- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.AbstractProjectedClustering
-
- 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
-
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.EM.Parameterizer
-
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.AbstractKMeans
-
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansLloyd.Parameterizer
-
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansMacQueen.Parameterizer
-
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMediansLloyd.Parameterizer
-
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMedoidsEM
-
- 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
-
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMedoidsPAM.Parameterizer
-
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.KNNDistanceOrder
-
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.KNNDistanceOrder.Parameterizer
-
- 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 - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.ABOD
-
k parameter
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.ABOD.Parameterizer
-
- 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 - 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.INFLO
-
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.INFLO.Parameterizer
-
- 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
-
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.KNNWeightOutlier.Parameterizer
-
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.LDOF
-
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.LDOF.Parameterizer
-
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.LOF
-
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.LOF.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.OnlineLOF.Parameterizer
-
The neighborhood size to use
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.ReferenceBasedOutlierDetection
-
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.ReferenceBasedOutlierDetection.Parameterizer
-
- 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.visualization.KNNExplorer.ExplorerWindow
-
- k - Variable in class de.lmu.ifi.dbs.elki.database.query.knn.KNNUtil.KNNSubList
-
Parameter k
- k - Variable in class de.lmu.ifi.dbs.elki.datasource.filter.AbstractRandomFeatureSelectionFilter
-
Holds the desired cardinality of the subset of attributes selected for
projection.
- k - Variable in class de.lmu.ifi.dbs.elki.datasource.filter.AbstractRandomFeatureSelectionFilter.Parameterizer
-
- 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.preprocessed.knn.AbstractMaterializeKNNPreprocessor.Factory
-
- k - Variable in class de.lmu.ifi.dbs.elki.index.preprocessed.knn.AbstractMaterializeKNNPreprocessor.Factory.Parameterizer
-
- 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
-
- 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
-
- k - Variable in class de.lmu.ifi.dbs.elki.index.preprocessed.preference.HiSCPreferenceVectorIndex.Factory.Parameterizer
-
- 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.utilities.datastructures.heap.KNNList
-
The value of k this was materialized for.
- 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.outlier.DistanceStddevOutlier
-
Number of neighbors to get.
- k - Variable in class tutorial.outlier.DistanceStddevOutlier.Parameterizer
-
Number of neighbors to get
- 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
-
- 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
-
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.clustering.AbstractProjectedClustering
-
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
-
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.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.ABOD
-
Parameter for k, the number of neighbors used in kNN queries.
- K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.AbstractAggarwalYuOutlier
-
OptionID for the target dimensionality
- 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.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.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.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
-
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.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.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.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.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_max - Variable in class de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.AbstractMkTreeUnified
-
Holds the maximum value of k to support.
- k_max - Variable in class de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.AbstractMkTreeUnifiedFactory
-
- k_max - Variable in class de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.AbstractMkTreeUnifiedFactory.Parameterizer
-
- k_max - Variable in class de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkapp.MkAppTree
-
Parameter k.
- k_max - Variable in class de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkapp.MkAppTreeFactory
-
Parameter k.
- k_max - Variable in class de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkapp.MkAppTreeFactory.Parameterizer
-
Parameter k.
- k_max - Variable in class de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkcop.MkCoPTree
-
Parameter k.
- k_max - Variable in class de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkcop.MkCopTreeFactory
-
Parameter k.
- k_max - Variable in class de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkcop.MkCopTreeFactory.Parameterizer
-
- k_max - Variable in class de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mktab.MkTabDirectoryEntry
-
The maximal number of knn distances to be stored.
- k_max - Variable in class de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mktab.MkTabLeafEntry
-
The maximal number of knn distances to be stored.
- 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_ID - Static variable in class de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.AbstractMkTreeUnifiedFactory
-
Parameter specifying the maximal number k of reverse k nearest neighbors to
be supported, must be an integer greater than 0.
- 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
-
- kcomp - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.LoOP
-
- kcomp - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.LoOP.Parameterizer
-
- KCOMP_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.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(boolean, 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
-
- 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.EpanechnikovKernelDensityFunction
-
Static instance.
- KERNEL - Static variable in class de.lmu.ifi.dbs.elki.math.statistics.GaussianKernelDensityFunction
-
Static instance.
- KERNEL - Static variable in class de.lmu.ifi.dbs.elki.math.statistics.TriangularKernelDensityFunction
-
Static instance.
- KERNEL - Static variable in class de.lmu.ifi.dbs.elki.math.statistics.UniformKernelDensityFunction
-
Static instance.
- KERNEL_FUNCTION_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.ABOD
-
Parameter for the kernel function.
- KernelDensityEstimator - Class in de.lmu.ifi.dbs.elki.math.statistics
-
Estimate density given an array of points.
- KernelDensityEstimator(double[], double, double, KernelDensityFunction, int) - Constructor for class de.lmu.ifi.dbs.elki.math.statistics.KernelDensityEstimator
-
Initialize and execute kernel density estimation.
- KernelDensityEstimator(double[], KernelDensityFunction) - 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
-
Inner function of a kernel density estimator.
- 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(double[][]) - Constructor for class de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel.KernelMatrix
-
Wraps the matrixArray in a KernelMatrix
- KernelMatrix(PrimitiveSimilarityFunction<? super O, DoubleDistance>, Relation<? extends O>) - Constructor for class de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel.KernelMatrix
-
Deprecated.
ID mapping is not reliable!
- KernelMatrix(PrimitiveSimilarityFunction<? super O, DoubleDistance>, Relation<? extends O>, ArrayDBIDs) - 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).
- 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
- 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(VisualizationTask) - Constructor for class de.lmu.ifi.dbs.elki.visualization.visualizers.visunproj.KeyVisualization
-
Constructor.
- KeyVisualization.Factory - Class in de.lmu.ifi.dbs.elki.visualization.visualizers.visunproj
-
Visualization factory
- KeyVisualization.Factory() - Constructor for class de.lmu.ifi.dbs.elki.visualization.visualizers.visunproj.KeyVisualization.Factory
-
- KMeans - Interface in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans
-
Some constants and options shared among kmeans family algorithms.
- KMEANSBORDER - Static variable in class de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.cluster.VoronoiVisualization
-
Generic tags to indicate the type of element.
- KMeansInitialization<V> - Interface in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans
-
Interface for initializing K-Means
- KMeansLloyd<V extends NumberVector<V,?>,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<V,?>,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<V,?>,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<V,?>,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
-
- KMeansPlusPlusInitialMeans<V,D extends NumberDistance<D,?>> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans
-
K-Means++ initialization for k-means.
- KMeansPlusPlusInitialMeans(Long) - 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
-
- KMediansLloyd<V extends NumberVector<V,?>,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<V,?>,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
-
- knn - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.subspace.SOD
-
- knn - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.subspace.SOD.Parameterizer
-
- KNN_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.subspace.SOD
-
Parameter to specify the number of shared nearest neighbors to be
considered for learning the subspace properties., must be an integer
greater than 0.
- 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
-
- 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(DistanceQuery<O, D>) - 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, KNNHeap<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<D>, Map<DBID, KNNHeap<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<D>, Map<DBID, KNNHeap<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
-
- 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(DistanceQuery<O, D>) - 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
- KNNExplorer<O extends NumberVector<?,?>,D extends NumberDistance<D,?>> - Class in de.lmu.ifi.dbs.elki.application.visualization
-
User application to explore the k Nearest Neighbors for a given data set and
distance function.
- KNNExplorer(boolean, Database, DistanceFunction<O, D>) - Constructor for class de.lmu.ifi.dbs.elki.application.visualization.KNNExplorer
-
Constructor.
- KNNExplorer.ExplorerWindow - Class in de.lmu.ifi.dbs.elki.application.visualization
-
Main window of KNN Explorer.
- KNNExplorer.ExplorerWindow() - Constructor for class de.lmu.ifi.dbs.elki.application.visualization.KNNExplorer.ExplorerWindow
-
Constructor.
- KNNExplorer.ExplorerWindow.SeriesLabelRenderer - Class in de.lmu.ifi.dbs.elki.application.visualization
-
Renderer for the labels, with coloring as in the plot.
- KNNExplorer.ExplorerWindow.SeriesLabelRenderer() - Constructor for class de.lmu.ifi.dbs.elki.application.visualization.KNNExplorer.ExplorerWindow.SeriesLabelRenderer
-
Constructor.
- KNNExplorer.Parameterizer<O extends NumberVector<?,?>,D extends NumberDistance<D,?>> - Class in de.lmu.ifi.dbs.elki.application.visualization
-
Parameterization class.
- KNNExplorer.Parameterizer() - Constructor for class de.lmu.ifi.dbs.elki.application.visualization.KNNExplorer.Parameterizer
-
- KNNHeap<D extends Distance<D>> - Class in de.lmu.ifi.dbs.elki.utilities.datastructures.heap
-
Heap used for KNN management.
- KNNHeap(int, D) - Constructor for class de.lmu.ifi.dbs.elki.utilities.datastructures.heap.KNNHeap
-
Constructor.
- KNNHeap(int) - Constructor for class de.lmu.ifi.dbs.elki.utilities.datastructures.heap.KNNHeap
-
Simplified constructor.
- KNNHeap.Comp<D extends Distance<D>> - Class in de.lmu.ifi.dbs.elki.utilities.datastructures.heap
-
Comparator to use.
- KNNHeap.Comp() - Constructor for class de.lmu.ifi.dbs.elki.utilities.datastructures.heap.KNNHeap.Comp
-
- 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<V,?>,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<V,?>,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<V,?>,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<O,?>,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<O,?>,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
-
- KNNList<D extends Distance<D>> - Class in de.lmu.ifi.dbs.elki.utilities.datastructures.heap
-
Finalized KNN List.
- KNNList(KNNHeap<D>) - Constructor for class de.lmu.ifi.dbs.elki.utilities.datastructures.heap.KNNList
-
Constructor, to be called from KNNHeap only.
- KNNList(Queue<D>, int) - Constructor for class de.lmu.ifi.dbs.elki.utilities.datastructures.heap.KNNList
-
Constructor.
- KNNList.Itr - Class in de.lmu.ifi.dbs.elki.utilities.datastructures.heap
-
Iterator
- KNNList.Itr() - Constructor for class de.lmu.ifi.dbs.elki.utilities.datastructures.heap.KNNList.Itr
-
- 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.
- 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
-
- knnQuery - Variable in class de.lmu.ifi.dbs.elki.application.visualization.KNNExplorer.ExplorerWindow
-
Holds the associated kNN query
- 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 - Variable in class de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mktab.MkTabTreeIndex
-
The knn query we use internally.
- 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<? extends NV,?>> - 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<V,?>> - 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<NV,?>> - 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.LOFResult
-
The kNN query w.r.t. the reachability distance.
- kNNRefer - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.LOF.LOFResult
-
The kNN query w.r.t. the reference neighborhood distance.
- KNNResult<D extends Distance<D>> - Interface in de.lmu.ifi.dbs.elki.database.query.knn
-
Interface for kNN results - List<> like.
- kNNsChanged(KNNChangeEvent) - Method in class de.lmu.ifi.dbs.elki.algorithm.outlier.OnlineLOF.LOFKNNListener
-
- kNNsChanged(KNNChangeEvent, KNNChangeEvent) - Method in class de.lmu.ifi.dbs.elki.algorithm.outlier.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, LOF.LOFResult<O, D>) - Method in class de.lmu.ifi.dbs.elki.algorithm.outlier.OnlineLOF.LOFKNNListener
-
Invoked after kNNs have been inserted and updated, updates the result.
- kNNsRemoved(DBIDs, DBIDs, DBIDs, LOF.LOFResult<O, D>) - Method in class de.lmu.ifi.dbs.elki.algorithm.outlier.OnlineLOF.LOFKNNListener
-
Invoked after kNNs have been removed and updated, updates the result.
- KNNUtil - Class in de.lmu.ifi.dbs.elki.database.query.knn
-
Helper classes for kNN results.
- KNNUtil() - Constructor for class de.lmu.ifi.dbs.elki.database.query.knn.KNNUtil
-
- KNNUtil.DBIDIterator - Class in de.lmu.ifi.dbs.elki.database.query.knn
-
Proxy iterator for accessing DBIDs.
- KNNUtil.DBIDIterator(Iterator<? extends DistanceResultPair<?>>) - Constructor for class de.lmu.ifi.dbs.elki.database.query.knn.KNNUtil.DBIDIterator
-
Constructor.
- KNNUtil.DBIDItr - Class in de.lmu.ifi.dbs.elki.database.query.knn
-
Proxy iterator for accessing DBIDs.
- KNNUtil.DBIDItr(Iterator<? extends DistanceResultPair<?>>) - Constructor for class de.lmu.ifi.dbs.elki.database.query.knn.KNNUtil.DBIDItr
-
Constructor.
- KNNUtil.DBIDView - Class in de.lmu.ifi.dbs.elki.database.query.knn
-
A view on the DBIDs of the result
- KNNUtil.DBIDView(KNNResult<?>) - Constructor for class de.lmu.ifi.dbs.elki.database.query.knn.KNNUtil.DBIDView
-
Constructor.
- KNNUtil.DistanceItr<D extends Distance<D>> - Class in de.lmu.ifi.dbs.elki.database.query.knn
-
Proxy iterator for accessing DBIDs.
- KNNUtil.DistanceItr(Iterator<? extends DistanceResultPair<D>>) - Constructor for class de.lmu.ifi.dbs.elki.database.query.knn.KNNUtil.DistanceItr
-
Constructor.
- KNNUtil.DistanceView<D extends Distance<D>> - Class in de.lmu.ifi.dbs.elki.database.query.knn
-
A view on the Distances of the result
- KNNUtil.DistanceView(KNNResult<D>) - Constructor for class de.lmu.ifi.dbs.elki.database.query.knn.KNNUtil.DistanceView
-
Constructor.
- KNNUtil.KNNSubList<D extends Distance<D>> - Class in de.lmu.ifi.dbs.elki.database.query.knn
-
Sublist of an existing result to contain only the first k elements.
- KNNUtil.KNNSubList(KNNResult<D>, int) - Constructor for class de.lmu.ifi.dbs.elki.database.query.knn.KNNUtil.KNNSubList
-
Constructor.
- KNNUtil.KNNSubList.Itr - Class in de.lmu.ifi.dbs.elki.database.query.knn
-
Iterator for the sublist.
- KNNUtil.KNNSubList.Itr() - Constructor for class de.lmu.ifi.dbs.elki.database.query.knn.KNNUtil.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
-
- 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.LoOP
-
- kreach - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.LoOP.Parameterizer
-
- KREACH_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.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.
