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.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.EM

Number of clusters

k - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.EM.Parameterizer

Number of clusters.

k - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.AbstractKMeans

Holds the value of KMeans.K_ID.

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.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.KMedoidsEM

Holds the value of KMeans.K_ID.

k - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMedoidsEM.Parameterizer

k - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMedoidsPAM

Holds the value of KMeans.K_ID.

k - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMedoidsPAM.Parameterizer

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.KNNDistanceOrder

Holds the value of KNNDistanceOrder.K_ID.

k - Variable in class de.lmu.ifi.dbs.elki.algorithm.KNNDistanceOrder.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.AbstractAggarwalYuOutlier

The target dimensionality.

k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.AbstractAggarwalYuOutlier.Parameterizer

k Parameter.

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.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.FastABOD

Number of nearest neighbors.

k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.FastABOD.Parameterizer

Number of neighbors.

k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.HilOut

Number of nearest neighbors

k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.HilOut.Parameterizer

Neighborhood size

k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.KNNOutlier

The parameter k

k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.KNNOutlier.Parameterizer

k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.KNNWeightOutlier

Holds the value of KNNWeightOutlier.K_ID.

k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.KNNWeightOutlier.Parameterizer

k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.INFLO

Holds the value of INFLO.K_ID.

k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.INFLO.Parameterizer

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

Holds the value of LDOF.K_ID.

k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LDOF.Parameterizer

k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LOF

Holds the value of LOF.Parameterizer.K_ID.

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.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

Parameter k.

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.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.ODIN

Number of neighbors for kNN graph.

k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.ODIN.Parameterizer

Number of nearest neighbors to use.

k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.ReferenceBasedOutlierDetection

Holds the value of ReferenceBasedOutlierDetection.K_ID.

k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.ReferenceBasedOutlierDetection.Parameterizer

Holds the value of ReferenceBasedOutlierDetection.K_ID.

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.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.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.database.ids.generic.DistanceDBIDPairKNNList

The value of k this was materialized for.

k - Variable in class de.lmu.ifi.dbs.elki.database.ids.generic.DoubleDistanceDBIDPairKNNList

The value of k this was materialized for.

k - Variable in class de.lmu.ifi.dbs.elki.database.ids.generic.DoubleDistanceDBIDPairKNNListHeap

The value of k this was materialized for.

k - Variable in class de.lmu.ifi.dbs.elki.database.ids.generic.DoubleDistanceKNNSubList

Parameter k.

k - Variable in class de.lmu.ifi.dbs.elki.database.ids.generic.KNNSubList

Parameter k.

k - Variable in class de.lmu.ifi.dbs.elki.database.ids.integer.DoubleDistanceIntegerDBIDKNNHeap

k for this heap.

k - Variable in class de.lmu.ifi.dbs.elki.database.ids.integer.DoubleDistanceIntegerDBIDKNNList

The k value this list was generated for.

k - Variable in class de.lmu.ifi.dbs.elki.database.ids.integer.DoubleDistanceIntegerDBIDPairKNNListHeap

The value of k this was materialized for.

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.distance.distancefunction.MinKDistance.Instance

Value for k

k - Variable in class de.lmu.ifi.dbs.elki.distance.distancefunction.MinKDistance

The value of k

k - Variable in class de.lmu.ifi.dbs.elki.distance.distancefunction.MinKDistance.Parameterizer

The value of k

k - Variable in class de.lmu.ifi.dbs.elki.index.lsh.hashfamilies.AbstractHashFunctionFamily

The number of projections to use for each hash function.

k - Variable in class de.lmu.ifi.dbs.elki.index.lsh.hashfamilies.AbstractHashFunctionFamily.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

Holds the value of KNNQueryFilteredPCAIndex.Factory.K_ID.

k - Variable in class de.lmu.ifi.dbs.elki.index.preprocessed.localpca.KNNQueryFilteredPCAIndex.Factory.Parameterizer

k - Variable in class de.lmu.ifi.dbs.elki.index.preprocessed.localpca.KNNQueryFilteredPCAIndex

Query k.

k - Variable in class de.lmu.ifi.dbs.elki.index.preprocessed.preference.HiSCPreferenceVectorIndex.Factory

Holds the value of parameter HiSCPreferenceVectorIndex.Factory.K_ID.

k - Variable in class de.lmu.ifi.dbs.elki.index.preprocessed.preference.HiSCPreferenceVectorIndex.Factory.Parameterizer

Holds the value of parameter HiSCPreferenceVectorIndex.Factory.K_ID.

k - Variable in class de.lmu.ifi.dbs.elki.index.preprocessed.preference.HiSCPreferenceVectorIndex

Holds the value of parameter 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.LogGammaAlternateDistribution

Alpha == k.

k - Variable in class de.lmu.ifi.dbs.elki.math.statistics.distribution.LogGammaAlternateDistribution.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.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

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_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.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.HiCO

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.Parameterizer

Parameter to specify the number of clusters to find, must be an integer greater than 0.

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.KNNDistanceOrder

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

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.AbstractAggarwalYuOutlier.Parameterizer

OptionID for the target dimensionality.

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 COP_SCORE, must be an integer greater than 0.

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.FastABOD.Parameterizer

Parameter for the nearest neighbors.

K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.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.KNNOutlier

Parameter to specify the k nearest neighbor

K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.KNNWeightOutlier

Parameter to specify the k nearest neighbor

K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.INFLO

Parameter to specify the number of nearest neighbors of an object to be considered for computing its INFLO_SCORE. must be an integer greater than 1.

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

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 1.

K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.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.ReferenceBasedOutlierDetection

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.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.statistics.AveragePrecisionAtK.Parameterizer

Parameter k to compute the average precision at.

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.distance.distancefunction.MinKDistance

OptionID for the "k" parameter.

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

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.preprocessed.preference.HiSCPreferenceVectorIndex.Factory

The number of nearest neighbors considered to determine the preference vector.

K_ID - Static variable in class de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkapp.MkAppTreeFactory

Parameter for k

K_ID - Static variable in class de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkcop.MkCopTreeFactory

Parameter for k

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

Parameter to specify the number of outliers to keep Key: -topk.k

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.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.metrical.mtreevariants.mktrees.MkTreeSettings

Holds the maximum value of k to support.

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_MULTIPLIER_ID - Static variable in class de.lmu.ifi.dbs.elki.index.projected.ProjectedIndex.Factory.Parameterizer

Option ID for querying a larger k.

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.index.preprocessed.subspaceproj.PreDeConSubspaceIndex

The kappa value for generating the variance vector.

KAPPA - Static variable in class de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.cluster.EMClusterVisualization.Instance

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

KappaDistribution.Parameterizer() - Constructor for class de.lmu.ifi.dbs.elki.math.statistics.distribution.KappaDistribution.Parameterizer

kcomp - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LoOP

Holds the value of LoOP.KCOMP_ID.

kcomp - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LoOP.Parameterizer

Holds the value of LoOP.KCOMP_ID.

KCOMP_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LoOP

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

Provides a KDDCLIApplication that can be used to perform any algorithm implementing Algorithm using any DatabaseConnection implementing DatabaseConnection.

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.

KDDCLIApplication.Parameterizer() - Constructor for class de.lmu.ifi.dbs.elki.application.KDDCLIApplication.Parameterizer

KDDTask - Class in de.lmu.ifi.dbs.elki

Provides a KDDTask that can be used to perform any algorithm implementing Algorithm using any DatabaseConnection implementing DatabaseConnection.

KDDTask(InputStep, AlgorithmStep, EvaluationStep, OutputStep, Collection<Pair<Object, Parameter<?>>>) - Constructor for class de.lmu.ifi.dbs.elki.KDDTask

Constructor.

KDDTask.Parameterizer - Class in de.lmu.ifi.dbs.elki

Parameterization class.

KDDTask.Parameterizer() - Constructor for class de.lmu.ifi.dbs.elki.KDDTask.Parameterizer

kdist - Variable in class de.lmu.ifi.dbs.elki.database.ids.integer.DoubleDistanceIntegerDBIDKNNHeap

Current maximum value.

kdKNNSearch(int, int, int, O, DoubleDistanceKNNHeap, DBIDArrayIter, double) - Method in class de.lmu.ifi.dbs.elki.index.tree.spatial.kd.MinimalisticMemoryKDTree.KDTreeKNNQuery

Perform a kNN search on the kd-tree.

kdRangeSearch(int, int, int, O, ModifiableDoubleDistanceDBIDList, DBIDArrayIter, double) - Method in class de.lmu.ifi.dbs.elki.index.tree.spatial.kd.MinimalisticMemoryKDTree.KDTreeRangeQuery

Perform a kNN search on the kd-tree.

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.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.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.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.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.

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.ABOD

Store the configured Kernel version.

kernelFunction - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.ABOD.Parameterizer

Distance function.

KernelMatrix - Class in de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel

Provides a class for storing the kernel matrix and several extraction methods for convenience.

KernelMatrix(PrimitiveSimilarityFunction<? super O, D>, 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, D>, Relation<? extends O>, DBIDs) - Constructor for class de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel.KernelMatrix

Provides a new kernel matrix.

KernelMatrix(Matrix) - 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.RangeMap(DBIDRange) - Constructor for class de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel.KernelMatrix.RangeMap

KernelMatrix.SortedArrayMap - Class in de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel

Map a DBID to an integer offset, Version to support arbitrary DBIDs.

KernelMatrix.SortedArrayMap(DBIDs) - Constructor for class de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel.KernelMatrix.SortedArrayMap

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.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.KeyVisualization.Instance

CSS class for hierarchy plot lines

keymap - Variable in class de.lmu.ifi.dbs.elki.datasource.parser.TermFrequencyParser

Map.

keySet() - Method in class de.lmu.ifi.dbs.elki.visualization.gui.overview.RectangleArranger

The item keys contained in the map.

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

KeyVisualization.Instance(VisualizationTask) - Constructor for class de.lmu.ifi.dbs.elki.visualization.visualizers.visunproj.KeyVisualization.Instance

Constructor.

KMeans<V extends NumberVector<?>,D extends Distance<?>,M extends MeanModel<V>> - Interface in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans

Some constants and options shared among kmeans family algorithms.

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.KMeansBisecting.Parameterizer

Parameter to specify the kMeans variant.

KMEANS_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.SampleKMeansInitialization.Parameterizer

Parameter to specify the kMeans variant.

KMeansBatchedLloyd<V extends NumberVector<?>,D extends Distance<D>> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans

Provides the k-means algorithm, using Lloyd-style bulk iterations.

KMeansBatchedLloyd(PrimitiveDistanceFunction<NumberVector<?>, D>, int, int, KMeansInitialization<V>, int, RandomFactory) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansBatchedLloyd

Constructor.

KMeansBatchedLloyd.Parameterizer<V extends NumberVector<?>,D extends Distance<D>> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans

Parameterization class.

KMeansBatchedLloyd.Parameterizer() - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansBatchedLloyd.Parameterizer

KMeansBisecting<V extends NumberVector<?>,D extends Distance<?>,M extends MeanModel<V>> - 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, D, M>) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansBisecting

Constructor.

KMeansBisecting.Parameterizer<V extends NumberVector<?>,D extends Distance<?>,M extends MeanModel<V>> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans

Parameterization class.

KMeansBisecting.Parameterizer() - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansBisecting.Parameterizer

KMEANSBORDER - Static variable in class de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.cluster.VoronoiVisualization

Generic tags to indicate the type of element.

KMeansHybridLloydMacQueen<V extends NumberVector<?>,D extends Distance<D>> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans

Provides the k-means algorithm, alternating between MacQueen-style incremental processing and Lloyd-Style batch steps.

KMeansHybridLloydMacQueen(PrimitiveDistanceFunction<NumberVector<?>, D>, int, int, KMeansInitialization<V>) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansHybridLloydMacQueen

Constructor.

KMeansHybridLloydMacQueen.Parameterizer<V extends NumberVector<?>,D extends Distance<D>> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans

Parameterization class.

KMeansHybridLloydMacQueen.Parameterizer() - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansHybridLloydMacQueen.Parameterizer

KMeansInitialization<V> - Interface in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans

Interface for initializing K-Means

KMeansLloyd<V extends NumberVector<?>,D extends Distance<D>> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans

Provides the k-means algorithm, using Lloyd-style bulk iterations.

KMeansLloyd(PrimitiveDistanceFunction<NumberVector<?>, D>, int, int, KMeansInitialization<V>) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansLloyd

Constructor.

KMeansLloyd.Parameterizer<V extends NumberVector<?>,D extends Distance<D>> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans

Parameterization class.

KMeansLloyd.Parameterizer() - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansLloyd.Parameterizer

KMeansMacQueen<V extends NumberVector<?>,D extends Distance<D>> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans

Provides the k-means algorithm, using MacQueen style incremental updates.

KMeansMacQueen(PrimitiveDistanceFunction<NumberVector<?>, D>, int, int, KMeansInitialization<V>) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansMacQueen

Constructor.

KMeansMacQueen.Parameterizer<V extends NumberVector<?>,D extends Distance<D>> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans

Parameterization class.

KMeansMacQueen.Parameterizer() - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansMacQueen.Parameterizer

KMeansModel<V extends NumberVector<?>> - 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(V) - Constructor for class de.lmu.ifi.dbs.elki.data.model.KMeansModel

Constructor with mean.

KMeansPlusPlusInitialMeans<V,D extends NumberDistance<D,?>> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans

K-Means++ initialization for k-means.

KMeansPlusPlusInitialMeans(RandomFactory) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansPlusPlusInitialMeans

Constructor.

KMeansPlusPlusInitialMeans.Parameterizer<V,D extends NumberDistance<D,?>> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans

Parameterization class.

KMeansPlusPlusInitialMeans.Parameterizer() - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansPlusPlusInitialMeans.Parameterizer

KMeansQualityMeasure<O extends NumberVector<?>,D extends Distance<?>> - Interface in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.quality

Interface for computing the quality of a K-Means clustering.

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<?>,D extends Distance<D>> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans

Provides the k-medians clustering algorithm, using Lloyd-style bulk iterations.

KMediansLloyd(PrimitiveDistanceFunction<NumberVector<?>, D>, int, int, KMeansInitialization<V>) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMediansLloyd

Constructor.

KMediansLloyd.Parameterizer<V extends NumberVector<?>,D extends Distance<D>> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans

Parameterization class.

KMediansLloyd.Parameterizer() - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMediansLloyd.Parameterizer

KMedoidsEM<V,D extends NumberDistance<D,?>> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans

Provides the k-medoids clustering algorithm, using a "bulk" variation of the "Partitioning Around Medoids" approach.

KMedoidsEM(PrimitiveDistanceFunction<? super V, D>, int, int, KMedoidsInitialization<V>) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMedoidsEM

Constructor.

KMedoidsEM.Parameterizer<V,D extends NumberDistance<D,?>> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans

Parameterization class.

KMedoidsEM.Parameterizer() - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMedoidsEM.Parameterizer

KMedoidsInitialization<V> - Interface in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans

Interface for initializing K-Medoids.

KMedoidsPAM<V,D extends NumberDistance<D,?>> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans

Provides the k-medoids clustering algorithm, using the "Partitioning Around Medoids" approach.

KMedoidsPAM(PrimitiveDistanceFunction<? super V, D>, int, int, KMedoidsInitialization<V>) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMedoidsPAM

Constructor.

KMedoidsPAM.Parameterizer<V,D extends NumberDistance<D,?>> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans

Parameterization class.

KMedoidsPAM.Parameterizer() - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMedoidsPAM.Parameterizer

KMLOutputHandler - Class in de.lmu.ifi.dbs.elki.result

Class to handle KML output.

KMLOutputHandler(File, OutlierScalingFunction, 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

KMLOutputHandler.Parameterizer() - Constructor for class de.lmu.ifi.dbs.elki.result.KMLOutputHandler.Parameterizer

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.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.

KNNBenchmarkAlgorithm<O,D extends Distance<D>> - 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, D>, int, DatabaseConnection, double, RandomFactory) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.benchmark.KNNBenchmarkAlgorithm

Constructor.

KNNBenchmarkAlgorithm.Parameterizer<O,D extends Distance<D>> - Class in de.lmu.ifi.dbs.elki.algorithm.benchmark

Parameterization class

KNNBenchmarkAlgorithm.Parameterizer() - Constructor for class de.lmu.ifi.dbs.elki.algorithm.benchmark.KNNBenchmarkAlgorithm.Parameterizer

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.

KNNChangeEvent.Type() - Constructor for enum de.lmu.ifi.dbs.elki.index.preprocessed.knn.KNNChangeEvent.Type

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.database.ids.generic.DistanceDBIDPairKNNHeap

Cached distance to k nearest neighbor (to avoid going through AbstractKNNHeap.peek() each time).

knndistance - Variable in class de.lmu.ifi.dbs.elki.database.ids.generic.DoubleDistanceDBIDPairKNNHeap

Cached distance to k nearest neighbor (to avoid going through AbstractKNNHeap.peek() too often).

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.

kNNdistanceAdjustment(E, Map<DBID, KNNList<D>>) - 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<D>>) - 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<D>>) - 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.

KNNDistanceOrder<O,D extends Distance<D>> - Class in de.lmu.ifi.dbs.elki.algorithm

Provides an order of the kNN-distances for all objects within the database.

KNNDistanceOrder(DistanceFunction<O, D>, int, double) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.KNNDistanceOrder

Constructor.

KNNDistanceOrder.Parameterizer<O,D extends Distance<D>> - Class in de.lmu.ifi.dbs.elki.algorithm

Parameterization class.

KNNDistanceOrder.Parameterizer() - Constructor for class de.lmu.ifi.dbs.elki.algorithm.KNNDistanceOrder.Parameterizer

Constructor.

KNNDistanceOrderResult<D extends Distance<D>> - Class in de.lmu.ifi.dbs.elki.result

Wraps a list containing the knn distances.

KNNDistanceOrderResult(String, String, List<D>) - Constructor for class de.lmu.ifi.dbs.elki.result.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.

knnDistances - Variable in class de.lmu.ifi.dbs.elki.result.KNNDistanceOrderResult

Store the kNN Distances

KNNHeap<D extends Distance<D>> - Interface in de.lmu.ifi.dbs.elki.database.ids.distance

Interface for kNN heaps.

KNNIndex<O> - Interface in de.lmu.ifi.dbs.elki.index

Index with support for kNN queries.

knnJoin - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.DeLiClu

Holds the knnJoin algorithm.

KNNJoin<V extends NumberVector<?>,D extends Distance<D>,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, D>, int) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.KNNJoin

Constructor.

KNNJoin.Parameterizer<V extends NumberVector<?>,D extends Distance<D>,N extends SpatialNode<N,E>,E extends SpatialEntry> - Class in de.lmu.ifi.dbs.elki.algorithm

Parameterization class.

KNNJoin.Parameterizer() - Constructor for class de.lmu.ifi.dbs.elki.algorithm.KNNJoin.Parameterizer

KNNJoin.Task - Class in de.lmu.ifi.dbs.elki.algorithm

Task in the processing queue.

KNNJoin.Task(D, int, int) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.KNNJoin.Task

Constructor.

KNNJoinMaterializeKNNPreprocessor<V extends NumberVector<?>,D extends Distance<D>> - 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, D>, int) - Constructor for class de.lmu.ifi.dbs.elki.index.preprocessed.knn.KNNJoinMaterializeKNNPreprocessor

Constructor.

KNNJoinMaterializeKNNPreprocessor.Factory<O extends NumberVector<?>,D extends Distance<D>> - Class in de.lmu.ifi.dbs.elki.index.preprocessed.knn

The parameterizable factory.

KNNJoinMaterializeKNNPreprocessor.Factory(int, DistanceFunction<? super O, D>) - Constructor for class de.lmu.ifi.dbs.elki.index.preprocessed.knn.KNNJoinMaterializeKNNPreprocessor.Factory

Constructor.

KNNJoinMaterializeKNNPreprocessor.Factory.Parameterizer<O extends NumberVector<?>,D extends Distance<D>> - Class in de.lmu.ifi.dbs.elki.index.preprocessed.knn

Parameterization class

KNNJoinMaterializeKNNPreprocessor.Factory.Parameterizer() - Constructor for class de.lmu.ifi.dbs.elki.index.preprocessed.knn.KNNJoinMaterializeKNNPreprocessor.Factory.Parameterizer

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.Mode() - Constructor for enum de.lmu.ifi.dbs.elki.algorithm.clustering.onedimensional.KNNKernelDensityMinimaClustering.Mode

KNNKernelDensityMinimaClustering.Parameterizer<V extends NumberVector<?>> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.onedimensional

Parameterization class.

KNNKernelDensityMinimaClustering.Parameterizer() - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.onedimensional.KNNKernelDensityMinimaClustering.Parameterizer

KNNList<D extends Distance<D>> - Interface in de.lmu.ifi.dbs.elki.database.ids.distance

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,D extends NumberDistance<D,?>> - Class in de.lmu.ifi.dbs.elki.algorithm.outlier

Outlier Detection based on the distance of an object to its k nearest neighbor.

KNNOutlier(DistanceFunction<? super O, D>, int) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.outlier.KNNOutlier

Constructor for a single kNN query.

KNNOutlier.Parameterizer<O,D extends NumberDistance<D,?>> - Class in de.lmu.ifi.dbs.elki.algorithm.outlier

Parameterization class.

KNNOutlier.Parameterizer() - Constructor for class de.lmu.ifi.dbs.elki.algorithm.outlier.KNNOutlier.Parameterizer

knnq - Variable in class de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.AbstractMkTree

Internal class for performing knn queries

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,D extends Distance<D>> - 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.distance.distancefunction.MinKDistance.Instance

KNN query instance

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_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.KNNWeightOutlier

The kNN query used.

KNNQUERY_ID - Static variable in class de.lmu.ifi.dbs.elki.distance.distancefunction.MinKDistance

OptionID for the KNN query class to use (preprocessor, approximation, ...)

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>, PCAFilteredRunner<NV>, KNNQuery<NV, DoubleDistance>, 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(DistanceFunction<V, DoubleDistance>, PCAFilteredRunner<V>, Integer) - Constructor for class de.lmu.ifi.dbs.elki.index.preprocessed.localpca.KNNQueryFilteredPCAIndex.Factory

Constructor.

KNNQueryFilteredPCAIndex.Factory.Parameterizer<NV extends NumberVector<?>> - Class in de.lmu.ifi.dbs.elki.index.preprocessed.localpca

Parameterization class.

KNNQueryFilteredPCAIndex.Factory.Parameterizer() - Constructor for class de.lmu.ifi.dbs.elki.index.preprocessed.localpca.KNNQueryFilteredPCAIndex.Factory.Parameterizer

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.

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, D>) - Method in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.OnlineLOF.LOFKNNListener

Invoked after kNNs have been inserted and updated, updates the result.

kNNsRemoved(DBIDs, DBIDs, DBIDs, FlexibleLOF.LOFResult<O, D>) - Method in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.OnlineLOF.LOFKNNListener

Invoked after kNNs have been removed and updated, updates the result.

KNNSubList<D extends Distance<D>> - Class in de.lmu.ifi.dbs.elki.database.ids.generic

Sublist of an existing result to contain only the first k elements.

KNNSubList(KNNList<D>, int) - Constructor for class de.lmu.ifi.dbs.elki.database.ids.generic.KNNSubList

Constructor.

KNNSubList.Itr - Class in de.lmu.ifi.dbs.elki.database.ids.generic

Iterator for the sublist.

KNNSubList.Itr() - Constructor for class de.lmu.ifi.dbs.elki.database.ids.generic.KNNSubList.Itr

KNNWeightOutlier<O,D extends NumberDistance<D,?>> - Class in de.lmu.ifi.dbs.elki.algorithm.outlier

Outlier Detection based on the accumulated distances of a point to its k nearest neighbors.

KNNWeightOutlier(DistanceFunction<? super O, D>, int) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.outlier.KNNWeightOutlier

Constructor with parameters.

KNNWeightOutlier.Parameterizer<O,D extends NumberDistance<D,?>> - Class in de.lmu.ifi.dbs.elki.algorithm.outlier

Parameterization class.

KNNWeightOutlier.Parameterizer() - Constructor for class de.lmu.ifi.dbs.elki.algorithm.outlier.KNNWeightOutlier.Parameterizer

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.

KolmogorovSmirnovDistanceFunction.Parameterizer() - Constructor for class de.lmu.ifi.dbs.elki.distance.distancefunction.histogram.KolmogorovSmirnovDistanceFunction.Parameterizer

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.

KolmogorovSmirnovTest.Parameterizer() - Constructor for class de.lmu.ifi.dbs.elki.math.statistics.tests.KolmogorovSmirnovTest.Parameterizer

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

Holds the value of LoOP.KREACH_ID.

kreach - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LoOP.Parameterizer

Holds the value of LoOP.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

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 than 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.

Kulczynski1DistanceFunction - Class in de.lmu.ifi.dbs.elki.distance.distancefunction

Kulczynski similarity 1, in distance form.

Kulczynski1DistanceFunction() - Constructor for class de.lmu.ifi.dbs.elki.distance.distancefunction.Kulczynski1DistanceFunction

Deprecated.

Use Kulczynski1DistanceFunction.STATIC instance instead.

Kulczynski1DistanceFunction.Parameterizer - Class in de.lmu.ifi.dbs.elki.distance.distancefunction

Parameterization class.

Kulczynski1DistanceFunction.Parameterizer() - Constructor for class de.lmu.ifi.dbs.elki.distance.distancefunction.Kulczynski1DistanceFunction.Parameterizer

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.

Kulczynski1SimilarityFunction.Parameterizer() - Constructor for class de.lmu.ifi.dbs.elki.distance.similarityfunction.Kulczynski1SimilarityFunction.Parameterizer

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.

Kulczynski2SimilarityFunction.Parameterizer() - Constructor for class de.lmu.ifi.dbs.elki.distance.similarityfunction.Kulczynski2SimilarityFunction.Parameterizer

KullbackLeiblerDivergenceAsymmetricDistanceFunction - Class in de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic

Kullback-Leibler (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.

KullbackLeiblerDivergenceAsymmetricDistanceFunction.Parameterizer() - Constructor for class de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic.KullbackLeiblerDivergenceAsymmetricDistanceFunction.Parameterizer

KullbackLeiblerDivergenceReverseAsymmetricDistanceFunction - Class in de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic

Kullback-Leibler (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.

KullbackLeiblerDivergenceReverseAsymmetricDistanceFunction.Parameterizer() - Constructor for class de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic.KullbackLeiblerDivergenceReverseAsymmetricDistanceFunction.Parameterizer

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