof.LOCI.DoubleIntArrayList: Double keys
keySet() - Method in class elki.visualization.gui.overview.RectangleArranger: The item keys contained in the map.
keyTyped(KeyEvent) - Method in class elki.gui.util.ParameterTable.ClassListEditor
keyTyped(KeyEvent) - Method in class elki.gui.util.ParameterTable.DropdownEditor
keyTyped(KeyEvent) - Method in class elki.gui.util.ParameterTable.FileNameEditor
keyTyped(KeyEvent) - Method in class elki.gui.util.ParameterTable.Handler
keyTyped(KeyEvent) - Method in class elki.gui.util.TreePopup.Handler
KeyVisualization - Class in elki.visualization.visualizers.visunproj: Visualizer, displaying the key for a clustering.
KeyVisualization() - Constructor for class elki.visualization.visualizers.visunproj.KeyVisualization
KeyVisualization.Instance - Class in elki.visualization.visualizers.visunproj: Instance
Klosgen - Class in elki.itemsetmining.associationrules.interest: Klösgen interestingness measure.
Klosgen() - Constructor for class elki.itemsetmining.associationrules.interest.Klosgen: Constructor.
kmax - Variable in class elki.index.tree.metrical.mtreevariants.mktrees.MkTreeSettings: Holds the maximum value of k to support.
kmax - Variable in class elki.outlier.lof.KDEOS: Maximum number of neighbors to use.
KMC2 - Class in elki.clustering.kmeans.initialization: K-MC² initialization
KMC2(int, RandomFactory) - Constructor for class elki.clustering.kmeans.initialization.KMC2: Constructor.
KMC2.Instance - Class in elki.clustering.kmeans.initialization: Abstract instance implementing the weight handling.
KMC2.Par - Class in elki.clustering.kmeans.initialization: Parameterization class.
kmeans - Variable in class elki.clustering.uncertain.CKMeans.Par: K-means instance to use.
kmeans(double[][], ClusteringFeature[], int[], int[]) - Method in class elki.clustering.hierarchical.birch.BIRCHLloydKMeans: Perform k-means clustering.
kmeans(ArrayList<? extends ClusterFeature>, int[], int[], CFTree<?>) - Method in class elki.clustering.kmeans.BetulaLloydKMeans: Perform k-means clustering.
KMeans<V extends NumberVector,M extends Model> - Interface in elki.clustering.kmeans: Some constants and options shared among kmeans family algorithms.
KMEANSBORDER - Static variable in class elki.visualization.visualizers.scatterplot.cluster.VoronoiVisualization: Generic tags to indicate the type of element.
KMeansInitialization - Interface in elki.clustering.kmeans.initialization: Interface for initializing K-Means
kmeansminusminus - Variable in class elki.outlier.clustering.KMeansMinusMinusOutlierDetection.Par: Clustering algorithm to run.
KMeansMinusMinus<V extends NumberVector> - Class in elki.clustering.kmeans: k-means--: A Unified Approach to Clustering and Outlier Detection.
KMeansMinusMinus(NumberVectorDistance<? super V>, int, int, KMeansInitialization, double, boolean) - Constructor for class elki.clustering.kmeans.KMeansMinusMinus: Constructor.
KMeansMinusMinus.Instance - Class in elki.clustering.kmeans: Inner instance, storing state for a single data set.
KMeansMinusMinus.Par<V extends NumberVector> - Class in elki.clustering.kmeans: Parameterization class.
KMeansMinusMinusOutlierDetection - Class in elki.outlier.clustering: k-means--: A Unified Approach to Clustering and Outlier Detection.
KMeansMinusMinusOutlierDetection(KMeansMinusMinus<?>) - Constructor for class elki.outlier.clustering.KMeansMinusMinusOutlierDetection: Constructor.
KMeansMinusMinusOutlierDetection.Par - Class in elki.outlier.clustering: Parameterizer.
KMeansModel - Class in elki.data.model: Trivial subclass of the MeanModel that indicates the clustering to be produced by k-means (so the Voronoi cell visualization is sensible).
KMeansModel(double[], double) - Constructor for class elki.data.model.KMeansModel: Constructor with mean.
KMeansOutlierDetection<O extends NumberVector> - Class in elki.outlier.clustering: Outlier detection by using k-means clustering.
KMeansOutlierDetection(KMeans<O, ?>, KMeansOutlierDetection.Rule) - Constructor for class elki.outlier.clustering.KMeansOutlierDetection: Constructor.
KMeansOutlierDetection.Rule - Enum in elki.outlier.clustering: Outlier scoring rule
KMeansPlusPlus<O> - Class in elki.clustering.kmeans.initialization: K-Means++ initialization for k-means.
KMeansPlusPlus(RandomFactory) - Constructor for class elki.clustering.kmeans.initialization.KMeansPlusPlus: Constructor.
KMeansPlusPlus.Instance<T> - Class in elki.clustering.kmeans.initialization: Abstract instance implementing the weight handling.
KMeansPlusPlus.MedoidsInstance - Class in elki.clustering.kmeans.initialization: Instance for k-medoids.
KMeansPlusPlus.NumberVectorInstance - Class in elki.clustering.kmeans.initialization: Instance for k-means, number vector based.
KMeansPlusPlus.Par<V> - Class in elki.clustering.kmeans.initialization: Parameterization class.
KMeansProcessor<V extends NumberVector> - Class in elki.clustering.kmeans.parallel: Parallel k-means implementation.
KMeansProcessor(Relation<V>, NumberVectorDistance<? super V>, WritableIntegerDataStore, double[]) - Constructor for class elki.clustering.kmeans.parallel.KMeansProcessor: Constructor.
KMeansProcessor.Instance<V extends NumberVector> - Class in elki.clustering.kmeans.parallel: Instance to process part of the data set, for a single iteration.
KMeansQualityMeasure<O extends NumberVector> - Interface in elki.clustering.kmeans.quality: Interface for computing the quality of a K-Means clustering.
KMediansLloyd<V extends NumberVector> - Class in elki.clustering.kmeans: k-medians clustering algorithm, but using Lloyd-style bulk iterations instead of the more complicated approach suggested by Kaufman and Rousseeuw (see PAM instead).
KMediansLloyd(NumberVectorDistance<? super V>, int, int, KMeansInitialization) - Constructor for class elki.clustering.kmeans.KMediansLloyd: Constructor.
KMediansLloyd.Instance - Class in elki.clustering.kmeans: Inner instance, storing state for a single data set.
KMediansLloyd.Par<V extends NumberVector> - Class in elki.clustering.kmeans: Parameterization class.
KMedoidsClustering<O> - Interface in elki.clustering.kmedoids: Interface for clustering algorithms that produce medoids.
KMedoidsInitialization<O> - Interface in elki.clustering.kmedoids.initialization: Interface for initializing K-Medoids.
KMedoidsKMedoidsInitialization<O> - Class in elki.clustering.kmedoids.initialization: Initialize k-medoids with k-medoids, for methods such as PAMSIL.
This could also be used to initialize, e.g., PAM with CLARA.
KMedoidsKMedoidsInitialization(KMedoidsClustering<O>) - Constructor for class elki.clustering.kmedoids.initialization.KMedoidsKMedoidsInitialization: Constructor.
KMedoidsKMedoidsInitialization.Par<O> - Class in elki.clustering.kmedoids.initialization: Parameterization class.
kmin - Variable in class elki.outlier.lof.KDEOS: Minimum number of neighbors to use.
KMLOutputHandler - Class in elki.result: Class to handle KML output.
KMLOutputHandler(Path, OutlierScaling, boolean, boolean) - Constructor for class elki.result.KMLOutputHandler: Constructor.
KMLOutputHandler.Par - Class in elki.result: Parameterization class
KMPP_DISTANCE_ID - Static variable in class elki.clustering.kmeans.initialization.betula.CFKPlusPlusLeaves.Par: k Means distance.
KMPP_DISTANCE_ID - Static variable in class elki.clustering.kmeans.initialization.betula.CFKPlusPlusTree.Par: k Means distance.
kmulti - Variable in class elki.index.projected.ProjectedIndex.Factory: Multiplier for k.
kmulti - Variable in class elki.index.projected.ProjectedIndex: Multiplier for k.
knn - Variable in class elki.outlier.subspace.SOD: Neighborhood size.
knn - Variable in class elki.outlier.subspace.SOD.Par: Neighborhood size.
KNN_CACHE_MAGIC - Static variable in class elki.application.cache.CacheDoubleDistanceKNNLists: Magic number to identify files.
KNN_ID - Static variable in class elki.outlier.subspace.SOD.Par: Parameter to specify the number of shared nearest neighbors to be considered for learning the subspace properties, must be an integer greater than 0.
kNNABOD(Relation<V>, DBIDs, WritableDoubleDataStore, DoubleMinMax) - Method in class elki.outlier.anglebased.FastABOD: Simpler kNN based, can use more indexing.
KNNBenchmark<O> - Class in elki.application.benchmark: Benchmarking experiment that computes the k nearest neighbors for each query point.
KNNBenchmark(InputStep, Distance<? super O>, int, DatabaseConnection, double, RandomFactory) - Constructor for class elki.application.benchmark.KNNBenchmark: Constructor.
kNNByDBID() - Method in class elki.database.query.QueryBuilder: Build a k-nearest-neighbors query; if possible also give a maximum k.
kNNByDBID(int) - Method in class elki.database.query.QueryBuilder: Build a k-nearest-neighbors query.
kNNByDBID(DistanceQuery<O>, int, int) - Method in class elki.index.distancematrix.PrecomputedDistanceMatrix
kNNByDBID(DistanceQuery<O>, int, int) - Method in interface elki.index.KNNIndex: Get a KNN query object for the given distance query and k.
kNNByDBID(DistanceQuery<O>, int, int) - Method in class elki.index.laesa.LAESA
kNNByDBID(DistanceQuery<O>, int, int) - Method in class elki.index.preprocessed.knn.AbstractMaterializeKNNPreprocessor
kNNByDBID(DistanceQuery<O>, int, int) - Method in class elki.index.preprocessed.knn.NaiveProjectedKNNPreprocessor
kNNByDBID(DistanceQuery<O>, int, int) - Method in class elki.index.preprocessed.knn.SpacefillingKNNPreprocessor
kNNByDBID(DistanceQuery<O>, int, int) - Method in class elki.index.projected.ProjectedIndex
kNNByDBID(DistanceQuery<O>, int, int) - Method in class elki.index.tree.metrical.covertree.CoverTree
kNNByDBID(DistanceQuery<O>, int, int) - Method in class elki.index.tree.metrical.covertree.SimplifiedCoverTree
kNNByDBID(DistanceQuery<O>, int, int) - Method in class elki.index.tree.metrical.mtreevariants.mktrees.mkapp.MkAppTreeIndex
kNNByDBID(DistanceQuery<O>, int, int) - Method in class elki.index.tree.metrical.mtreevariants.mktrees.mkcop.MkCoPTreeIndex
kNNByDBID(DistanceQuery<O>, int, int) - Method in class elki.index.tree.metrical.mtreevariants.mktrees.mkmax.MkMaxTreeIndex
kNNByDBID(DistanceQuery<O>, int, int) - Method in class elki.index.tree.metrical.mtreevariants.mktrees.mktab.MkTabTreeIndex
kNNByDBID(DistanceQuery<O>, int, int) - Method in class elki.index.tree.metrical.mtreevariants.mtree.MTreeIndex
kNNByDBID(DistanceQuery<O>, int, int) - Method in class elki.index.tree.metrical.vptree.GNAT
kNNByDBID(DistanceQuery<O>, int, int) - Method in class elki.index.tree.metrical.vptree.VPTree
kNNByDBID(Relation<? extends O>, DistanceQuery<O>, int, int) - Method in class elki.database.query.EmpiricalQueryOptimizer
kNNByDBID(Relation<? extends O>, DistanceQuery<O>, int, int) - Method in interface elki.database.query.QueryOptimizer: Optimize a kNN query for this relation.
kNNByObject() - Method in class elki.database.query.QueryBuilder: Build a k-nearest-neighbors query; if possible also give a maximum k.
kNNByObject(int) - Method in class elki.database.query.QueryBuilder: Build a k-nearest-neighbors query.
kNNByObject(DistanceQuery<O>, int, int) - Method in class elki.index.distancematrix.PrecomputedDistanceMatrix
kNNByObject(DistanceQuery<O>, int, int) - Method in interface elki.index.DistancePriorityIndex
kNNByObject(DistanceQuery<O>, int, int) - Method in class elki.index.idistance.InMemoryIDistanceIndex
kNNByObject(DistanceQuery<O>, int, int) - Method in interface elki.index.KNNIndex: Get a KNN query object for the given distance query and k.
kNNByObject(DistanceQuery<O>, int, int) - Method in class elki.index.laesa.LAESA
kNNByObject(DistanceQuery<O>, int, int) - Method in class elki.index.preprocessed.knn.AbstractMaterializeKNNPreprocessor: Deprecated.
not possible
kNNByObject(DistanceQuery<O>, int, int) - Method in class elki.index.preprocessed.knn.NaiveProjectedKNNPreprocessor
kNNByObject(DistanceQuery<O>, int, int) - Method in class elki.index.preprocessed.knn.NNDescent
kNNByObject(DistanceQuery<O>, int, int) - Method in class elki.index.preprocessed.knn.SpacefillingKNNPreprocessor
kNNByObject(DistanceQuery<O>, int, int) - Method in class elki.index.preprocessed.knn.SpacefillingMaterializeKNNPreprocessor
kNNByObject(DistanceQuery<O>, int, int) - Method in class elki.index.projected.LatLngAsECEFIndex
kNNByObject(DistanceQuery<O>, int, int) - Method in class elki.index.projected.LngLatAsECEFIndex
kNNByObject(DistanceQuery<O>, int, int) - Method in class elki.index.projected.ProjectedIndex
kNNByObject(DistanceQuery<O>, int, int) - Method in class elki.index.tree.metrical.covertree.CoverTree
kNNByObject(DistanceQuery<O>, int, int) - Method in class elki.index.tree.metrical.covertree.SimplifiedCoverTree
kNNByObject(DistanceQuery<O>, int, int) - Method in class elki.index.tree.metrical.mtreevariants.mktrees.mkapp.MkAppTreeIndex
kNNByObject(DistanceQuery<O>, int, int) - Method in class elki.index.tree.metrical.mtreevariants.mktrees.mkcop.MkCoPTreeIndex
kNNByObject(DistanceQuery<O>, int, int) - Method in class elki.index.tree.metrical.mtreevariants.mktrees.mkmax.MkMaxTreeIndex
kNNByObject(DistanceQuery<O>, int, int) - Method in class elki.index.tree.metrical.mtreevariants.mktrees.mktab.MkTabTreeIndex
kNNByObject(DistanceQuery<O>, int, int) - Method in class elki.index.tree.metrical.mtreevariants.mtree.MTreeIndex
kNNByObject(DistanceQuery<O>, int, int) - Method in class elki.index.tree.metrical.vptree.GNAT
kNNByObject(DistanceQuery<O>, int, int) - Method in class elki.index.tree.metrical.vptree.VPTree
kNNByObject(DistanceQuery<O>, int, int) - Method in class elki.index.tree.spatial.kd.MemoryKDTree
kNNByObject(DistanceQuery<O>, int, int) - Method in class elki.index.tree.spatial.kd.MinimalisticMemoryKDTree
kNNByObject(DistanceQuery<O>, int, int) - Method in class elki.index.tree.spatial.kd.SmallMemoryKDTree
kNNByObject(DistanceQuery<O>, int, int) - Method in class elki.index.tree.spatial.rstarvariants.deliclu.DeLiCluTreeIndex
kNNByObject(DistanceQuery<O>, int, int) - Method in class elki.index.tree.spatial.rstarvariants.flat.FlatRStarTreeIndex
kNNByObject(DistanceQuery<O>, int, int) - Method in class elki.index.tree.spatial.rstarvariants.rdknn.RdKNNTree
kNNByObject(DistanceQuery<O>, int, int) - Method in class elki.index.tree.spatial.rstarvariants.rstar.RStarTreeIndex
kNNByObject(DistanceQuery<V>, int, int) - Method in class elki.index.invertedlist.InMemoryInvertedIndex
kNNByObject(DistanceQuery<V>, int, int) - Method in class elki.index.lsh.InMemoryLSHIndex.Instance
kNNByObject(DistanceQuery<V>, int, int) - Method in class elki.index.vafile.PartialVAFile
kNNByObject(DistanceQuery<V>, int, int) - Method in class elki.index.vafile.VAFile
kNNByObject(Relation<? extends O>, DistanceQuery<O>, int, int) - Method in class elki.database.query.EmpiricalQueryOptimizer
kNNByObject(Relation<? extends O>, DistanceQuery<O>, int, int) - Method in interface elki.database.query.QueryOptimizer: Optimize a kNN query for this relation.
KNNChangeEvent - Class in elki.index.preprocessed.knn: Encapsulates information describing changes of the k nearest neighbors (kNNs) of some objects due to insertion or removal of objects.
KNNChangeEvent(Object, KNNChangeEvent.Type, DBIDs, DBIDs) - Constructor for class elki.index.preprocessed.knn.KNNChangeEvent: Used to create an event when kNNs of some objects have been changed.
KNNChangeEvent.Type - Enum in elki.index.preprocessed.knn: Available event types.
KNNClassifier<O> - Class in elki.classification: KNNClassifier classifies instances based on the class distribution among the k nearest neighbors in a database.
KNNClassifier(Distance<? super O>, int) - Constructor for class elki.classification.KNNClassifier: Constructor.
KNNDD<O> - Class in elki.outlier.distance: Nearest Neighbor Data Description.
KNNDD(Distance<? super O>, int) - Constructor for class elki.outlier.distance.KNNDD: Constructor for a single kNN query.
KNNDD.Par<O> - Class in elki.outlier.distance: Parameterization class.
KNNDIST - Static variable in class elki.visualization.visualizers.scatterplot.selection.DistanceFunctionVisualization.Instance
knnDistance - Variable in class elki.index.tree.metrical.mtreevariants.mktrees.mkmax.MkMaxDirectoryEntry: The aggregated k-nearest neighbor distance of the underlying MkMax-Tree node.
knnDistance - Variable in class elki.index.tree.metrical.mtreevariants.mktrees.mkmax.MkMaxLeafEntry: The k-nearest neighbor distance of the underlying data object.
knnDistance - Variable in class elki.index.tree.spatial.rstarvariants.rdknn.RdKNNDirectoryEntry: The aggregated knn distance of this entry.
knnDistance - Variable in class elki.index.tree.spatial.rstarvariants.rdknn.RdKNNLeafEntry: The knn distance of the underlying data object.
kNNDistance() - Method in class elki.index.tree.metrical.mtreevariants.mktrees.mkmax.MkMaxTreeNode: Determines and returns the k-nearest neighbor distance of this node as the maximum of the k-nearest neighbor distances of all entries.
kNNDistance() - Method in class elki.index.tree.spatial.rstarvariants.rdknn.RdKNNNode: Computes and returns the aggregated knn distance of this node
kNNdistanceAdjustment(MkMaxEntry, Map<DBID, KNNList>) - Method in class elki.index.tree.metrical.mtreevariants.mktrees.mkmax.MkMaxTree: Adjusts the knn distance in the subtree of the specified root entry.
kNNdistanceAdjustment(MkTabEntry, Map<DBID, KNNList>) - Method in class elki.index.tree.metrical.mtreevariants.mktrees.mktab.MkTabTree
kNNdistanceAdjustment(E, Map<DBID, KNNList>) - Method in class elki.index.tree.metrical.mtreevariants.mktrees.AbstractMkTreeUnified: Performs a distance adjustment in the subtree of the specified root entry.
knnDistanceApproximation() - Method in class elki.index.tree.metrical.mtreevariants.mktrees.mkapp.MkAppTreeNode: Determines and returns the polynomial approximation for the knn distances of this node as the maximum of the polynomial approximations of all entries.
KNNDistanceOrderResult(double[], int) - Constructor for class elki.algorithm.KNNDistancesSampler.KNNDistanceOrderResult: Construct result
knnDistances - Variable in class elki.index.tree.metrical.mtreevariants.mktrees.mktab.MkTabDirectoryEntry: The aggregated knn distances of the underlying node.
knnDistances - Variable in class elki.index.tree.metrical.mtreevariants.mktrees.mktab.MkTabLeafEntry: The knn distances of the underlying data object.
knnDistances(DBIDRef) - Method in class elki.index.tree.metrical.mtreevariants.mktrees.mktab.MkTabTreeIndex: Returns the knn distance of the object with the specified id.
kNNDistances() - Method in class elki.index.tree.metrical.mtreevariants.mktrees.mktab.MkTabTreeNode: Determines and returns the knn distance of this node as the maximum knn distance of all entries.
KNNDistancesSampler<O> - Class in elki.algorithm: Provides an order of the kNN-distances for all objects within the database.
KNNDistancesSampler(Distance<? super O>, int, double, RandomFactory) - Constructor for class elki.algorithm.KNNDistancesSampler: Constructor.
KNNDistancesSampler.KNNDistanceOrderResult - Class in elki.algorithm: Curve result for a list containing the knn distances.
KNNDistancesSampler.Par<O> - Class in elki.algorithm: Parameterization class.
KNNEvaluator() - Constructor for class elki.algorithm.statistics.EvaluateRetrievalPerformance.KNNEvaluator
KNNHeap - Interface in elki.database.ids: Interface for kNN heaps.
knnIndex - Variable in class elki.database.query.EmpiricalQueryOptimizer: kNN preprocessor class.
KNNIndex<O> - Interface in elki.index: Index with support for kNN queries.
KNNJoin - Class in elki.algorithm: Joins in a given spatial database to each object its k-nearest neighbors.
KNNJoin(SpatialPrimitiveDistance<?>, int) - Constructor for class elki.algorithm.KNNJoin: Constructor.
KNNJoin.Par - Class in elki.algorithm: Parameterization class.
KNNJoin.Task - Class in elki.algorithm: Task in the processing queue.
KNNJoinMaterializeKNNPreprocessor<V extends SpatialComparable> - Class in elki.index.preprocessed.knn: Class to materialize the kNN using a spatial join on an R-tree.
KNNJoinMaterializeKNNPreprocessor(Relation<V>, Distance<? super V>, int) - Constructor for class elki.index.preprocessed.knn.KNNJoinMaterializeKNNPreprocessor: Constructor.
KNNJoinMaterializeKNNPreprocessor.Factory<O extends SpatialComparable> - Class in elki.index.preprocessed.knn: The parameterizable factory.
KNNKernelDensityMinimaClustering - Class in elki.clustering.onedimensional: Cluster one-dimensional data by splitting the data set on local minima after performing kernel density estimation.
KNNKernelDensityMinimaClustering(int, KernelDensityFunction, KNNKernelDensityMinimaClustering.Mode, int, int) - Constructor for class elki.clustering.onedimensional.KNNKernelDensityMinimaClustering: Constructor.
KNNKernelDensityMinimaClustering.Mode - Enum in elki.clustering.onedimensional: Estimation mode.
KNNKernelDensityMinimaClustering.Par - Class in elki.clustering.onedimensional: Parameterization class.
KNNList - Interface in elki.database.ids: Interface for kNN results.
KNNLIST - Static variable in class elki.data.type.TypeUtil: KNN lists.
KNNListener - Interface in elki.index.preprocessed.knn: Listener interface invoked when the k nearest neighbors (kNNs) of some objects have been changed due to insertion or removals of objects.
KNNMARKER - Static variable in class elki.visualization.visualizers.scatterplot.selection.DistanceFunctionVisualization.Instance: Generic tags to indicate the type of element.
KNNOutlier<O> - Class in elki.outlier.distance: Outlier Detection based on the distance of an object to its k nearest neighbor.
KNNOutlier(Distance<? super O>, int) - Constructor for class elki.outlier.distance.KNNOutlier: Constructor for a single kNN query.
KNNOutlier.Par<O> - Class in elki.outlier.distance: Parameterization class.
knnperf - Variable in class elki.algorithm.statistics.EvaluateRetrievalPerformance.RetrievalPerformanceResult: KNN performance
KNNProcessor - Class in elki.parallel.processor: Processor to compute the kNN of each object.
KNNProcessor(int, Supplier<KNNSearcher<DBIDRef>>) - Constructor for class elki.parallel.processor.KNNProcessor: Constructor.
KNNProcessor.Instance - Class in elki.parallel.processor: Instance for precomputing the kNN.
knnq - Variable in class elki.classification.KNNClassifier: kNN query class.
knnq - Variable in class elki.index.tree.metrical.mtreevariants.mktrees.AbstractMkTree: Internal class for performing knn queries
knnq - Variable in class elki.parallel.processor.KNNProcessor.Instance: kNN query
knnq - Variable in class elki.parallel.processor.KNNProcessor: KNN query object
knnQueries - Variable in class elki.index.tree.metrical.mtreevariants.AbstractMTree.Statistics: For counting the number of knn queries answered.
knnQueries - Variable in class elki.index.tree.spatial.rstarvariants.AbstractRStarTree.Statistics: For counting the number of knn queries answered.
knnQuery - Variable in class elki.database.query.rknn.LinearScanRKNNByDBID: KNN query we use.
knnQuery - Variable in class elki.database.query.rknn.LinearScanRKNNByObject: KNN query we use.
knnQuery - Variable in class elki.index.preprocessed.knn.MaterializeKNNPreprocessor: KNNSearcher instance to use.
knnQuery - Variable in class elki.index.tree.spatial.rstarvariants.rdknn.RdKNNTree: Internal knn query object, for updating the rKNN.
kNNReach - Variable in class elki.outlier.lof.FlexibleLOF.LOFResult: The kNN query w.r.t. the reachability distance.
kNNRefer - Variable in class elki.outlier.lof.FlexibleLOF.LOFResult: The kNN query w.r.t. the reference neighborhood distance.
knns - Variable in class elki.outlier.lof.parallel.LOFProcessor: KNN store
knns - Variable in class elki.outlier.lof.parallel.LRDProcessor: KNN store
knns - Variable in class elki.outlier.lof.parallel.SimplifiedLRDProcessor: KNN store
kNNsChanged(KNNChangeEvent) - Method in interface elki.index.preprocessed.knn.KNNListener: Invoked after kNNs have been updated, inserted or removed in some way.
kNNsChanged(KNNChangeEvent) - Method in class elki.outlier.lof.OnlineLOF.LOFKNNListener
kNNsChanged(KNNChangeEvent, KNNChangeEvent) - Method in class elki.outlier.lof.OnlineLOF.LOFKNNListener: Invoked after the events of both preprocessors have been received, i.e.
KNNSearcher<O> - Interface in elki.database.query.knn: The interface of an actual instance.
kNNsInserted(DBIDs, DBIDs, DBIDs, FlexibleLOF.LOFResult<O>) - Method in class elki.outlier.lof.OnlineLOF.LOFKNNListener: Invoked after kNNs have been inserted and updated, updates the result.
KNNSOS<O> - Class in elki.outlier.distance: kNN-based adaption of Stochastic Outlier Selection.
KNNSOS(Distance<? super O>, int) - Constructor for class elki.outlier.distance.KNNSOS: Constructor.
kNNsRemoved(DBIDs, DBIDs, DBIDs, FlexibleLOF.LOFResult<O>) - Method in class elki.outlier.lof.OnlineLOF.LOFKNNListener: Invoked after kNNs have been removed and updated, updates the result.
KNNWeightOutlier<O> - Class in elki.outlier.distance: Outlier Detection based on the accumulated distances of a point to its k nearest neighbors.
KNNWeightOutlier(Distance<? super O>, int) - Constructor for class elki.outlier.distance.KNNWeightOutlier: Constructor with parameters.
KNNWeightOutlier.Par<O> - Class in elki.outlier.distance: Parameterization class.
KNNWeightProcessor - Class in elki.outlier.distance.parallel: Compute the kNN weight score, used by ParallelKNNWeightOutlier.
KNNWeightProcessor(int) - Constructor for class elki.outlier.distance.parallel.KNNWeightProcessor: Constructor.
KNNWeightProcessor.Instance - Class in elki.outlier.distance.parallel: Instance for precomputing the kNN.
KNOWN_REVERSED - Static variable in class elki.application.greedyensemble.EvaluatePrecomputedOutlierScores: Pattern to match a set of known reversed scores.
knownParameterizables - Variable in class elki.application.internal.CheckParameterizables: Known parameterizable classes/interfaces.
KolmogorovSmirnovDistance - Class in elki.distance.histogram: Distance function based on the Kolmogorov-Smirnov goodness of fit test.
KolmogorovSmirnovDistance() - Constructor for class elki.distance.histogram.KolmogorovSmirnovDistance: Deprecated.
Use static instance!
KolmogorovSmirnovDistance.Par - Class in elki.distance.histogram: Parameterization class, using the static instance.
KolmogorovSmirnovTest - Class in elki.math.statistics.tests: Kolmogorov-Smirnov test.
KolmogorovSmirnovTest() - Constructor for class elki.math.statistics.tests.KolmogorovSmirnovTest: Constructor.
KolmogorovSmirnovTest.Par - Class in elki.math.statistics.tests: Parameterizer, to use the static instance.
kplus - Variable in class elki.clustering.dbscan.LSDBC: Number of neighbors (+ query point)
kplus - Variable in class elki.outlier.distance.KNNDD: The parameter k (plus query point!)
kplus - Variable in class elki.outlier.distance.KNNOutlier: The parameter k (plus query point!)
kplus - Variable in class elki.outlier.distance.KNNWeightOutlier: Holds the number of nearest neighbors to query (plus the query point!)
kplus - Variable in class elki.outlier.distance.LocalIsolationCoefficient: Holds the number of nearest neighbors to query (plus the query point!)
kplus - Variable in class elki.outlier.distance.ODIN: Number of neighbors for kNN graph.
kplus - Variable in class elki.outlier.distance.parallel.ParallelKNNOutlier: Parameter k + 1
kplus - Variable in class elki.outlier.distance.parallel.ParallelKNNWeightOutlier: Parameter k + 1
kplus - Variable in class elki.outlier.DWOF: Holds the value of DWOF.Par.K_ID i.e.
kplus - Variable in class elki.outlier.intrinsic.LID: Number of neighbors to use + query point.
kplus - Variable in class elki.outlier.lof.INFLO: Number of neighbors to use.
kplus - Variable in class elki.outlier.lof.LDF: Parameter k + 1 for the query point.
kplus - Variable in class elki.outlier.lof.LDOF: Number of neighbors to query + query point itself.
kplus - Variable in class elki.outlier.lof.LOF: The number of neighbors to query (plus the query point!)
kplus - Variable in class elki.outlier.lof.parallel.ParallelLOF: Parameter k + 1 for query point
kplus - Variable in class elki.outlier.lof.parallel.ParallelSimplifiedLOF: Parameter k + 1 for the query point
kplus - Variable in class elki.outlier.lof.SimpleKernelDensityLOF: Number of neighbors + the query point
kplus - Variable in class elki.outlier.lof.SimplifiedLOF: The number of neighbors to query, plus the query point.
kplus - Variable in class elki.outlier.lof.VarianceOfVolume: The number of neighbors to query (plus the query point!)
kplus - Variable in class elki.outlier.SimpleCOP: Number of neighbors to be considered + the query point
kplus - Variable in class tutorial.outlier.ODIN: Number of neighbors for kNN graph.
KR_ID - Static variable in class elki.outlier.intrinsic.IDOS.Par: Parameter to specify the neighborhood size to use for the averaging.
krange - Variable in class elki.application.greedyensemble.ComputeKNNOutlierScores: Range of k.
krange - Variable in class elki.application.greedyensemble.ComputeKNNOutlierScores.Par: k step size
KRANGE_ID - Static variable in class elki.application.greedyensemble.ComputeKNNOutlierScores.Par: Option ID for k parameter range
krate - Variable in class elki.application.statistics.EstimateIntrinsicDimensionality: Number of neighbors to use.
kreach - Variable in class elki.outlier.lof.FlexibleLOF: Number of neighbors used for reachability distance.
kreach - Variable in class elki.outlier.lof.FlexibleLOF.Par: The set size to use for reachability distance.
kreach - Variable in class elki.outlier.lof.LoOP: Reachability neighborhood size.
KREACH_ID - Static variable in class elki.outlier.lof.FlexibleLOF.Par: Parameter to specify the number of nearest neighbors of an object to be considered for computing its reachability distance.
KREF_ID - Static variable in class elki.outlier.lof.FlexibleLOF.Par: Parameter to specify the number of nearest neighbors of an object to be considered for computing its LOF score, must be an integer greater or equal to 1.
krefer - Variable in class elki.outlier.lof.FlexibleLOF: Number of neighbors in comparison set.
krefer - Variable in class elki.outlier.lof.FlexibleLOF.Par: The reference set size to use.
KSQUARE_ID - Static variable in class elki.application.greedyensemble.ComputeKNNOutlierScores.Par: Option ID with an additional bound on k.
ksquarestop - Variable in class elki.application.greedyensemble.ComputeKNNOutlierScores: Maximum k for O(k^2) methods.
ksquarestop - Variable in class elki.application.greedyensemble.ComputeKNNOutlierScores.Par: Maximum k for O(k^2) methods.
KuhnMunkres - Class in elki.utilities.datastructures: Kuhn-Munkres optimal matching (aka the Hungarian algorithm).
KuhnMunkres() - Constructor for class elki.utilities.datastructures.KuhnMunkres
KuhnMunkresStern - Class in elki.utilities.datastructures: A version of Kuhn-Munkres inspired by the implementation of Kevin L.
KuhnMunkresStern() - Constructor for class elki.utilities.datastructures.KuhnMunkresStern
KuhnMunkresWong - Class in elki.utilities.datastructures: Kuhn-Munkres optimal matching (aka the Hungarian algorithm), supposedly in a modern variant.
KuhnMunkresWong() - Constructor for class elki.utilities.datastructures.KuhnMunkresWong
Kulczynski1Similarity - Class in elki.similarity: Kulczynski similarity 1.
Kulczynski1Similarity() - Constructor for class elki.similarity.Kulczynski1Similarity: Deprecated.
Use Kulczynski1Similarity.STATIC instance instead.
Kulczynski1Similarity.Par - Class in elki.similarity: Parameterization class.
Kulczynski2Similarity - Class in elki.similarity: Kulczynski similarity 2.
Kulczynski2Similarity() - Constructor for class elki.similarity.Kulczynski2Similarity: Deprecated.
Use Kulczynski2Similarity.STATIC_CONTINUOUS instance instead.
Kulczynski2Similarity.Par - Class in elki.similarity: Parameterization class.
KullbackLeiblerDivergenceAsymmetricDistance - Class in elki.distance.probabilistic: Kullback-Leibler divergence, also known as relative entropy, information deviation, or just KL-distance (albeit asymmetric).
KullbackLeiblerDivergenceAsymmetricDistance() - Constructor for class elki.distance.probabilistic.KullbackLeiblerDivergenceAsymmetricDistance: Deprecated.
Use static instance!
KullbackLeiblerDivergenceAsymmetricDistance.Par - Class in elki.distance.probabilistic: Parameterization class, using the static instance.
KullbackLeiblerDivergenceReverseAsymmetricDistance - Class in elki.distance.probabilistic: Kullback-Leibler divergence, also known as relative entropy, information deviation or just KL-distance (albeit asymmetric).
KullbackLeiblerDivergenceReverseAsymmetricDistance() - Constructor for class elki.distance.probabilistic.KullbackLeiblerDivergenceReverseAsymmetricDistance: Deprecated.
Use static instance!
KullbackLeiblerDivergenceReverseAsymmetricDistance.Par - Class in elki.distance.probabilistic: Parameterization class, using the static instance.

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