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

A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

K

k - Variable in class de.lmu.ifi.dbs.elki.algorithm.benchmark.KNNBenchmarkAlgorithm: Number of neighbors to retrieve.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.benchmark.KNNBenchmarkAlgorithm.Parameterizer: K parameter
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.benchmark.ValidateApproximativeKNNIndex: Number of neighbors to retrieve.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.benchmark.ValidateApproximativeKNNIndex.Parameterizer: K parameter
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.classification.KNNClassifier: Holds the value of @link #K_PARAM}.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.classification.KNNClassifier.Parameterizer: Holds the value of @link #K_PARAM}.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.AbstractProjectedClustering: Holds the value of AbstractProjectedClustering.Parameterizer.K_ID.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.AbstractProjectedClustering.Parameterizer
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.COPAC.Settings: Neighborhood size.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.ERiC.Settings: Neighborhood size.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.em.EM: Number of clusters
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.em.EM.Parameterizer: Number of clusters.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan.LSDBC: kNN parameter.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan.LSDBC.Parameterizer: kNN parameter.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.AbstractKMeans.Instance: Number of clusters.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.AbstractKMeans: Number of cluster centers to initialize.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.AbstractKMeans.Parameterizer: k Parameter.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.CLARANS: Number of clusters to find.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.CLARANS.Parameterizer: Number of cluster centers to find.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansBisecting: Desired value of k.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansBisecting.Parameterizer: Desired number of clusters.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMedoidsPAM: The number of clusters to produce.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMedoidsPAM.Parameterizer: The number of clusters to produce.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMedoidsPark: Holds the value of KMeans.K_ID.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMedoidsPark.Parameterizer
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.XMeans: Effective number of clusters, minimum and maximum.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.onedimensional.KNNKernelDensityMinimaClustering: Number of neighbors to use for bandwidth.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.onedimensional.KNNKernelDensityMinimaClustering.Parameterizer: Number of neighbors to use for bandwidth.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain.UKMeans: Number of cluster centers to initialize.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain.UKMeans.Parameterizer: Number of cluster centers to initialize.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.KNNDistancesSampler: Parameter k.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.KNNDistancesSampler.KNNDistanceOrderResult: Number of neighbors considered for this KNNDIstanceOrder
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.KNNDistancesSampler.Parameterizer: Parameter k.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.KNNJoin: The k parameter.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.KNNJoin.Parameterizer: K parameter.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.anglebased.FastABOD: Number of nearest neighbors.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.anglebased.FastABOD.Parameterizer: Number of neighbors.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.COP: Number of neighbors to be considered.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.COP.Parameterizer: Number of neighbors to be considered.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.HilOut: Number of nearest neighbors
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.HilOut.Parameterizer: Neighborhood size
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.KNNDD: The parameter k (including query point!)
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.KNNDD.Parameterizer: k parameter
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.KNNOutlier: The parameter k (including query point!)
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.KNNOutlier.Parameterizer: k parameter
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.KNNSOS: Number of neighbors (not including query point).
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.KNNSOS.Parameterizer: Number of neighbors
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.KNNWeightOutlier: Holds the number of nearest neighbors to query (excluding the query point!)
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.KNNWeightOutlier.Parameterizer: k parameter
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.LocalIsolationCoefficient: Holds the number of nearest neighbors to query (including query point!)
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.LocalIsolationCoefficient.Parameterizer: k parameter
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.ODIN: Number of neighbors for kNN graph.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.ODIN.Parameterizer: Number of nearest neighbors to use.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.parallel.KNNWeightProcessor.Instance: k Parameter
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.parallel.KNNWeightProcessor: K parameter
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.parallel.ParallelKNNOutlier: Parameter k
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.parallel.ParallelKNNOutlier.Parameterizer: K parameter
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.parallel.ParallelKNNWeightOutlier: Parameter k
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.parallel.ParallelKNNWeightOutlier.Parameterizer: K parameter
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.ReferenceBasedOutlierDetection: Holds the number of neighbors to use for density estimation.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.ReferenceBasedOutlierDetection.Parameterizer: Holds the value of ReferenceBasedOutlierDetection.Parameterizer.K_ID.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.DWOF: Holds the value of DWOF.Parameterizer.K_ID i.e.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.DWOF.Parameterizer: Number of neighbors to get
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.intrinsic.IntrinsicDimensionalityOutlier: Number of neighbors to use.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.intrinsic.IntrinsicDimensionalityOutlier.Parameterizer: Number of neighbors to use for ID estimation.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.intrinsic.ISOS: Number of neighbors (not including query point).
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.intrinsic.ISOS.Parameterizer: Number of neighbors
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.COF: The number of neighbors to query (including the query point!)
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.COF.Parameterizer: The neighborhood size to use.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.INFLO.Parameterizer: Number of neighbors to use.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LDF: Parameter k.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LDF.Parameterizer: The neighborhood size to use.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LDOF: Number of neighbors to query.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LDOF.Parameterizer: Number of neighbors to use
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LOF: The number of neighbors to query (including the query point!)
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LOF.Parameterizer: The neighborhood size to use.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.parallel.ParallelLOF: Parameter k
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.parallel.ParallelLOF.Parameterizer: K parameter
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.parallel.ParallelSimplifiedLOF: Parameter k
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.parallel.ParallelSimplifiedLOF.Parameterizer: K parameter
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.SimpleKernelDensityLOF: Parameter k.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.SimpleKernelDensityLOF.Parameterizer: The neighborhood size to use.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.SimplifiedLOF: The number of neighbors to query, excluding the query point.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.SimplifiedLOF.Parameterizer: The neighborhood size to use.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.VarianceOfVolume: The number of neighbors to query (including the query point!)
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.VarianceOfVolume.Parameterizer: The neighborhood size to use.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.meta.FeatureBagging: The parameters k for LOF.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.meta.FeatureBagging.Parameterizer: The neighborhood size to use.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.SimpleCOP: Number of neighbors to be considered.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.SimpleCOP.Parameterizer: Number of neighbors to be considered.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.CTLuGLSBackwardSearchAlgorithm: Parameter k - neighborhood size
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.CTLuGLSBackwardSearchAlgorithm.Parameterizer: Parameter k - neighborhood size
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.CTLuRandomWalkEC: Parameter k.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.CTLuRandomWalkEC.Parameterizer: Parameter for kNN.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood.PrecomputedKNearestNeighborNeighborhood.Factory: parameter k
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood.PrecomputedKNearestNeighborNeighborhood.Factory.Parameterizer: Parameter k
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.subspace.AbstractAggarwalYuOutlier: The target dimensionality.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.subspace.AbstractAggarwalYuOutlier.Parameterizer: k Parameter.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.statistics.AveragePrecisionAtK: The parameter k - the number of neighbors to retrieve.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.statistics.AveragePrecisionAtK.Parameterizer: Neighborhood size.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.statistics.HopkinsStatisticClusteringTendency: Nearest neighbor to use.
k - Variable in class de.lmu.ifi.dbs.elki.algorithm.statistics.HopkinsStatisticClusteringTendency.Parameterizer: Nearest neighbor number.
k - Variable in class de.lmu.ifi.dbs.elki.application.cache.CacheDoubleDistanceKNNLists: Number of neighbors to precompute.
k - Variable in class de.lmu.ifi.dbs.elki.application.cache.CacheDoubleDistanceKNNLists.Parameterizer: Number of neighbors to precompute.
k - Variable in class de.lmu.ifi.dbs.elki.data.projection.random.SimplifiedRandomHyperplaneProjectionFamily.SignedProjection: Output dimensionality
k - Variable in class de.lmu.ifi.dbs.elki.database.ids.integer.DoubleIntegerDBIDKNNHeap: k for this heap.
k - Variable in class de.lmu.ifi.dbs.elki.database.ids.integer.DoubleIntegerDBIDKNNList: The k value this list was generated for.
k - Variable in class de.lmu.ifi.dbs.elki.database.ids.integer.IntegerDBIDKNNSubList: Parameter k.
k - Variable in class de.lmu.ifi.dbs.elki.datasource.filter.transform.NumberVectorRandomFeatureSelectionFilter: Holds the desired cardinality of the subset of attributes selected for projection.
k - Variable in class de.lmu.ifi.dbs.elki.datasource.filter.transform.NumberVectorRandomFeatureSelectionFilter.Parameterizer: Number of attributes to select.
k - Variable in class de.lmu.ifi.dbs.elki.evaluation.scores.PrecisionAtKEvaluation: Parameter k.
k - Variable in class de.lmu.ifi.dbs.elki.evaluation.scores.PrecisionAtKEvaluation.Parameterizer: K parameter
k - Variable in class de.lmu.ifi.dbs.elki.index.idistance.InMemoryIDistanceIndex.Factory: Number of reference points
k - Variable in class de.lmu.ifi.dbs.elki.index.idistance.InMemoryIDistanceIndex.Factory.Parameterizer: Number of reference points
k - Variable in class de.lmu.ifi.dbs.elki.index.lsh.hashfamilies.AbstractProjectedHashFunctionFamily: The number of projections to use for each hash function.
k - Variable in class de.lmu.ifi.dbs.elki.index.lsh.hashfamilies.AbstractProjectedHashFunctionFamily.Parameterizer: The number of projections to use for each hash function.
k - Variable in class de.lmu.ifi.dbs.elki.index.lsh.hashfamilies.CosineHashFunctionFamily: The number of projections to use for each hash function.
k - Variable in class de.lmu.ifi.dbs.elki.index.lsh.hashfamilies.CosineHashFunctionFamily.Parameterizer: The number of projections to use for each hash function.
k - Variable in class de.lmu.ifi.dbs.elki.index.preprocessed.knn.AbstractMaterializeKNNPreprocessor.Factory: Holds the value of AbstractMaterializeKNNPreprocessor.Factory.K_ID.
k - Variable in class de.lmu.ifi.dbs.elki.index.preprocessed.knn.AbstractMaterializeKNNPreprocessor.Factory.Parameterizer: Holds the value of AbstractMaterializeKNNPreprocessor.Factory.K_ID.
k - Variable in class de.lmu.ifi.dbs.elki.index.preprocessed.knn.AbstractMaterializeKNNPreprocessor: The query k value.
k - Variable in class de.lmu.ifi.dbs.elki.index.preprocessed.localpca.KNNQueryFilteredPCAIndex.Factory: Number of neighbors to query.
k - Variable in class de.lmu.ifi.dbs.elki.index.preprocessed.localpca.KNNQueryFilteredPCAIndex.Factory.Parameterizer: Number of neighbors to query.
k - Variable in class de.lmu.ifi.dbs.elki.index.preprocessed.localpca.KNNQueryFilteredPCAIndex: Number of neighbors to query.
k - Variable in class de.lmu.ifi.dbs.elki.index.preprocessed.preference.HiSCPreferenceVectorIndex.Factory: The number of nearest neighbors considered to determine the preference vector.
k - Variable in class de.lmu.ifi.dbs.elki.index.preprocessed.preference.HiSCPreferenceVectorIndex.Factory.Parameterizer: The number of nearest neighbors considered to determine the preference vector.
k - Variable in class de.lmu.ifi.dbs.elki.index.preprocessed.preference.HiSCPreferenceVectorIndex: The number of nearest neighbors considered to determine the preference vector.
k - Variable in class de.lmu.ifi.dbs.elki.math.statistics.distribution.ExpGammaDistribution: Alpha == k.
k - Variable in class de.lmu.ifi.dbs.elki.math.statistics.distribution.ExpGammaDistribution.Parameterizer: Alpha == k.
k - Variable in class de.lmu.ifi.dbs.elki.math.statistics.distribution.GammaDistribution: Alpha == k
k - Variable in class de.lmu.ifi.dbs.elki.math.statistics.distribution.GammaDistribution.Parameterizer: Parameters.
k - Variable in class de.lmu.ifi.dbs.elki.math.statistics.distribution.GeneralizedExtremeValueDistribution: Parameters (location, scale, shape)
k - Variable in class de.lmu.ifi.dbs.elki.math.statistics.distribution.GeneralizedExtremeValueDistribution.Parameterizer: Parameters.
k - Variable in class de.lmu.ifi.dbs.elki.math.statistics.distribution.LogGammaDistribution: Alpha == k.
k - Variable in class de.lmu.ifi.dbs.elki.math.statistics.distribution.LogGammaDistribution.Parameterizer: Parameters.
k - Variable in class de.lmu.ifi.dbs.elki.math.statistics.distribution.WeibullDistribution: Shape parameter k.
k - Variable in class de.lmu.ifi.dbs.elki.math.statistics.distribution.WeibullDistribution.Parameterizer: Parameters.
k - Variable in class de.lmu.ifi.dbs.elki.parallel.processor.KDistanceProcessor.Instance: k Parameter
k - Variable in class de.lmu.ifi.dbs.elki.parallel.processor.KDistanceProcessor: K parameter
k - Variable in class de.lmu.ifi.dbs.elki.parallel.processor.KNNProcessor.Instance: k Parameter
k - Variable in class de.lmu.ifi.dbs.elki.parallel.processor.KNNProcessor: K parameter
k - Variable in class de.lmu.ifi.dbs.elki.utilities.scaling.outlier.OutlierGammaScaling: Gamma parameter k
k - Variable in class de.lmu.ifi.dbs.elki.utilities.scaling.outlier.TopKOutlierScaling: Number of outliers to keep.
k - Variable in class de.lmu.ifi.dbs.elki.utilities.scaling.outlier.TopKOutlierScaling.Parameterizer: Number of outliers to keep.
k - Variable in class tutorial.clustering.SameSizeKMeansAlgorithm.Parameterizer: k Parameter.
k - Variable in class tutorial.outlier.DistanceStddevOutlier: Number of neighbors to get.
k - Variable in class tutorial.outlier.DistanceStddevOutlier.Parameterizer: Number of neighbors to get
k - Variable in class tutorial.outlier.ODIN: Number of neighbors for kNN graph.
k - Variable in class tutorial.outlier.ODIN.Parameterizer: Number of nearest neighbors to use.
k_0 - Variable in class de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkcop.ApproximationLine: The start value for k.
k_c - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.intrinsic.IDOS: kNN for the context set (ID computation).
k_c - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.intrinsic.IDOS.Parameterizer: kNN for the context set (ID computation).
k_i - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.AbstractProjectedClustering: Holds the value of AbstractProjectedClustering.Parameterizer.K_I_ID.
k_i - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.AbstractProjectedClustering.Parameterizer
K_I_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.AbstractProjectedClustering.Parameterizer: Parameter to specify the multiplier for the initial number of seeds, must be an integer greater than 0.
K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.benchmark.KNNBenchmarkAlgorithm.Parameterizer: Parameter for the number of neighbors.
K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.benchmark.ValidateApproximativeKNNIndex.Parameterizer: Parameter for the number of neighbors.
K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.classification.KNNClassifier.Parameterizer: Parameter to specify the number of neighbors to take into account for classification, must be an integer greater than 0.
K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.AbstractProjectedClustering.Parameterizer: Parameter to specify the number of clusters to find, must be an integer greater than 0.
K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.COPAC.Parameterizer: Size for the kNN neighborhood used in the PCA step of COPAC.
K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.ERiC.Parameterizer: Size for the kNN neighborhood used in the PCA step of ERiC.
K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.HiCO.Parameterizer: 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 de.lmu.ifi.dbs.elki.algorithm.clustering.em.EM.Parameterizer: Parameter to specify the number of clusters to find, must be an integer greater than 0.
K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan.LSDBC.Parameterizer: Parameter for neighborhood size.
K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.extraction.ClustersWithNoiseExtraction.Parameterizer: The number of clusters to extract.
K_ID - Static variable in interface de.lmu.ifi.dbs.elki.algorithm.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 de.lmu.ifi.dbs.elki.algorithm.clustering.onedimensional.KNNKernelDensityMinimaClustering.Parameterizer: Number of neighbors for bandwidth estimation.
K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.HiSC.Parameterizer: The number of nearest neighbors considered to determine the preference vector.
K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.KNNDistancesSampler.Parameterizer: 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 de.lmu.ifi.dbs.elki.algorithm.KNNJoin.Parameterizer: Parameter that specifies the k-nearest neighbors to be assigned, must be an integer greater than 0.
K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.anglebased.FastABOD.Parameterizer: Parameter for the nearest neighbors.
K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.COP.Parameterizer: 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 de.lmu.ifi.dbs.elki.algorithm.outlier.distance.HilOut.Parameterizer: Parameter to specify how many next neighbors should be used in the computation
K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.KNNDD.Parameterizer: Parameter to specify the k nearest neighbor
K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.KNNOutlier.Parameterizer: Parameter to specify the k nearest neighbor
K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.KNNWeightOutlier.Parameterizer: Parameter to specify the k nearest neighbor.
K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.LocalIsolationCoefficient.Parameterizer: Parameter to specify the k nearest neighbor.
K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.ODIN.Parameterizer: Parameter for the number of nearest neighbors: -odin.k <int>
K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.ReferenceBasedOutlierDetection.Parameterizer: Parameter to specify 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 de.lmu.ifi.dbs.elki.algorithm.outlier.DWOF.Parameterizer: Option ID for the number of neighbors.
K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.intrinsic.IntrinsicDimensionalityOutlier.Parameterizer: Parameter for the number of neighbors.
K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.COF.Parameterizer: Parameter to specify the neighborhood size for COF.
K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.INFLO.Parameterizer: 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 de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LDF.Parameterizer: Option ID for k
K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LDOF.Parameterizer: 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 de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LOF.Parameterizer: Parameter to specify the number of nearest neighbors of an object to be considered for computing its LOF score, must be an integer greater than or equal to 1.
K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.VarianceOfVolume.Parameterizer: Parameter to specify the number of nearest neighbors of an object to be considered for computing its VOV score, must be an integer greater than or equal to 1.
K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.SimpleCOP.Parameterizer: 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 de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.CTLuGLSBackwardSearchAlgorithm.Parameterizer: Parameter to specify the k nearest neighbors
K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.CTLuRandomWalkEC.Parameterizer: Parameter to specify the number of neighbors.
K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood.PrecomputedKNearestNeighborNeighborhood.Factory.Parameterizer: Parameter k
K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.subspace.AbstractAggarwalYuOutlier.Parameterizer: OptionID for the target dimensionality.
K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.statistics.AveragePrecisionAtK.Parameterizer: Parameter k to compute the average precision at.
K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.statistics.HopkinsStatisticClusteringTendency.Parameterizer: Parameter for k.
K_ID - Static variable in class de.lmu.ifi.dbs.elki.application.cache.CacheDoubleDistanceKNNLists.Parameterizer: Parameter that specifies the number of neighbors to precompute.
K_ID - Static variable in class de.lmu.ifi.dbs.elki.evaluation.scores.PrecisionAtKEvaluation.Parameterizer: Option ID for the k parameter.
K_ID - Static variable in class de.lmu.ifi.dbs.elki.index.idistance.InMemoryIDistanceIndex.Factory.Parameterizer: Number of reference points.
K_ID - Static variable in class de.lmu.ifi.dbs.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 de.lmu.ifi.dbs.elki.index.preprocessed.localpca.KNNQueryFilteredPCAIndex.Factory.Parameterizer: 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 de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkapp.MkAppTreeFactory.Parameterizer: Parameter for k
K_ID - Static variable in class de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkcop.MkCopTreeFactory.Parameterizer: Parameter for k
K_ID - Static variable in class de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rdknn.RdKNNTreeFactory: Parameter for k
K_ID - Static variable in class de.lmu.ifi.dbs.elki.math.statistics.distribution.ExpGammaDistribution.Parameterizer: k parameter, same as GammaDistribution.Parameterizer.K_ID.
K_ID - Static variable in class de.lmu.ifi.dbs.elki.math.statistics.distribution.GammaDistribution.Parameterizer: K parameter.
K_ID - Static variable in class de.lmu.ifi.dbs.elki.utilities.scaling.outlier.TopKOutlierScaling.Parameterizer: Parameter to specify the number of outliers to keep
K_ID - Static variable in class tutorial.outlier.DistanceStddevOutlier.Parameterizer: Option ID for parameterization.
K_ID - Static variable in class tutorial.outlier.ODIN.Parameterizer: Parameter for the number of nearest neighbors: -odin.k <int>
k_max - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.XMeans: Effective number of clusters, minimum and maximum.
k_max - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.XMeans.Parameterizer: Minimum and maximum number of result clusters.
k_max - Variable in class de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.MkTreeHeader: The maximum number k of reverse kNN queries to be supported.
k_max - Variable in class de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rdknn.RdkNNSettings: Parameter k.
k_max - Variable in class de.lmu.ifi.dbs.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 de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.AbstractMkTreeUnifiedFactory.Parameterizer: 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 de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.XMeans: Effective number of clusters, minimum and maximum.
k_min - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.XMeans.Parameterizer: Minimum and maximum number of result clusters.
K_MIN_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.XMeans.Parameterizer: Minimum number of clusters.
K_MULTIPLIER_ID - Static variable in class de.lmu.ifi.dbs.elki.index.projected.ProjectedIndex.Factory.Parameterizer: Option ID for querying a larger k.
k_r - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.intrinsic.IDOS: kNN for the reference set.
k_r - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.intrinsic.IDOS.Parameterizer: kNN for the reference set.
K_S_CRITICAL001 - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.subspace.OUTRES: Constant for Kolmogorov-Smirnov at alpha=0.01 (table value)
kappa - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.FourC.Settings: Kappa penalty parameter, to punish deviation in low-variance Eigenvectors.
kappa - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.PreDeCon.Settings: The kappa penality factor for deviations in preferred dimensions.
KAPPA - Static variable in class de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.cluster.EMClusterVisualization.Instance: Kappa constant.
KAPPA_DEFAULT - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.FourC.Settings.Parameterizer: Default for kappa parameter.
KAPPA_DEFAULT - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.PreDeCon.Settings.Parameterizer: Default for kappa parameter.
KAPPA_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.FourC.Settings.Parameterizer: Parameter Kappa: penalty for deviations in preferred dimensions.
KAPPA_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.PreDeCon.Settings.Parameterizer: Parameter Kappa: penalty for deviations in preferred dimensions.
KappaDistribution - Class in de.lmu.ifi.dbs.elki.math.statistics.distribution: Kappa distribution, by Hosking.
KappaDistribution(double, double, double, double) - Constructor for class de.lmu.ifi.dbs.elki.math.statistics.distribution.KappaDistribution: Constructor.
KappaDistribution(double, double, double, double, Random) - Constructor for class de.lmu.ifi.dbs.elki.math.statistics.distribution.KappaDistribution: Constructor.
KappaDistribution(double, double, double, double, RandomFactory) - Constructor for class de.lmu.ifi.dbs.elki.math.statistics.distribution.KappaDistribution: Constructor.
KappaDistribution.Parameterizer - Class in de.lmu.ifi.dbs.elki.math.statistics.distribution: Parameterization class
KC_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.intrinsic.IDOS.Parameterizer: Parameter to specify the number of nearest neighbors of an object to be used for the GED computation.
kcomp - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LoOP: Comparison neighborhood size.
kcomp - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LoOP.Parameterizer: Holds the value of LoOP.Parameterizer.KCOMP_ID.
KCOMP_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LoOP.Parameterizer: Parameter to specify the number of nearest neighbors of an object to be considered for computing its LOOP_SCORE, must be an integer greater than 1.
KDDCLIApplication - Class in de.lmu.ifi.dbs.elki.application: Basic command line application for Knowledge Discovery in Databases use cases.
KDDCLIApplication(KDDTask) - Constructor for class de.lmu.ifi.dbs.elki.application.KDDCLIApplication: Constructor.
KDDCLIApplication.Parameterizer - Class in de.lmu.ifi.dbs.elki.application: Parameterization class.
KDDTask - Class in de.lmu.ifi.dbs.elki: KDDTask encapsulates the common workflow of an unsupervised knowledge discovery task.
KDDTask(InputStep, AlgorithmStep, EvaluationStep, OutputStep, Collection<TrackedParameter>) - Constructor for class de.lmu.ifi.dbs.elki.KDDTask: Constructor.
KDDTask.Parameterizer - Class in de.lmu.ifi.dbs.elki: Parameterization class.
KDEOS<O> - Class in de.lmu.ifi.dbs.elki.algorithm.outlier.lof: Generalized Outlier Detection with Flexible Kernel Density Estimates.
KDEOS(DistanceFunction<? super O>, int, int, KernelDensityFunction, double, double, int) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.KDEOS: Constructor.
KDEOS.Parameterizer<O> - Class in de.lmu.ifi.dbs.elki.algorithm.outlier.lof: Parameterization class
kdist - Variable in class de.lmu.ifi.dbs.elki.database.ids.integer.DoubleIntegerDBIDKNNHeap: Current maximum value.
KDistanceProcessor - Class in de.lmu.ifi.dbs.elki.parallel.processor: Compute the kNN distance for each object.
KDistanceProcessor(int) - Constructor for class de.lmu.ifi.dbs.elki.parallel.processor.KDistanceProcessor: Constructor.
KDistanceProcessor.Instance - Class in de.lmu.ifi.dbs.elki.parallel.processor: Instance for precomputing the kNN.
kdists - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.parallel.LRDProcessor: k-distance store
kdKNNSearch(int, int, int, O, KNNHeap, DBIDArrayIter, double) - Method in class de.lmu.ifi.dbs.elki.index.tree.spatial.kd.MinimalisticMemoryKDTree.KDTreeKNNQuery: Perform a kNN search on the kd-tree.
kdKNNSearch(int, int, int, O, KNNHeap, DoubleDBIDListIter, double) - Method in class de.lmu.ifi.dbs.elki.index.tree.spatial.kd.SmallMemoryKDTree.KDTreeKNNQuery: Perform a kNN search on the kd-tree.
kdRangeSearch(int, int, int, O, ModifiableDoubleDBIDList, DBIDArrayIter, double) - Method in class de.lmu.ifi.dbs.elki.index.tree.spatial.kd.MinimalisticMemoryKDTree.KDTreeRangeQuery: Perform a kNN search on the kd-tree.
kdRangeSearch(int, int, int, O, ModifiableDoubleDBIDList, DoubleDBIDListIter, double) - Method in class de.lmu.ifi.dbs.elki.index.tree.spatial.kd.SmallMemoryKDTree.KDTreeRangeQuery: Perform a kNN search on the kd-tree.
KDTreeKNNQuery(DistanceQuery<O>, Norm<? super O>) - Constructor for class de.lmu.ifi.dbs.elki.index.tree.spatial.kd.MinimalisticMemoryKDTree.KDTreeKNNQuery: Constructor.
KDTreeKNNQuery(DistanceQuery<O>, Norm<? super O>) - Constructor for class de.lmu.ifi.dbs.elki.index.tree.spatial.kd.SmallMemoryKDTree.KDTreeKNNQuery: Constructor.
KDTreeRangeQuery(DistanceQuery<O>, Norm<? super O>) - Constructor for class de.lmu.ifi.dbs.elki.index.tree.spatial.kd.MinimalisticMemoryKDTree.KDTreeRangeQuery: Constructor.
KDTreeRangeQuery(DistanceQuery<O>, Norm<? super O>) - Constructor for class de.lmu.ifi.dbs.elki.index.tree.spatial.kd.SmallMemoryKDTree.KDTreeRangeQuery: Constructor.
keep - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain.RepresentativeUncertainClustering: Keep all samples (not only the representative results)
keep - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain.RepresentativeUncertainClustering.Parameterizer: Keep all samples (not only the representative results).
keep - Variable in class de.lmu.ifi.dbs.elki.algorithm.projection.AbstractProjectionAlgorithm: Keep the original data relation.
keep - Variable in class de.lmu.ifi.dbs.elki.algorithm.projection.SNE.Parameterizer: Keep the original data relation.
keep - Variable in class de.lmu.ifi.dbs.elki.algorithm.projection.TSNE.Parameterizer: Keep the original data relation.
keep - Variable in class de.lmu.ifi.dbs.elki.datasource.filter.typeconversions.UncertainifyFilter: Flag to keep the original data.
keep - Variable in class de.lmu.ifi.dbs.elki.datasource.filter.typeconversions.UncertainifyFilter.Parameterizer: Flag to keep the original data.
KEEP_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.projection.AbstractProjectionAlgorithm: Flag to keep the original projection
KEEP_ID - Static variable in class de.lmu.ifi.dbs.elki.datasource.filter.typeconversions.UncertainifyFilter.Parameterizer: Flag to keep the original data.
KEEP_SAMPLES_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain.RepresentativeUncertainClustering.Parameterizer: Flag to keep all samples.
keepfirst - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization.FarthestPointsInitialMeans.Parameterizer: Flag for discarding the first object chosen.
keepfirst - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization.FarthestSumPointsInitialMeans.Parameterizer: Flag for discarding the first object chosen.
KEEPFIRST_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization.FarthestPointsInitialMeans.Parameterizer: Option ID to control the handling of the first object chosen.
keepmed - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.CLARA: Keep the previous medoids in the sample (see page 145).
keepmed - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.CLARA.Parameterizer: Keep the previous medoids in the sample.
keepmed - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.FastCLARA: Keep the previous medoids in the sample (see page 145).
keepmed - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.FastCLARA.Parameterizer: Keep the previous medoids in the sample.
keepsteep - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.optics.OPTICSXi: Keep the steep areas, for visualization.
keepsteep - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.optics.OPTICSXi.Parameterizer
KEEPSTEEP_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.optics.OPTICSXi.Parameterizer: Parameter to keep the steep areas
kernel - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.NaiveMeanShiftClustering: Density estimation kernel.
kernel - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.NaiveMeanShiftClustering.Parameterizer: Kernel function.
kernel - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.onedimensional.KNNKernelDensityMinimaClustering: Kernel density function.
kernel - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.onedimensional.KNNKernelDensityMinimaClustering.Parameterizer: Kernel density function.
kernel - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.KDEOS: Kernel function to use for density estimation.
kernel - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.KDEOS.Parameterizer: Kernel function to use for density estimation.
kernel - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LDF: Kernel density function
kernel - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LDF.Parameterizer: Kernel density function parameter
kernel - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.SimpleKernelDensityLOF: Kernel density function
kernel - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.SimpleKernelDensityLOF.Parameterizer: Kernel density function parameter
kernel - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.subspace.OUTRES.KernelDensityEstimator: Actual kernel in use
kernel - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.svm.LibSVMOneClassOutlierDetection: Kernel function in use.
kernel - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.svm.LibSVMOneClassOutlierDetection.Parameterizer: Kernel in use.
kernel - Variable in class de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel.KernelMatrix: The kernel matrix
KERNEL - Static variable in class de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions.BiweightKernelDensityFunction: Static instance.
KERNEL - Static variable in class de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions.CosineKernelDensityFunction: Static instance.
KERNEL - Static variable in class de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions.EpanechnikovKernelDensityFunction: Static instance.
KERNEL - Static variable in class de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions.GaussianKernelDensityFunction: Static instance.
KERNEL - Static variable in class de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions.TriangularKernelDensityFunction: Static instance.
KERNEL - Static variable in class de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions.TricubeKernelDensityFunction: Static instance.
KERNEL - Static variable in class de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions.TriweightKernelDensityFunction: Static instance.
KERNEL - Static variable in class de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions.UniformKernelDensityFunction: Static instance.
KERNEL_FUNCTION_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.anglebased.ABOD.Parameterizer: Parameter for the kernel function.
KERNEL_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.NaiveMeanShiftClustering.Parameterizer: Parameter for kernel function.
KERNEL_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.onedimensional.KNNKernelDensityMinimaClustering.Parameterizer: Kernel function.
KERNEL_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.KDEOS.Parameterizer: Parameter to specify the kernel density function.
KERNEL_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LDF.Parameterizer: Option ID for kernel.
KERNEL_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.SimpleKernelDensityLOF.Parameterizer: Option ID for kernel density LOF kernel.
KERNEL_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.svm.LibSVMOneClassOutlierDetection.Parameterizer: Parameter for kernel function.
KERNEL_MIN_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.KDEOS.Parameterizer: Parameter to specify the minimum bandwidth.
KERNEL_SCALE_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.KDEOS.Parameterizer: Parameter to specify the kernel scaling factor.
KernelDensityEstimator(Relation<? extends NumberVector>, double) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.outlier.subspace.OUTRES.KernelDensityEstimator: Constructor.
KernelDensityEstimator - Class in de.lmu.ifi.dbs.elki.math.statistics: Estimate density given an array of points.
KernelDensityEstimator(double[], double, double, KernelDensityFunction, int, double) - Constructor for class de.lmu.ifi.dbs.elki.math.statistics.KernelDensityEstimator: Initialize and execute kernel density estimation.
KernelDensityEstimator(double[], KernelDensityFunction, double) - Constructor for class de.lmu.ifi.dbs.elki.math.statistics.KernelDensityEstimator: Process an array of data
KernelDensityFunction - Interface in de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions: Inner function of a kernel density estimator.
kernelFunction - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.anglebased.ABOD: Store the configured Kernel version.
kernelFunction - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.anglebased.ABOD.Parameterizer: Distance function.
KernelMatrix - Class in de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel: Kernel matrix representation.
KernelMatrix(PrimitiveSimilarityFunction<? super O>, Relation<? extends O>, DBIDs) - Constructor for class de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel.KernelMatrix: Provides a new kernel matrix.
KernelMatrix(SimilarityQuery<? super O>, Relation<? extends O>, DBIDs) - Constructor for class de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel.KernelMatrix: Provides a new kernel matrix.
KernelMatrix(double[][]) - Constructor for class de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel.KernelMatrix: Makes a new kernel matrix from matrix (with data copying).
KernelMatrix.DBIDMap - Interface in de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel: Map a DBID to its offset TODO: move to shared code.
KernelMatrix.RangeMap - Class in de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel: Map a DBID to an integer offset, DBIDRange version.
KernelMatrix.SortedArrayMap - Class in de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel: Map a DBID to an integer offset, Version to support arbitrary DBIDs.
KEY - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.em.EM: Key for statistics logging.
key - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.AbstractKMeans.Instance: Key for statistics logging.
KEY - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMedoidsFastPAM: Key for statistics logging.
KEY - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMedoidsFastPAM1: Key for statistics logging.
KEY - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMedoidsPAMReynolds: Key for statistics logging.
KEY - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMedoidsPark: Key for statistics logging.
KEY - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.XMeans: Key for statistics logging.
KEY - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain.UKMeans: Key for statistics logging.
key - Variable in class de.lmu.ifi.dbs.elki.algorithm.itemsetmining.FPGrowth.FPNode: Key, weight, and number of children.
key - Variable in class de.lmu.ifi.dbs.elki.evaluation.clustering.internal.EvaluateCIndex: Key for logging statistics.
key - Variable in class de.lmu.ifi.dbs.elki.evaluation.clustering.internal.EvaluateConcordantPairs: Key for logging statistics.
key - Variable in class de.lmu.ifi.dbs.elki.evaluation.clustering.internal.EvaluateDaviesBouldin: Key for logging statistics.
key - Variable in class de.lmu.ifi.dbs.elki.evaluation.clustering.internal.EvaluatePBMIndex: Key for logging statistics.
key - Variable in class de.lmu.ifi.dbs.elki.evaluation.clustering.internal.EvaluateSilhouette: Key for logging statistics.
key - Variable in class de.lmu.ifi.dbs.elki.evaluation.clustering.internal.EvaluateSimplifiedSilhouette: Key for logging statistics.
key - Variable in class de.lmu.ifi.dbs.elki.evaluation.clustering.internal.EvaluateSquaredErrors: Key for logging statistics.
key - Variable in class de.lmu.ifi.dbs.elki.evaluation.clustering.internal.EvaluateVarianceRatioCriteria: Key for logging statistics.
key - Variable in class de.lmu.ifi.dbs.elki.evaluation.outlier.OutlierRankingEvaluation: Key prefix for statistics logging.
key - Variable in class de.lmu.ifi.dbs.elki.logging.statistics.AbstractStatistic: Key to report the statistic with.
key(PlotItem, VisualizationTask) - Method in class de.lmu.ifi.dbs.elki.visualization.gui.overview.LayerMap: Helper function for building a key object
KEY - Static variable in interface de.lmu.ifi.dbs.elki.visualization.style.StyleLibrary: Key
KEY_CAPTION - Static variable in class de.lmu.ifi.dbs.elki.visualization.visualizers.visunproj.DendrogramVisualization.Instance: CSS class for key captions.
KEY_CAPTION - Static variable in class de.lmu.ifi.dbs.elki.visualization.visualizers.visunproj.KeyVisualization.Instance: CSS class for key captions.
KEY_ENTRY - Static variable in class de.lmu.ifi.dbs.elki.visualization.visualizers.visunproj.KeyVisualization.Instance: CSS class for key entries.
KEY_HIERLINE - Static variable in class de.lmu.ifi.dbs.elki.visualization.visualizers.visunproj.DendrogramVisualization.Instance: CSS class for hierarchy plot lines
KEY_HIERLINE - Static variable in class de.lmu.ifi.dbs.elki.visualization.visualizers.visunproj.KeyVisualization.Instance: CSS class for hierarchy plot lines
keymap - Variable in class de.lmu.ifi.dbs.elki.datasource.parser.SimpleTransactionParser: Map.
keymap - Variable in class de.lmu.ifi.dbs.elki.datasource.parser.TermFrequencyParser: Map.
keyPressed(KeyEvent) - Method in class de.lmu.ifi.dbs.elki.gui.util.ParameterTable.ClassListEditor
keyPressed(KeyEvent) - Method in class de.lmu.ifi.dbs.elki.gui.util.ParameterTable.DropdownEditor
keyPressed(KeyEvent) - Method in class de.lmu.ifi.dbs.elki.gui.util.ParameterTable.FileNameEditor
keyPressed(KeyEvent) - Method in class de.lmu.ifi.dbs.elki.gui.util.ParameterTable.Handler
keyPressed(KeyEvent) - Method in class de.lmu.ifi.dbs.elki.gui.util.TreePopup.Handler
keyReleased(KeyEvent) - Method in class de.lmu.ifi.dbs.elki.gui.util.ParameterTable.ClassListEditor
keyReleased(KeyEvent) - Method in class de.lmu.ifi.dbs.elki.gui.util.ParameterTable.DropdownEditor
keyReleased(KeyEvent) - Method in class de.lmu.ifi.dbs.elki.gui.util.ParameterTable.FileNameEditor
keyReleased(KeyEvent) - Method in class de.lmu.ifi.dbs.elki.gui.util.ParameterTable.Handler
keyReleased(KeyEvent) - Method in class de.lmu.ifi.dbs.elki.gui.util.TreePopup.Handler
keys - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LOCI.DoubleIntArrayList: Double keys
keySet() - Method in class de.lmu.ifi.dbs.elki.visualization.gui.overview.RectangleArranger: The item keys contained in the map.
keyTyped(KeyEvent) - Method in class de.lmu.ifi.dbs.elki.gui.util.ParameterTable.ClassListEditor
keyTyped(KeyEvent) - Method in class de.lmu.ifi.dbs.elki.gui.util.ParameterTable.DropdownEditor
keyTyped(KeyEvent) - Method in class de.lmu.ifi.dbs.elki.gui.util.ParameterTable.FileNameEditor
keyTyped(KeyEvent) - Method in class de.lmu.ifi.dbs.elki.gui.util.ParameterTable.Handler
keyTyped(KeyEvent) - Method in class de.lmu.ifi.dbs.elki.gui.util.TreePopup.Handler
KeyVisualization - Class in de.lmu.ifi.dbs.elki.visualization.visualizers.visunproj: Visualizer, displaying the key for a clustering.
KeyVisualization() - Constructor for class de.lmu.ifi.dbs.elki.visualization.visualizers.visunproj.KeyVisualization
KeyVisualization.Instance - Class in de.lmu.ifi.dbs.elki.visualization.visualizers.visunproj: Instance
Klosgen - Class in de.lmu.ifi.dbs.elki.algorithm.itemsetmining.associationrules.interest: Klösgen interestingness measure.
Klosgen() - Constructor for class de.lmu.ifi.dbs.elki.algorithm.itemsetmining.associationrules.interest.Klosgen: Constructor.
kmax - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.KDEOS: Minimum and maximum number of neighbors to use.
kmax - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.KDEOS.Parameterizer: Minimum and maximum number of neighbors to use.
kmax - Variable in class de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.MkTreeSettings: Holds the maximum value of k to support.
KMAX_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.KDEOS.Parameterizer: Maximum value of k to analyze.
KMeans<V extends NumberVector,M extends Model> - Interface in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans: Some constants and options shared among kmeans family algorithms.
kmeans - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain.CKMeans.Parameterizer: K-means instance to use.
KMEANS_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.BestOfMultipleKMeans.Parameterizer: Parameter to specify the kMeans variant.
KMEANS_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization.SampleKMeansInitialization.Parameterizer: Parameter to specify the kMeans variant.
KMEANS_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansBisecting.Parameterizer: Parameter to specify the kMeans variant.
KMeansAnnulus<V extends NumberVector> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans: Annulus k-means algorithm.
KMeansAnnulus(NumberVectorDistanceFunction<? super V>, int, int, KMeansInitialization, boolean) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansAnnulus: Constructor.
KMeansAnnulus.Instance - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans: Inner instance, storing state for a single data set.
KMeansAnnulus.Parameterizer<V extends NumberVector> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans: Parameterization class.
KMeansBisecting<V extends NumberVector,M extends MeanModel> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans: The bisecting k-means algorithm works by starting with an initial partitioning into two clusters, then repeated splitting of the largest cluster to get additional clusters.
KMeansBisecting(int, KMeans<V, M>) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansBisecting: Constructor.
KMeansBisecting.Parameterizer<V extends NumberVector,M extends MeanModel> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans: Parameterization class.
KMEANSBORDER - Static variable in class de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.cluster.VoronoiVisualization: Generic tags to indicate the type of element.
KMeansCompare<V extends NumberVector> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans: Compare-Means: Accelerated k-means by exploiting the triangle inequality and pairwise distances of means to prune candidate means.
KMeansCompare(NumberVectorDistanceFunction<? super V>, int, int, KMeansInitialization) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansCompare: Constructor.
KMeansCompare.Instance - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans: Inner instance, storing state for a single data set.
KMeansCompare.Parameterizer<V extends NumberVector> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans: Parameterization class.
KMeansElkan<V extends NumberVector> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans: Elkan's fast k-means by exploiting the triangle inequality.
KMeansElkan(NumberVectorDistanceFunction<? super V>, int, int, KMeansInitialization, boolean) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansElkan: Constructor.
KMeansElkan.Instance - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans: Inner instance, storing state for a single data set.
KMeansElkan.Parameterizer<V extends NumberVector> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans: Parameterization class.
KMeansExponion<V extends NumberVector> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans: Newlings's exponion k-means algorithm, exploiting the triangle inequality.
KMeansExponion(NumberVectorDistanceFunction<? super V>, int, int, KMeansInitialization, boolean) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansExponion: Constructor.
KMeansExponion.Instance - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans: Inner instance, storing state for a single data set.
KMeansExponion.Parameterizer<V extends NumberVector> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans: Parameterization class.
KMeansHamerly<V extends NumberVector> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans: Hamerly's fast k-means by exploiting the triangle inequality.
KMeansHamerly(NumberVectorDistanceFunction<? super V>, int, int, KMeansInitialization, boolean) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansHamerly: Constructor.
KMeansHamerly.Instance - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans: Inner instance, storing state for a single data set.
KMeansHamerly.Parameterizer<V extends NumberVector> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans: Parameterization class.
KMeansInitialization - Interface in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization: Interface for initializing K-Means
KMeansLloyd<V extends NumberVector> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans: The standard k-means algorithm, using bulk iterations and commonly attributed to Lloyd and Forgy (independently).
KMeansLloyd(NumberVectorDistanceFunction<? super V>, int, int, KMeansInitialization) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansLloyd: Constructor.
KMeansLloyd.Instance - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans: Inner instance, storing state for a single data set.
KMeansLloyd.Parameterizer<V extends NumberVector> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans: Parameterization class.
KMeansMacQueen<V extends NumberVector> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans: The original k-means algorithm, using MacQueen style incremental updates; making this effectively an "online" (streaming) algorithm.
KMeansMacQueen(NumberVectorDistanceFunction<? super V>, int, int, KMeansInitialization) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansMacQueen: Constructor.
KMeansMacQueen.Instance - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans: Inner instance, storing state for a single data set.
KMeansMacQueen.Parameterizer<V extends NumberVector> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans: Parameterization class.
KMeansMinusMinus<V extends NumberVector> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans: k-means--: A Unified Approach to Clustering and Outlier Detection.
KMeansMinusMinus(NumberVectorDistanceFunction<? super V>, int, int, KMeansInitialization, double, boolean) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansMinusMinus: Constructor.
KMeansMinusMinus.Instance - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans: Inner instance, storing state for a single data set.
KMeansMinusMinus.Parameterizer<V extends NumberVector> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans: Parameterization class.
KMeansModel - Class in de.lmu.ifi.dbs.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 de.lmu.ifi.dbs.elki.data.model.KMeansModel: Constructor with mean.
KMeansOutlierDetection<O extends NumberVector> - Class in de.lmu.ifi.dbs.elki.algorithm.outlier.clustering: Outlier detection by using k-means clustering.
KMeansOutlierDetection(KMeans<O, ?>) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.outlier.clustering.KMeansOutlierDetection: Constructor.
KMeansOutlierDetection.Parameterizer<O extends NumberVector> - Class in de.lmu.ifi.dbs.elki.algorithm.outlier.clustering: Parameterizer.
KMeansPlusPlusInitialMeans<O> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization: K-Means++ initialization for k-means.
KMeansPlusPlusInitialMeans(RandomFactory) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization.KMeansPlusPlusInitialMeans: Constructor.
KMeansPlusPlusInitialMeans.Parameterizer<V> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization: Parameterization class.
KMeansProcessor<V extends NumberVector> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.parallel: Parallel k-means implementation.
KMeansProcessor(Relation<V>, NumberVectorDistanceFunction<? super V>, WritableIntegerDataStore, double[]) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.parallel.KMeansProcessor: Constructor.
KMeansProcessor.Instance<V extends NumberVector> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.parallel: Instance to process part of the data set, for a single iteration.
KMeansQualityMeasure<O extends NumberVector> - Interface in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.quality: Interface for computing the quality of a K-Means clustering.
KMeansSimplifiedElkan<V extends NumberVector> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans: Simplified version of Elkan's k-means by exploiting the triangle inequality.
KMeansSimplifiedElkan(NumberVectorDistanceFunction<? super V>, int, int, KMeansInitialization, boolean) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansSimplifiedElkan: Constructor.
KMeansSimplifiedElkan.Instance - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans: Inner instance, storing state for a single data set.
KMeansSimplifiedElkan.Parameterizer<V extends NumberVector> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans: Parameterization class.
KMeansSort<V extends NumberVector> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans: Sort-Means: Accelerated k-means by exploiting the triangle inequality and pairwise distances of means to prune candidate means (with sorting).
KMeansSort(NumberVectorDistanceFunction<? super V>, int, int, KMeansInitialization) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansSort: Constructor.
KMeansSort.Instance - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans: Inner instance, storing state for a single data set.
KMeansSort.Parameterizer<V extends NumberVector> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans: Parameterization class.
kMeansVariant - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.BestOfMultipleKMeans.Parameterizer: Variant of kMeans to use.
kMeansVariant - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansBisecting.Parameterizer: Variant of kMeans
KMediansLloyd<V extends NumberVector> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans: k-medians clustering algorithm, but using Lloyd-style bulk iterations instead of the more complicated approach suggested by Kaufman and Rousseeuw (see KMedoidsPAM instead).
KMediansLloyd(NumberVectorDistanceFunction<? super V>, int, int, KMeansInitialization) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMediansLloyd: Constructor.
KMediansLloyd.Instance - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans: Inner instance, storing state for a single data set.
KMediansLloyd.Parameterizer<V extends NumberVector> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans: Parameterization class.
KMedoidsFastPAM<V> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans: FastPAM: An improved version of PAM, that is usually O(k) times faster.
KMedoidsFastPAM(DistanceFunction<? super V>, int, int, KMedoidsInitialization<V>) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMedoidsFastPAM: Constructor.
KMedoidsFastPAM(DistanceFunction<? super V>, int, int, KMedoidsInitialization<V>, double) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMedoidsFastPAM: Constructor.
KMedoidsFastPAM.Instance - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans: Instance for a single dataset.
KMedoidsFastPAM.Parameterizer<V> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans: Parameterization class.
KMedoidsFastPAM1<V> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans: FastPAM1: A version of PAM that is O(k) times faster, i.e., now in O((n-k)²).
KMedoidsFastPAM1(DistanceFunction<? super V>, int, int, KMedoidsInitialization<V>) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMedoidsFastPAM1: Constructor.
KMedoidsFastPAM1.Instance - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans: Instance for a single dataset.
KMedoidsFastPAM1.Parameterizer<V> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans: Parameterization class.
KMedoidsInitialization<V> - Interface in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization: Interface for initializing K-Medoids.
KMedoidsPAM<V> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans: The original Partitioning Around Medoids (PAM) algorithm or k-medoids clustering, as proposed by Kaufman and Rousseeuw in "Clustering by means of Medoids".
KMedoidsPAM(DistanceFunction<? super V>, int, int, KMedoidsInitialization<V>) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMedoidsPAM: Constructor.
KMedoidsPAM.Instance - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans: Instance for a single dataset.
KMedoidsPAM.Parameterizer<V> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans: Parameterization class.
KMedoidsPAMReynolds<V> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans: The Partitioning Around Medoids (PAM) algorithm with some additional optimizations proposed by Reynolds et al.
KMedoidsPAMReynolds(DistanceFunction<? super V>, int, int, KMedoidsInitialization<V>) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMedoidsPAMReynolds: Constructor.
KMedoidsPAMReynolds.Instance - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans: Instance for a single dataset.
KMedoidsPAMReynolds.Parameterizer<V> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans: Parameterization class.
KMedoidsPark<V> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans: A k-medoids clustering algorithm, implemented as EM-style bulk algorithm.
KMedoidsPark(DistanceFunction<? super V>, int, int, KMedoidsInitialization<V>) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMedoidsPark: Constructor.
KMedoidsPark.Parameterizer<V> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans: Parameterization class.
kmin - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.KDEOS: Minimum and maximum number of neighbors to use.
kmin - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.KDEOS.Parameterizer: Minimum and maximum number of neighbors to use.
KMIN_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.KDEOS.Parameterizer: Minimum value of k to analyze.
KMLOutputHandler - Class in de.lmu.ifi.dbs.elki.result: Class to handle KML output.
KMLOutputHandler(File, OutlierScaling, boolean, boolean) - Constructor for class de.lmu.ifi.dbs.elki.result.KMLOutputHandler: Constructor.
KMLOutputHandler.Parameterizer - Class in de.lmu.ifi.dbs.elki.result: Parameterization class
kmulti - Variable in class de.lmu.ifi.dbs.elki.index.projected.ProjectedIndex.Factory: Multiplier for k.
kmulti - Variable in class de.lmu.ifi.dbs.elki.index.projected.ProjectedIndex.Factory.Parameterizer: Multiplier for k.
kmulti - Variable in class de.lmu.ifi.dbs.elki.index.projected.ProjectedIndex: Multiplier for k.
knn - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.subspace.SOD: Neighborhood size.
knn - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.subspace.SOD.Parameterizer: Neighborhood size
KNN_CACHE_MAGIC - Static variable in class de.lmu.ifi.dbs.elki.application.cache.CacheDoubleDistanceKNNLists: Magic number to identify files.
KNN_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.KNNSOS.Parameterizer: Parameter to specify the number of neighbors
KNN_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.intrinsic.ISOS.Parameterizer: Parameter to specify the number of neighbors
KNN_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.subspace.SOD.Parameterizer: 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(Database, Relation<V>, DBIDs, WritableDoubleDataStore, DoubleMinMax) - Method in class de.lmu.ifi.dbs.elki.algorithm.outlier.anglebased.FastABOD: Simpler kNN based, can use more indexing.
KNNBenchmarkAlgorithm<O> - Class in de.lmu.ifi.dbs.elki.algorithm.benchmark: Benchmarking algorithm that computes the k nearest neighbors for each query point.
KNNBenchmarkAlgorithm(DistanceFunction<? super O>, int, DatabaseConnection, double, RandomFactory) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.benchmark.KNNBenchmarkAlgorithm: Constructor.
KNNBenchmarkAlgorithm.Parameterizer<O> - Class in de.lmu.ifi.dbs.elki.algorithm.benchmark: Parameterization class
KNNChangeEvent - Class in de.lmu.ifi.dbs.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 de.lmu.ifi.dbs.elki.index.preprocessed.knn.KNNChangeEvent: Used to create an event when kNNs of some objects have been changed.
KNNChangeEvent.Type - Enum in de.lmu.ifi.dbs.elki.index.preprocessed.knn: Available event types.
KNNClassifier<O> - Class in de.lmu.ifi.dbs.elki.algorithm.classification: KNNClassifier classifies instances based on the class distribution among the k nearest neighbors in a database.
KNNClassifier(DistanceFunction<? super O>, int) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.classification.KNNClassifier: Constructor.
KNNClassifier.Parameterizer<O> - Class in de.lmu.ifi.dbs.elki.algorithm.classification: Parameterization class
KNNDD<O> - Class in de.lmu.ifi.dbs.elki.algorithm.outlier.distance: Nearest Neighbor Data Description.
KNNDD(DistanceFunction<? super O>, int) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.KNNDD: Constructor for a single kNN query.
KNNDD.Parameterizer<O> - Class in de.lmu.ifi.dbs.elki.algorithm.outlier.distance: Parameterization class.
KNNDIST - Static variable in class de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.selection.DistanceFunctionVisualization.Instance
knnDistance - Variable in class de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkmax.MkMaxDirectoryEntry: The aggregated k-nearest neighbor distance of the underlying MkMax-Tree node.
knnDistance - Variable in class de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkmax.MkMaxLeafEntry: The k-nearest neighbor distance of the underlying data object.
kNNDistance() - Method in class de.lmu.ifi.dbs.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 - Variable in class de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rdknn.RdKNNDirectoryEntry: The aggregated knn distance of this entry.
knnDistance - Variable in class de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rdknn.RdKNNLeafEntry: The knn distance of the underlying data object.
kNNDistance() - Method in class de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rdknn.RdKNNNode: Computes and returns the aggregated knn distance of this node
kNNdistanceAdjustment(E, Map<DBID, KNNList>) - Method in class de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.AbstractMkTreeUnified: Performs a distance adjustment in the subtree of the specified root entry.
kNNdistanceAdjustment(MkMaxEntry, Map<DBID, KNNList>) - Method in class de.lmu.ifi.dbs.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 de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mktab.MkTabTree
knnDistanceApproximation() - Method in class de.lmu.ifi.dbs.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 de.lmu.ifi.dbs.elki.algorithm.KNNDistancesSampler.KNNDistanceOrderResult: Construct result
knnDistances - Variable in class de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mktab.MkTabDirectoryEntry: The aggregated knn distances of the underlying node.
knnDistances - Variable in class de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mktab.MkTabLeafEntry: The knn distances of the underlying data object.
knnDistances(O) - Method in class de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mktab.MkTabTreeIndex: Returns the knn distance of the object with the specified id.
kNNDistances() - Method in class de.lmu.ifi.dbs.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 de.lmu.ifi.dbs.elki.algorithm: Provides an order of the kNN-distances for all objects within the database.
KNNDistancesSampler(DistanceFunction<? super O>, int, double, RandomFactory) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.KNNDistancesSampler: Constructor.
KNNDistancesSampler.KNNDistanceOrderResult - Class in de.lmu.ifi.dbs.elki.algorithm: Curve result for a list containing the knn distances.
KNNDistancesSampler.Parameterizer<O> - Class in de.lmu.ifi.dbs.elki.algorithm: Parameterization class.
KNNEvaluator() - Constructor for class de.lmu.ifi.dbs.elki.algorithm.statistics.EvaluateRetrievalPerformance.KNNEvaluator
KNNHeap - Interface in de.lmu.ifi.dbs.elki.database.ids: Interface for kNN heaps.
KNNIndex<O> - Interface in de.lmu.ifi.dbs.elki.index: Index with support for kNN queries.
KNNJoin<V extends NumberVector,N extends SpatialNode<N,E>,E extends SpatialEntry> - Class in de.lmu.ifi.dbs.elki.algorithm: Joins in a given spatial database to each object its k-nearest neighbors.
KNNJoin(DistanceFunction<? super V>, int) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.KNNJoin: Constructor.
KNNJoin.Parameterizer<V extends NumberVector,N extends SpatialNode<N,E>,E extends SpatialEntry> - Class in de.lmu.ifi.dbs.elki.algorithm: Parameterization class.
KNNJoin.Task - Class in de.lmu.ifi.dbs.elki.algorithm: Task in the processing queue.
KNNJoinMaterializeKNNPreprocessor<V extends NumberVector> - Class in de.lmu.ifi.dbs.elki.index.preprocessed.knn: Class to materialize the kNN using a spatial join on an R-tree.
KNNJoinMaterializeKNNPreprocessor(Relation<V>, DistanceFunction<? super V>, int) - Constructor for class de.lmu.ifi.dbs.elki.index.preprocessed.knn.KNNJoinMaterializeKNNPreprocessor: Constructor.
KNNJoinMaterializeKNNPreprocessor.Factory<O extends NumberVector> - Class in de.lmu.ifi.dbs.elki.index.preprocessed.knn: The parameterizable factory.
KNNJoinMaterializeKNNPreprocessor.Factory.Parameterizer<O extends NumberVector> - Class in de.lmu.ifi.dbs.elki.index.preprocessed.knn: Parameterization class
KNNKernelDensityMinimaClustering<V extends NumberVector> - Class in de.lmu.ifi.dbs.elki.algorithm.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 de.lmu.ifi.dbs.elki.algorithm.clustering.onedimensional.KNNKernelDensityMinimaClustering: Constructor.
KNNKernelDensityMinimaClustering.Mode - Enum in de.lmu.ifi.dbs.elki.algorithm.clustering.onedimensional: Estimation mode.
KNNKernelDensityMinimaClustering.Parameterizer<V extends NumberVector> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.onedimensional: Parameterization class.
KNNLIST - Static variable in class de.lmu.ifi.dbs.elki.data.type.TypeUtil: KNN lists.
KNNList - Interface in de.lmu.ifi.dbs.elki.database.ids: Interface for kNN results.
KNNListener - Interface in de.lmu.ifi.dbs.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 de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.selection.DistanceFunctionVisualization.Instance: Generic tags to indicate the type of element.
KNNOutlier<O> - Class in de.lmu.ifi.dbs.elki.algorithm.outlier.distance: Outlier Detection based on the distance of an object to its k nearest neighbor.
KNNOutlier(DistanceFunction<? super O>, int) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.KNNOutlier: Constructor for a single kNN query.
KNNOutlier.Parameterizer<O> - Class in de.lmu.ifi.dbs.elki.algorithm.outlier.distance: Parameterization class.
knnperf - Variable in class de.lmu.ifi.dbs.elki.algorithm.statistics.EvaluateRetrievalPerformance.RetrievalPerformanceResult: KNN performance
KNNProcessor<O> - Class in de.lmu.ifi.dbs.elki.parallel.processor: Processor to compute the kNN of each object.
KNNProcessor(int, KNNQuery<O>) - Constructor for class de.lmu.ifi.dbs.elki.parallel.processor.KNNProcessor: Constructor.
KNNProcessor.Instance<O> - Class in de.lmu.ifi.dbs.elki.parallel.processor: Instance for precomputing the kNN.
knnq - Variable in class de.lmu.ifi.dbs.elki.algorithm.classification.KNNClassifier: kNN query class.
knnq - Variable in class de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.AbstractMkTree: Internal class for performing knn queries
knnq - Variable in class de.lmu.ifi.dbs.elki.parallel.processor.KNNProcessor.Instance: kNN query
knnq - Variable in class de.lmu.ifi.dbs.elki.parallel.processor.KNNProcessor: KNN query object
knnQueries - Variable in class de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.AbstractMTree.Statistics: For counting the number of knn queries answered.
knnQueries - Variable in class de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.AbstractRStarTree.Statistics: For counting the number of knn queries answered.
KNNQuery<O> - Interface in de.lmu.ifi.dbs.elki.database.query.knn: The interface of an actual instance.
knnQuery - Variable in class de.lmu.ifi.dbs.elki.database.query.rknn.LinearScanRKNNQuery: KNN query we use.
knnQuery - Variable in class de.lmu.ifi.dbs.elki.index.preprocessed.knn.MaterializeKNNPreprocessor: KNNQuery instance to use.
knnQuery - Variable in class de.lmu.ifi.dbs.elki.index.preprocessed.localpca.KNNQueryFilteredPCAIndex: The kNN query instance we use.
knnQuery - Variable in class de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rdknn.RdKNNTree: Internal knn query object, for updating the rKNN.
KNNQueryFilteredPCAIndex<NV extends NumberVector> - Class in de.lmu.ifi.dbs.elki.index.preprocessed.localpca: Provides the local neighborhood to be considered in the PCA as the k nearest neighbors of an object.
KNNQueryFilteredPCAIndex(Relation<NV>, PCARunner, EigenPairFilter, KNNQuery<NV>, int) - Constructor for class de.lmu.ifi.dbs.elki.index.preprocessed.localpca.KNNQueryFilteredPCAIndex: Constructor.
KNNQueryFilteredPCAIndex.Factory<V extends NumberVector> - Class in de.lmu.ifi.dbs.elki.index.preprocessed.localpca: Factory class.
KNNQueryFilteredPCAIndex.Factory.Parameterizer<NV extends NumberVector> - Class in de.lmu.ifi.dbs.elki.index.preprocessed.localpca: Parameterization class.
kNNReach - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.FlexibleLOF.LOFResult: The kNN query w.r.t. the reachability distance.
kNNRefer - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.FlexibleLOF.LOFResult: The kNN query w.r.t. the reference neighborhood distance.
knns - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.parallel.LOFProcessor: KNN store
knns - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.parallel.LRDProcessor: KNN store
knns - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.parallel.SimplifiedLRDProcessor: KNN store
kNNsChanged(KNNChangeEvent) - Method in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.OnlineLOF.LOFKNNListener
kNNsChanged(KNNChangeEvent, KNNChangeEvent) - Method in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.OnlineLOF.LOFKNNListener: Invoked after the events of both preprocessors have been received, i.e.
kNNsChanged(KNNChangeEvent) - Method in interface de.lmu.ifi.dbs.elki.index.preprocessed.knn.KNNListener: Invoked after kNNs have been updated, inserted or removed in some way.
kNNsInserted(DBIDs, DBIDs, DBIDs, FlexibleLOF.LOFResult<O>) - Method in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.OnlineLOF.LOFKNNListener: Invoked after kNNs have been inserted and updated, updates the result.
KNNSOS<O> - Class in de.lmu.ifi.dbs.elki.algorithm.outlier.distance: kNN-based adaption of Stochastic Outlier Selection.
KNNSOS(DistanceFunction<? super O>, int) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.KNNSOS: Constructor.
KNNSOS.Parameterizer<O> - Class in de.lmu.ifi.dbs.elki.algorithm.outlier.distance: Parameterization class.
kNNsRemoved(DBIDs, DBIDs, DBIDs, FlexibleLOF.LOFResult<O>) - Method in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.OnlineLOF.LOFKNNListener: Invoked after kNNs have been removed and updated, updates the result.
KNNWeightOutlier<O> - Class in de.lmu.ifi.dbs.elki.algorithm.outlier.distance: Outlier Detection based on the accumulated distances of a point to its k nearest neighbors.
KNNWeightOutlier(DistanceFunction<? super O>, int) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.KNNWeightOutlier: Constructor with parameters.
KNNWeightOutlier.Parameterizer<O> - Class in de.lmu.ifi.dbs.elki.algorithm.outlier.distance: Parameterization class.
KNNWeightProcessor - Class in de.lmu.ifi.dbs.elki.algorithm.outlier.distance.parallel: Compute the kNN weight score, used by ParallelKNNWeightOutlier.
KNNWeightProcessor(int) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.parallel.KNNWeightProcessor: Constructor.
KNNWeightProcessor.Instance - Class in de.lmu.ifi.dbs.elki.algorithm.outlier.distance.parallel: Instance for precomputing the kNN.
KNOWN_REVERSED - Static variable in class de.lmu.ifi.dbs.elki.application.greedyensemble.EvaluatePrecomputedOutlierScores: Pattern to match a set of known reversed scores.
knownParameterizables - Variable in class de.lmu.ifi.dbs.elki.application.internal.CheckParameterizables: Known parameterizable classes/interfaces.
KolmogorovSmirnovDistanceFunction - Class in de.lmu.ifi.dbs.elki.distance.distancefunction.histogram: Distance function based on the Kolmogorov-Smirnov goodness of fit test.
KolmogorovSmirnovDistanceFunction() - Constructor for class de.lmu.ifi.dbs.elki.distance.distancefunction.histogram.KolmogorovSmirnovDistanceFunction: Deprecated.
Use static instance!
KolmogorovSmirnovDistanceFunction.Parameterizer - Class in de.lmu.ifi.dbs.elki.distance.distancefunction.histogram: Parameterization class, using the static instance.
KolmogorovSmirnovTest - Class in de.lmu.ifi.dbs.elki.math.statistics.tests: Kolmogorov-Smirnov test.
KolmogorovSmirnovTest() - Constructor for class de.lmu.ifi.dbs.elki.math.statistics.tests.KolmogorovSmirnovTest: Constructor.
KolmogorovSmirnovTest.Parameterizer - Class in de.lmu.ifi.dbs.elki.math.statistics.tests: Parameterizer, to use the static instance.
kplus1 - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.INFLO: Number of neighbors to use.
KR_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.intrinsic.IDOS.Parameterizer: Parameter to specify the neighborhood size to use for the averaging.
krange - Variable in class de.lmu.ifi.dbs.elki.application.greedyensemble.ComputeKNNOutlierScores: Range of k.
krange - Variable in class de.lmu.ifi.dbs.elki.application.greedyensemble.ComputeKNNOutlierScores.Parameterizer: k step size
KRANGE_ID - Static variable in class de.lmu.ifi.dbs.elki.application.greedyensemble.ComputeKNNOutlierScores.Parameterizer: Option ID for k parameter range
krate - Variable in class de.lmu.ifi.dbs.elki.algorithm.statistics.EstimateIntrinsicDimensionality: Number of neighbors to use.
krate - Variable in class de.lmu.ifi.dbs.elki.algorithm.statistics.EstimateIntrinsicDimensionality.Parameterizer: Number of neighbors to use.
KRATE_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.statistics.EstimateIntrinsicDimensionality.Parameterizer: Number of kNN to use for each object.
kreach - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.FlexibleLOF: Number of neighbors used for reachability distance.
kreach - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.FlexibleLOF.Parameterizer: The set size to use for reachability distance.
kreach - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LoOP: Reachability neighborhood size.
kreach - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LoOP.Parameterizer: Holds the value of LoOP.Parameterizer.KREACH_ID.
KREACH_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.FlexibleLOF.Parameterizer: Parameter to specify the number of nearest neighbors of an object to be considered for computing its reachability distance.
KREACH_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LoOP.Parameterizer: Parameter to specify the number of nearest neighbors of an object to be considered for computing its LOOP_SCORE, must be an integer greater than 1.
KREF_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.FlexibleLOF.Parameterizer: 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 de.lmu.ifi.dbs.elki.algorithm.outlier.lof.FlexibleLOF: Number of neighbors in comparison set.
krefer - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.FlexibleLOF.Parameterizer: The reference set size to use.
KSQUARE_ID - Static variable in class de.lmu.ifi.dbs.elki.application.greedyensemble.ComputeKNNOutlierScores.Parameterizer: Option ID with an additional bound on k.
ksquarestop - Variable in class de.lmu.ifi.dbs.elki.application.greedyensemble.ComputeKNNOutlierScores: Maximum k for O(k^2) methods.
ksquarestop - Variable in class de.lmu.ifi.dbs.elki.application.greedyensemble.ComputeKNNOutlierScores.Parameterizer: Maximum k for O(k^2) methods.
Kulczynski1SimilarityFunction - Class in de.lmu.ifi.dbs.elki.distance.similarityfunction: Kulczynski similarity 1.
Kulczynski1SimilarityFunction() - Constructor for class de.lmu.ifi.dbs.elki.distance.similarityfunction.Kulczynski1SimilarityFunction: Deprecated.
Use Kulczynski1SimilarityFunction.STATIC instance instead.
Kulczynski1SimilarityFunction.Parameterizer - Class in de.lmu.ifi.dbs.elki.distance.similarityfunction: Parameterization class.
Kulczynski2SimilarityFunction - Class in de.lmu.ifi.dbs.elki.distance.similarityfunction: Kulczynski similarity 2.
Kulczynski2SimilarityFunction() - Constructor for class de.lmu.ifi.dbs.elki.distance.similarityfunction.Kulczynski2SimilarityFunction: Deprecated.
Use Kulczynski2SimilarityFunction.STATIC_CONTINUOUS instance instead.
Kulczynski2SimilarityFunction.Parameterizer - Class in de.lmu.ifi.dbs.elki.distance.similarityfunction: Parameterization class.
KullbackLeiblerDivergenceAsymmetricDistanceFunction - Class in de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic: Kullback-Leibler divergence, also known as relative entropy, information deviation, or just KL-distance (albeit asymmetric).
KullbackLeiblerDivergenceAsymmetricDistanceFunction() - Constructor for class de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic.KullbackLeiblerDivergenceAsymmetricDistanceFunction: Deprecated.
Use static instance!
KullbackLeiblerDivergenceAsymmetricDistanceFunction.Parameterizer - Class in de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic: Parameterization class, using the static instance.
KullbackLeiblerDivergenceReverseAsymmetricDistanceFunction - Class in de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic: Kullback-Leibler divergence, also known as relative entropy, information deviation or just KL-distance (albeit asymmetric).
KullbackLeiblerDivergenceReverseAsymmetricDistanceFunction() - Constructor for class de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic.KullbackLeiblerDivergenceReverseAsymmetricDistanceFunction: Deprecated.
Use static instance!
KullbackLeiblerDivergenceReverseAsymmetricDistanceFunction.Parameterizer - Class in de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic: Parameterization class, using the static instance.

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