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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
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.
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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