- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.benchmark.KNNBenchmarkAlgorithm
-
Number of neighbors to retrieve.
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.benchmark.KNNBenchmarkAlgorithm.Parameterizer
-
K parameter
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.benchmark.ValidateApproximativeKNNIndex
-
Number of neighbors to retrieve.
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.benchmark.ValidateApproximativeKNNIndex.Parameterizer
-
K parameter
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.classification.KNNClassifier
-
Holds the value of @link #K_PARAM}.
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.classification.KNNClassifier.Parameterizer
-
Holds the value of @link #K_PARAM}.
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.AbstractProjectedClustering
-
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.AbstractProjectedClustering.Parameterizer
-
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.COPAC.Settings
-
Neighborhood size.
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.ERiC.Settings
-
Neighborhood size.
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.em.EM
-
Number of clusters
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.em.EM.Parameterizer
-
Number of clusters.
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan.LSDBC
-
kNN parameter.
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan.LSDBC.Parameterizer
-
kNN parameter.
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.AbstractKMeans
-
Number of cluster centers to initialize.
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.AbstractKMeans.Parameterizer
-
k Parameter.
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.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
-
- 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
-
The number of clusters to produce.
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMedoidsPAM.Parameterizer
-
The number of clusters to produce.
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.XMeans
-
Effective number of clusters, minimum and maximum.
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.onedimensional.KNNKernelDensityMinimaClustering
-
Number of neighbors to use for bandwidth.
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.onedimensional.KNNKernelDensityMinimaClustering.Parameterizer
-
Number of neighbors to use for bandwidth.
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain.UKMeans
-
Number of cluster centers to initialize.
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain.UKMeans.Parameterizer
-
Number of cluster centers to initialize.
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.KNNDistancesSampler
-
Parameter k.
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.KNNDistancesSampler.KNNDistanceOrderResult
-
Number of neighbors considered for this KNNDIstanceOrder
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.KNNDistancesSampler.Parameterizer
-
Parameter k.
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.KNNJoin
-
The k parameter.
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.KNNJoin.Parameterizer
-
K parameter.
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.anglebased.FastABOD
-
Number of nearest neighbors.
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.anglebased.FastABOD.Parameterizer
-
Number of neighbors.
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.COP
-
Number of neighbors to be considered.
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.COP.Parameterizer
-
Number of neighbors to be considered.
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.HilOut
-
Number of nearest neighbors
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.HilOut.Parameterizer
-
Neighborhood size
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.KNNOutlier
-
The parameter k (including query point!)
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.KNNOutlier.Parameterizer
-
k parameter
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.KNNWeightOutlier
-
Holds the number of nearest neighbors to query (excluding the query point!)
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.KNNWeightOutlier.Parameterizer
-
k parameter
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.LocalIsolationCoefficient
-
Holds the number of nearest neighbors to query (including query point!)
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.LocalIsolationCoefficient.Parameterizer
-
k parameter
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.ODIN
-
Number of neighbors for kNN graph.
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.ODIN.Parameterizer
-
Number of nearest neighbors to use.
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.parallel.KNNWeightProcessor.Instance
-
k Parameter
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.parallel.KNNWeightProcessor
-
K parameter
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.parallel.ParallelKNNOutlier
-
Parameter k
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.parallel.ParallelKNNOutlier.Parameterizer
-
K parameter
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.parallel.ParallelKNNWeightOutlier
-
Parameter k
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.parallel.ParallelKNNWeightOutlier.Parameterizer
-
K parameter
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.ReferenceBasedOutlierDetection
-
Holds the number of neighbors to use for density estimation.
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.ReferenceBasedOutlierDetection.Parameterizer
-
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.DWOF
-
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.DWOF.Parameterizer
-
Number of neighbors to get
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.intrinsic.IntrinsicDimensionalityOutlier
-
Number of neighbors to use.
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.intrinsic.IntrinsicDimensionalityOutlier.Parameterizer
-
Number of neighbors to use for ID estimation.
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.COF
-
The number of neighbors to query (including the query point!)
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.COF.Parameterizer
-
The neighborhood size to use.
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.INFLO
-
Number of neighbors to use.
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.INFLO.Parameterizer
-
Number of neighbors to use.
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LDF
-
Parameter k.
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LDF.Parameterizer
-
The neighborhood size to use.
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LDOF
-
Number of neighbors to query.
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LDOF.Parameterizer
-
Number of neighbors to use
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LOF
-
The number of neighbors to query (including the query point!)
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LOF.Parameterizer
-
The neighborhood size to use.
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.parallel.ParallelLOF
-
Parameter k
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.parallel.ParallelLOF.Parameterizer
-
K parameter
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.parallel.ParallelSimplifiedLOF
-
Parameter k
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.parallel.ParallelSimplifiedLOF.Parameterizer
-
K parameter
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.SimpleKernelDensityLOF
-
Parameter k.
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.SimpleKernelDensityLOF.Parameterizer
-
The neighborhood size to use.
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.SimplifiedLOF
-
The number of neighbors to query, excluding the query point.
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.SimplifiedLOF.Parameterizer
-
The neighborhood size to use.
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.VarianceOfVolume
-
The number of neighbors to query (including the query point!)
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.VarianceOfVolume.Parameterizer
-
The neighborhood size to use.
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.meta.FeatureBagging
-
The parameters k for LOF.
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.meta.FeatureBagging.Parameterizer
-
The neighborhood size to use.
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.SimpleCOP
-
Number of neighbors to be considered.
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.SimpleCOP.Parameterizer
-
Number of neighbors to be considered.
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.CTLuGLSBackwardSearchAlgorithm
-
Parameter k - neighborhood size
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.CTLuGLSBackwardSearchAlgorithm.Parameterizer
-
Parameter k - neighborhood size
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.CTLuRandomWalkEC
-
Parameter k.
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.CTLuRandomWalkEC.Parameterizer
-
Parameter for kNN.
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood.PrecomputedKNearestNeighborNeighborhood.Factory
-
parameter k
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood.PrecomputedKNearestNeighborNeighborhood.Factory.Parameterizer
-
Parameter k
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.subspace.AbstractAggarwalYuOutlier
-
The target dimensionality.
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.subspace.AbstractAggarwalYuOutlier.Parameterizer
-
k Parameter.
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.statistics.AveragePrecisionAtK
-
The parameter k - the number of neighbors to retrieve.
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.statistics.AveragePrecisionAtK.Parameterizer
-
Neighborhood size.
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.statistics.HopkinsStatisticClusteringTendency
-
Nearest neighbor to use.
- k - Variable in class de.lmu.ifi.dbs.elki.algorithm.statistics.HopkinsStatisticClusteringTendency.Parameterizer
-
Nearest neighbor number.
- k - Variable in class de.lmu.ifi.dbs.elki.application.cache.CacheDoubleDistanceKNNLists
-
Number of neighbors to precompute.
- k - Variable in class de.lmu.ifi.dbs.elki.application.cache.CacheDoubleDistanceKNNLists.Parameterizer
-
Number of neighbors to precompute.
- k - Variable in class de.lmu.ifi.dbs.elki.database.ids.generic.KNNSubList
-
Parameter k.
- k - Variable in class de.lmu.ifi.dbs.elki.database.ids.integer.DoubleIntegerDBIDKNNHeap
-
k for this heap.
- k - Variable in class de.lmu.ifi.dbs.elki.database.ids.integer.DoubleIntegerDBIDKNNList
-
The k value this list was generated for.
- k - Variable in class de.lmu.ifi.dbs.elki.database.ids.integer.DoubleIntegerDBIDPairKNNListHeap
-
The value of k this was materialized for.
- k - Variable in class de.lmu.ifi.dbs.elki.database.ids.integer.IntegerDBIDKNNSubList
-
Parameter k.
- k - Variable in class de.lmu.ifi.dbs.elki.datasource.filter.transform.NumberVectorRandomFeatureSelectionFilter
-
Holds the desired cardinality of the subset of attributes selected for
projection.
- k - Variable in class de.lmu.ifi.dbs.elki.datasource.filter.transform.NumberVectorRandomFeatureSelectionFilter.Parameterizer
-
Number of attributes to select.
- k - Variable in class de.lmu.ifi.dbs.elki.evaluation.scores.PrecisionAtKEvaluation
-
Parameter k.
- k - Variable in class de.lmu.ifi.dbs.elki.evaluation.scores.PrecisionAtKEvaluation.Parameterizer
-
K parameter
- k - Variable in class de.lmu.ifi.dbs.elki.index.idistance.InMemoryIDistanceIndex.Factory
-
Number of reference points
- k - Variable in class de.lmu.ifi.dbs.elki.index.idistance.InMemoryIDistanceIndex.Factory.Parameterizer
-
Number of reference points
- k - Variable in class de.lmu.ifi.dbs.elki.index.lsh.hashfamilies.AbstractProjectedHashFunctionFamily
-
The number of projections to use for each hash function.
- k - Variable in class de.lmu.ifi.dbs.elki.index.lsh.hashfamilies.AbstractProjectedHashFunctionFamily.Parameterizer
-
The number of projections to use for each hash function.
- k - Variable in class de.lmu.ifi.dbs.elki.index.lsh.hashfamilies.CosineHashFunctionFamily
-
The number of projections to use for each hash function.
- k - Variable in class de.lmu.ifi.dbs.elki.index.lsh.hashfamilies.CosineHashFunctionFamily.Parameterizer
-
The number of projections to use for each hash function.
- k - Variable in class de.lmu.ifi.dbs.elki.index.preprocessed.knn.AbstractMaterializeKNNPreprocessor.Factory
-
- 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
-
Number of neighbors to query.
- k - Variable in class de.lmu.ifi.dbs.elki.index.preprocessed.localpca.KNNQueryFilteredPCAIndex.Factory.Parameterizer
-
Number of neighbors to query.
- k - Variable in class de.lmu.ifi.dbs.elki.index.preprocessed.localpca.KNNQueryFilteredPCAIndex
-
Number of neighbors to query.
- k - Variable in class de.lmu.ifi.dbs.elki.index.preprocessed.preference.HiSCPreferenceVectorIndex.Factory
-
- 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.linearalgebra.randomprojections.SimplifiedRandomHyperplaneProjectionFamily.SignedProjection
-
Output dimensionality
- 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.parallel.processor.KDistanceProcessor.Instance
-
k Parameter
- k - Variable in class de.lmu.ifi.dbs.elki.parallel.processor.KDistanceProcessor
-
K parameter
- k - Variable in class de.lmu.ifi.dbs.elki.parallel.processor.KNNProcessor.Instance
-
k Parameter
- k - Variable in class de.lmu.ifi.dbs.elki.parallel.processor.KNNProcessor
-
K parameter
- k - Variable in class de.lmu.ifi.dbs.elki.utilities.scaling.outlier.OutlierGammaScaling
-
Gamma parameter k
- k - Variable in class de.lmu.ifi.dbs.elki.utilities.scaling.outlier.TopKOutlierScaling
-
Number of outliers to keep.
- k - Variable in class de.lmu.ifi.dbs.elki.utilities.scaling.outlier.TopKOutlierScaling.Parameterizer
-
- k_0 - Variable in class de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkcop.ApproximationLine
-
The start value for k.
- k_c - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.intrinsic.IDOS
-
kNN for the context set (ID computation).
- k_c - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.intrinsic.IDOS.Parameterizer
-
kNN for the context set (ID computation).
- k_i - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.AbstractProjectedClustering
-
- k_i - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.AbstractProjectedClustering.Parameterizer
-
- K_I_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.AbstractProjectedClustering.Parameterizer
-
Parameter to specify the multiplier for the initial number of seeds, must
be an integer greater than 0.
- K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.benchmark.KNNBenchmarkAlgorithm.Parameterizer
-
Parameter for the number of neighbors.
- K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.benchmark.ValidateApproximativeKNNIndex.Parameterizer
-
Parameter for the number of neighbors.
- K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.classification.KNNClassifier.Parameterizer
-
Parameter to specify the number of neighbors to take into account for
classification, must be an integer greater than 0.
- K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.AbstractProjectedClustering.Parameterizer
-
Parameter to specify the number of clusters to find, must be an integer
greater than 0.
- K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.COPAC.Settings.Parameterizer
-
Size for the kNN neighborhood used in the PCA step of COPAC.
- K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.ERiC.Settings.Parameterizer
-
Size for the kNN neighborhood used in the PCA step of ERiC.
- K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.HiCO.Parameterizer
-
Optional parameter to specify the number of nearest neighbors considered
in the PCA, must be an integer greater than 0.
- K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.em.EM.Parameterizer
-
Parameter to specify the number of clusters to find, must be an integer
greater than 0.
- K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan.LSDBC.Parameterizer
-
Parameter for neighborhood size.
- K_ID - Static variable in 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.KNNDistancesSampler.Parameterizer
-
Parameter to specify the distance of the k-distant object to be assessed,
must be an integer greater than 0.
- K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.KNNJoin.Parameterizer
-
Parameter that specifies the k-nearest neighbors to be assigned, must be an
integer greater than 0.
- K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.anglebased.FastABOD.Parameterizer
-
Parameter for the nearest neighbors.
- K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.COP.Parameterizer
-
Parameter to specify the number of nearest neighbors of an object to be
considered for computing its COP_SCORE, must be an integer greater than
0.
- K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.HilOut.Parameterizer
-
Parameter to specify how many next neighbors should be used in the
computation
- K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.KNNOutlier.Parameterizer
-
Parameter to specify the k nearest neighbor
- K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.KNNWeightOutlier.Parameterizer
-
Parameter to specify the k nearest neighbor.
- K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.LocalIsolationCoefficient.Parameterizer
-
Parameter to specify the k nearest neighbor.
- K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.ODIN.Parameterizer
-
Parameter for the number of nearest neighbors:
-odin.k <int>
- K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.ReferenceBasedOutlierDetection.Parameterizer
-
Parameter to specify the number of nearest neighbors of an object, to be
considered for computing its REFOD_SCORE, must be an integer greater than
1.
- K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.DWOF.Parameterizer
-
Option ID for the number of neighbors.
- K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.intrinsic.IntrinsicDimensionalityOutlier.Parameterizer
-
Parameter for the number of neighbors.
- K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.COF.Parameterizer
-
Parameter to specify the neighborhood size for COF.
- K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.INFLO.Parameterizer
-
Parameter to specify the number of nearest neighbors of an object to be
considered for computing its INFLO score.
- K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LDF.Parameterizer
-
Option ID for k
- K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LDOF.Parameterizer
-
Parameter to specify the number of nearest neighbors of an object to be
considered for computing its LDOF_SCORE, must be an integer greater than
1.
- K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LOF.Parameterizer
-
Parameter to specify the number of nearest neighbors of an object to be
considered for computing its LOF score, must be an integer greater than
or equal to 1.
- K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.VarianceOfVolume.Parameterizer
-
Parameter to specify the number of nearest neighbors of an object to be
considered for computing its VOV score, must be an integer greater than
or equal to 1.
- K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.SimpleCOP.Parameterizer
-
Parameter to specify the number of nearest neighbors of an object to be
considered for computing its COP_SCORE, must be an integer greater than
0.
- K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.CTLuGLSBackwardSearchAlgorithm.Parameterizer
-
Parameter to specify the k nearest neighbors
- K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.CTLuRandomWalkEC.Parameterizer
-
Parameter to specify the number of neighbors.
- K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood.PrecomputedKNearestNeighborNeighborhood.Factory.Parameterizer
-
Parameter k
- K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.subspace.AbstractAggarwalYuOutlier.Parameterizer
-
OptionID for the target dimensionality.
- K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.statistics.AveragePrecisionAtK.Parameterizer
-
Parameter k to compute the average precision at.
- K_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.statistics.HopkinsStatisticClusteringTendency.Parameterizer
-
Parameter for k.
- K_ID - Static variable in class de.lmu.ifi.dbs.elki.application.cache.CacheDoubleDistanceKNNLists.Parameterizer
-
Parameter that specifies the number of neighbors to precompute.
- K_ID - Static variable in class de.lmu.ifi.dbs.elki.evaluation.scores.PrecisionAtKEvaluation.Parameterizer
-
Option ID for the k parameter.
- K_ID - Static variable in class de.lmu.ifi.dbs.elki.index.idistance.InMemoryIDistanceIndex.Factory.Parameterizer
-
Number of reference points.
- K_ID - Static variable in class de.lmu.ifi.dbs.elki.index.preprocessed.knn.AbstractMaterializeKNNPreprocessor.Factory
-
Parameter to specify the number of nearest neighbors of an object to be
materialized. must be an integer greater than 1.
- K_ID - Static variable in class de.lmu.ifi.dbs.elki.index.preprocessed.localpca.KNNQueryFilteredPCAIndex.Factory.Parameterizer
-
Optional parameter to specify the number of nearest neighbors
considered in the PCA, must be an integer greater than 0.
- K_ID - Static variable in class de.lmu.ifi.dbs.elki.index.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.index.tree.spatial.rstarvariants.rdknn.RdKNNTreeFactory
-
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_max - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.XMeans
-
Effective number of clusters, minimum and maximum.
- k_max - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.XMeans.Parameterizer
-
Minimum and maximum number of result clusters.
- k_max - Variable in class de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.MkTreeHeader
-
The maximum number k of reverse kNN queries to be supported.
- k_max - Variable in class de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.MkTreeSettings
-
Holds the maximum value of k to support.
- k_max - Variable in class de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rdknn.RdkNNSettings
-
Parameter k.
- k_max - Variable in class de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rdknn.RdKNNTreeHeader
-
The maximum number k of reverse kNN queries to be supported.
- K_MAX_ID - Static variable in class de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.AbstractMkTreeUnifiedFactory.Parameterizer
-
Parameter specifying the maximal number k of reverse k nearest neighbors
to be supported, must be an integer greater than 0.
- k_min - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.XMeans
-
Effective number of clusters, minimum and maximum.
- k_min - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.XMeans.Parameterizer
-
Minimum and maximum number of result clusters.
- K_MIN_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.XMeans.Parameterizer
-
Minimum number of clusters.
- K_MULTIPLIER_ID - Static variable in class de.lmu.ifi.dbs.elki.index.projected.ProjectedIndex.Factory.Parameterizer
-
Option ID for querying a larger k.
- k_r - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.intrinsic.IDOS
-
kNN for the reference set.
- k_r - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.intrinsic.IDOS.Parameterizer
-
kNN for the reference set.
- K_S_CRITICAL001 - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.subspace.OUTRES
-
Constant for Kolmogorov-Smirnov at alpha=0.01 (table value)
- kappa - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.FourC.Settings
-
Kappa penalty parameter, to punish deviation in low-variance
Eigenvectors.
- kappa - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.PreDeCon.Settings
-
The kappa penality factor for deviations in preferred dimensions.
- KAPPA - Static variable in class de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.cluster.EMClusterVisualization.Instance
-
Kappa constant,
- KAPPA_DEFAULT - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.FourC.Settings.Parameterizer
-
Default for kappa parameter.
- KAPPA_DEFAULT - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.PreDeCon.Settings.Parameterizer
-
Default for kappa parameter.
- KAPPA_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.FourC.Settings.Parameterizer
-
Parameter Kappa: penalty for deviations in preferred dimensions.
- KAPPA_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.PreDeCon.Settings.Parameterizer
-
Parameter Kappa: penalty for deviations in preferred dimensions.
- KappaDistribution - Class in de.lmu.ifi.dbs.elki.math.statistics.distribution
-
Kappa distribution, by Hosking.
- KappaDistribution(double, double, double, double) - Constructor for class de.lmu.ifi.dbs.elki.math.statistics.distribution.KappaDistribution
-
Constructor.
- KappaDistribution(double, double, double, double, Random) - Constructor for class de.lmu.ifi.dbs.elki.math.statistics.distribution.KappaDistribution
-
Constructor.
- KappaDistribution(double, double, double, double, RandomFactory) - Constructor for class de.lmu.ifi.dbs.elki.math.statistics.distribution.KappaDistribution
-
Constructor.
- KappaDistribution.Parameterizer - Class in de.lmu.ifi.dbs.elki.math.statistics.distribution
-
Parameterization class
- KappaDistribution.Parameterizer() - Constructor for class de.lmu.ifi.dbs.elki.math.statistics.distribution.KappaDistribution.Parameterizer
-
- KC_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.intrinsic.IDOS.Parameterizer
-
Parameter to specify the number of nearest neighbors of an object to be
used for the GED computation.
- kcomp - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LoOP
-
Comparison neighborhood size.
- kcomp - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LoOP.Parameterizer
-
- KCOMP_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LoOP.Parameterizer
-
Parameter to specify the number of nearest neighbors of an object to be
considered for computing its LOOP_SCORE, must be an integer greater than
1.
- KDDCLIApplication - Class in de.lmu.ifi.dbs.elki.application
-
Basic command line application for Knowledge Discovery in Databases use
cases.
- KDDCLIApplication(KDDTask) - Constructor for class de.lmu.ifi.dbs.elki.application.KDDCLIApplication
-
Constructor.
- KDDCLIApplication.Parameterizer - Class in de.lmu.ifi.dbs.elki.application
-
Parameterization class.
- KDDCLIApplication.Parameterizer() - Constructor for class de.lmu.ifi.dbs.elki.application.KDDCLIApplication.Parameterizer
-
- KDDTask - Class in de.lmu.ifi.dbs.elki
-
KDDTask encapsulates the common workflow of an unsupervised knowledge
discovery task.
- KDDTask(InputStep, AlgorithmStep, EvaluationStep, OutputStep, Collection<TrackedParameter>) - Constructor for class de.lmu.ifi.dbs.elki.KDDTask
-
Constructor.
- KDDTask.Parameterizer - Class in de.lmu.ifi.dbs.elki
-
Parameterization class.
- KDDTask.Parameterizer() - Constructor for class de.lmu.ifi.dbs.elki.KDDTask.Parameterizer
-
- KDEOS<O> - Class in de.lmu.ifi.dbs.elki.algorithm.outlier.lof
-
Generalized Outlier Detection with Flexible Kernel Density Estimates.
- KDEOS(DistanceFunction<? super O>, int, int, KernelDensityFunction, double, double, int) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.KDEOS
-
Constructor.
- KDEOS.Parameterizer<O> - Class in de.lmu.ifi.dbs.elki.algorithm.outlier.lof
-
Parameterization class
- KDEOS.Parameterizer() - Constructor for class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.KDEOS.Parameterizer
-
- kdist - Variable in class de.lmu.ifi.dbs.elki.database.ids.integer.DoubleIntegerDBIDKNNHeap
-
Current maximum value.
- KDistanceProcessor - Class in de.lmu.ifi.dbs.elki.parallel.processor
-
Compute the kNN distance for each object.
- KDistanceProcessor(int) - Constructor for class de.lmu.ifi.dbs.elki.parallel.processor.KDistanceProcessor
-
Constructor.
- KDistanceProcessor.Instance - Class in de.lmu.ifi.dbs.elki.parallel.processor
-
Instance for precomputing the kNN.
- KDistanceProcessor.Instance(int, SharedObject.Instance<? extends KNNList>, SharedDouble.Instance) - Constructor for class de.lmu.ifi.dbs.elki.parallel.processor.KDistanceProcessor.Instance
-
Constructor.
- kdists - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.parallel.LRDProcessor
-
k-distance store
- kdKNNSearch(int, int, int, O, KNNHeap, DBIDArrayIter, double) - Method in class de.lmu.ifi.dbs.elki.index.tree.spatial.kd.MinimalisticMemoryKDTree.KDTreeKNNQuery
-
Perform a kNN search on the kd-tree.
- kdKNNSearch(int, int, int, O, KNNHeap, DoubleDBIDListIter, double) - Method in class de.lmu.ifi.dbs.elki.index.tree.spatial.kd.SmallMemoryKDTree.KDTreeKNNQuery
-
Perform a kNN search on the kd-tree.
- kdRangeSearch(int, int, int, O, ModifiableDoubleDBIDList, DBIDArrayIter, double) - Method in class de.lmu.ifi.dbs.elki.index.tree.spatial.kd.MinimalisticMemoryKDTree.KDTreeRangeQuery
-
Perform a kNN search on the kd-tree.
- kdRangeSearch(int, int, int, O, ModifiableDoubleDBIDList, DoubleDBIDListIter, double) - Method in class de.lmu.ifi.dbs.elki.index.tree.spatial.kd.SmallMemoryKDTree.KDTreeRangeQuery
-
Perform a kNN search on the kd-tree.
- keep - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain.RepresentativeUncertainClustering
-
Keep all samples (not only the representative results)
- keep - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain.RepresentativeUncertainClustering.Parameterizer
-
Keep all samples (not only the representative results).
- keep - Variable in class de.lmu.ifi.dbs.elki.datasource.filter.typeconversions.UncertainifyFilter
-
Flag to keep the original data.
- keep - Variable in class de.lmu.ifi.dbs.elki.datasource.filter.typeconversions.UncertainifyFilter.Parameterizer
-
Flag to keep the original data.
- KEEP_ID - Static variable in class de.lmu.ifi.dbs.elki.datasource.filter.typeconversions.UncertainifyFilter.Parameterizer
-
Flag to keep the original data.
- KEEP_SAMPLES_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain.RepresentativeUncertainClustering.Parameterizer
-
Flag to keep all samples.
- keepfirst - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization.FarthestPointsInitialMeans.Parameterizer
-
Flag for discarding the first object chosen.
- keepfirst - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization.FarthestSumPointsInitialMeans.Parameterizer
-
Flag for discarding the first object chosen.
- KEEPFIRST_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization.FarthestPointsInitialMeans.Parameterizer
-
Option ID to control the handling of the first object chosen.
- keepsteep - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.optics.OPTICSXi
-
Keep the steep areas, for visualization.
- keepsteep - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.optics.OPTICSXi.Parameterizer
-
- KEEPSTEEP_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.optics.OPTICSXi.Parameterizer
-
Parameter to keep the steep areas
- kernel - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.NaiveMeanShiftClustering
-
Density estimation kernel.
- kernel - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.NaiveMeanShiftClustering.Parameterizer
-
Kernel function.
- kernel - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.onedimensional.KNNKernelDensityMinimaClustering
-
Kernel density function.
- kernel - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.onedimensional.KNNKernelDensityMinimaClustering.Parameterizer
-
Kernel density function.
- kernel - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.KDEOS
-
Kernel function to use for density estimation.
- kernel - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.KDEOS.Parameterizer
-
Kernel function to use for density estimation.
- kernel - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LDF
-
Kernel density function
- kernel - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LDF.Parameterizer
-
Kernel density function parameter
- kernel - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.SimpleKernelDensityLOF
-
Kernel density function
- kernel - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.SimpleKernelDensityLOF.Parameterizer
-
Kernel density function parameter
- kernel - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.subspace.OUTRES.KernelDensityEstimator
-
Actual kernel in use
- kernel - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.svm.LibSVMOneClassOutlierDetection
-
Kernel function in use.
- kernel - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.svm.LibSVMOneClassOutlierDetection.Parameterizer
-
Kernel in use.
- kernel - Variable in class de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel.KernelMatrix
-
The kernel matrix
- KERNEL - Static variable in class de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions.BiweightKernelDensityFunction
-
Static instance.
- KERNEL - Static variable in class de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions.CosineKernelDensityFunction
-
Static instance.
- KERNEL - Static variable in class de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions.EpanechnikovKernelDensityFunction
-
Static instance.
- KERNEL - Static variable in class de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions.GaussianKernelDensityFunction
-
Static instance.
- KERNEL - Static variable in class de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions.TriangularKernelDensityFunction
-
Static instance.
- KERNEL - Static variable in class de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions.TricubeKernelDensityFunction
-
Static instance.
- KERNEL - Static variable in class de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions.TriweightKernelDensityFunction
-
Static instance.
- KERNEL - Static variable in class de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions.UniformKernelDensityFunction
-
Static instance.
- KERNEL_FUNCTION_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.anglebased.ABOD.Parameterizer
-
Parameter for the kernel function.
- KERNEL_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.NaiveMeanShiftClustering.Parameterizer
-
Parameter for kernel function.
- KERNEL_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.onedimensional.KNNKernelDensityMinimaClustering.Parameterizer
-
Kernel function.
- KERNEL_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.KDEOS.Parameterizer
-
Parameter to specify the kernel density function.
- KERNEL_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LDF.Parameterizer
-
Option ID for kernel.
- KERNEL_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.SimpleKernelDensityLOF.Parameterizer
-
Option ID for kernel density LOF kernel.
- KERNEL_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.svm.LibSVMOneClassOutlierDetection.Parameterizer
-
Parameter for kernel function.
- KERNEL_MIN_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.KDEOS.Parameterizer
-
Parameter to specify the minimum bandwidth.
- KERNEL_SCALE_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.KDEOS.Parameterizer
-
Parameter to specify the kernel scaling factor.
- KernelDensityEstimator - Class in de.lmu.ifi.dbs.elki.math.statistics
-
Estimate density given an array of points.
- KernelDensityEstimator(double[], double, double, KernelDensityFunction, int, double) - Constructor for class de.lmu.ifi.dbs.elki.math.statistics.KernelDensityEstimator
-
Initialize and execute kernel density estimation.
- KernelDensityEstimator(double[], KernelDensityFunction, double) - Constructor for class de.lmu.ifi.dbs.elki.math.statistics.KernelDensityEstimator
-
Process an array of data
- KernelDensityFunction - Interface in de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions
-
Inner function of a kernel density estimator.
- kernelFunction - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.anglebased.ABOD
-
Store the configured Kernel version.
- kernelFunction - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.anglebased.ABOD.Parameterizer
-
Distance function.
- KernelMatrix - Class in de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel
-
Kernel matrix representation.
- KernelMatrix(PrimitiveSimilarityFunction<? super O>, Relation<? extends O>, DBIDs) - Constructor for class de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel.KernelMatrix
-
Provides a new kernel matrix.
- KernelMatrix(SimilarityQuery<? super O>, Relation<? extends O>, DBIDs) - Constructor for class de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel.KernelMatrix
-
Provides a new kernel matrix.
- KernelMatrix(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 - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansBatchedLloyd
-
Key for statistics logging.
- KEY - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansElkan
-
Key for statistics logging.
- KEY - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansHamerly
-
Key for statistics logging.
- KEY - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansHybridLloydMacQueen
-
Key for statistics logging.
- KEY - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansLloyd
-
Key for statistics logging.
- KEY - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansMacQueen
-
Key for statistics logging.
- KEY - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMediansLloyd
-
Key for statistics logging.
- KEY - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMedoidsEM
-
Key for statistics logging.
- KEY - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMedoidsPAM
-
Key for statistics logging.
- KEY - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.SingleAssignmentKMeans
-
Key for statistics logging.
- KEY - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.XMeans
-
Key for statistics logging.
- KEY - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain.UKMeans
-
Key for statistics logging.
- key - Variable in class de.lmu.ifi.dbs.elki.algorithm.itemsetmining.FPGrowth.FPNode
-
Key, weight, and number of children.
- key - Variable in class de.lmu.ifi.dbs.elki.evaluation.clustering.internal.EvaluateCIndex
-
Key for logging statistics.
- key - Variable in class de.lmu.ifi.dbs.elki.evaluation.clustering.internal.EvaluateConcordantPairs
-
Key for logging statistics.
- key - Variable in class de.lmu.ifi.dbs.elki.evaluation.clustering.internal.EvaluateDaviesBouldin
-
Key for logging statistics.
- key - Variable in class de.lmu.ifi.dbs.elki.evaluation.clustering.internal.EvaluatePBMIndex
-
Key for logging statistics.
- key - Variable in class de.lmu.ifi.dbs.elki.evaluation.clustering.internal.EvaluateSilhouette
-
Key for logging statistics.
- key - Variable in class de.lmu.ifi.dbs.elki.evaluation.clustering.internal.EvaluateSimplifiedSilhouette
-
Key for logging statistics.
- key - Variable in class de.lmu.ifi.dbs.elki.evaluation.clustering.internal.EvaluateSquaredErrors
-
Key for logging statistics.
- key - Variable in class de.lmu.ifi.dbs.elki.evaluation.clustering.internal.EvaluateVarianceRatioCriteria
-
Key for logging statistics.
- key - Variable in class de.lmu.ifi.dbs.elki.evaluation.outlier.OutlierRankingEvaluation
-
Key prefix for statistics logging.
- key - Variable in class de.lmu.ifi.dbs.elki.logging.statistics.AbstractStatistic
-
Key to report the statistic with.
- key(PlotItem, VisualizationTask) - Method in class de.lmu.ifi.dbs.elki.visualization.gui.overview.LayerMap
-
Helper function for building a key object
- KEY - Static variable in interface de.lmu.ifi.dbs.elki.visualization.style.StyleLibrary
-
Key
- KEY_CAPTION - Static variable in class de.lmu.ifi.dbs.elki.visualization.visualizers.visunproj.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.SimpleTransactionParser
-
Map.
- keymap - Variable in class de.lmu.ifi.dbs.elki.datasource.parser.TermFrequencyParser
-
Map.
- keyPressed(KeyEvent) - Method in class de.lmu.ifi.dbs.elki.gui.util.ParameterTable.ClassListEditor
-
- keyPressed(KeyEvent) - Method in class de.lmu.ifi.dbs.elki.gui.util.ParameterTable.DropdownEditor
-
- keyPressed(KeyEvent) - Method in class de.lmu.ifi.dbs.elki.gui.util.ParameterTable.FileNameEditor
-
- keyPressed(KeyEvent) - Method in class de.lmu.ifi.dbs.elki.gui.util.ParameterTable.Handler
-
- keyPressed(KeyEvent) - Method in class de.lmu.ifi.dbs.elki.gui.util.TreePopup.Handler
-
- keyReleased(KeyEvent) - Method in class de.lmu.ifi.dbs.elki.gui.util.ParameterTable.ClassListEditor
-
- keyReleased(KeyEvent) - Method in class de.lmu.ifi.dbs.elki.gui.util.ParameterTable.DropdownEditor
-
- keyReleased(KeyEvent) - Method in class de.lmu.ifi.dbs.elki.gui.util.ParameterTable.FileNameEditor
-
- keyReleased(KeyEvent) - Method in class de.lmu.ifi.dbs.elki.gui.util.ParameterTable.Handler
-
- keyReleased(KeyEvent) - Method in class de.lmu.ifi.dbs.elki.gui.util.TreePopup.Handler
-
- keys - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LOCI.DoubleIntArrayList
-
Double keys
- keySet() - Method in class de.lmu.ifi.dbs.elki.visualization.gui.overview.RectangleArranger
-
The item keys contained in the map.
- keyTyped(KeyEvent) - Method in class de.lmu.ifi.dbs.elki.gui.util.ParameterTable.ClassListEditor
-
- keyTyped(KeyEvent) - Method in class de.lmu.ifi.dbs.elki.gui.util.ParameterTable.DropdownEditor
-
- keyTyped(KeyEvent) - Method in class de.lmu.ifi.dbs.elki.gui.util.ParameterTable.FileNameEditor
-
- keyTyped(KeyEvent) - Method in class de.lmu.ifi.dbs.elki.gui.util.ParameterTable.Handler
-
- keyTyped(KeyEvent) - Method in class de.lmu.ifi.dbs.elki.gui.util.TreePopup.Handler
-
- KeyVisualization - Class in de.lmu.ifi.dbs.elki.visualization.visualizers.visunproj
-
Visualizer, displaying the key for a clustering.
- KeyVisualization() - Constructor for class de.lmu.ifi.dbs.elki.visualization.visualizers.visunproj.KeyVisualization
-
- KeyVisualization.Instance - Class in de.lmu.ifi.dbs.elki.visualization.visualizers.visunproj
-
Instance
- KeyVisualization.Instance(VisualizationTask, VisualizationPlot, double, double) - Constructor for class de.lmu.ifi.dbs.elki.visualization.visualizers.visunproj.KeyVisualization.Instance
-
Constructor.
- kmax - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.KDEOS
-
Minimum and maximum number of neighbors to use.
- kmax - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.KDEOS.Parameterizer
-
Minimum and maximum number of neighbors to use.
- KMAX_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.KDEOS.Parameterizer
-
Maximum value of k to analyze.
- KMeans<V extends NumberVector,M extends Model> - Interface in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans
-
Some constants and options shared among kmeans family algorithms.
- kmeans - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain.CKMeans.Parameterizer
-
K-means instance to use.
- KMEANS_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.BestOfMultipleKMeans.Parameterizer
-
Parameter to specify the kMeans variant.
- KMEANS_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization.SampleKMeansInitialization.Parameterizer
-
Parameter to specify the kMeans variant.
- KMEANS_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansBisecting.Parameterizer
-
Parameter to specify the kMeans variant.
- KMeansBatchedLloyd<V extends NumberVector> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans
-
An algorithm for k-means, using Lloyd-style bulk iterations.
- KMeansBatchedLloyd(NumberVectorDistanceFunction<? super V>, int, int, KMeansInitialization<? super V>, int, RandomFactory) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansBatchedLloyd
-
Constructor.
- KMeansBatchedLloyd.Parameterizer<V extends NumberVector> - 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,M extends MeanModel> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans
-
The bisecting k-means algorithm works by starting with an initial
partitioning into two clusters, then repeated splitting of the largest
cluster to get additional clusters.
- KMeansBisecting(int, KMeans<V, M>) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansBisecting
-
Constructor.
- KMeansBisecting.Parameterizer<V extends NumberVector,M extends MeanModel> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans
-
Parameterization class.
- 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.
- KMeansElkan<V extends NumberVector> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans
-
Elkan's fast k-means by exploiting the triangle inequality.
- KMeansElkan(NumberVectorDistanceFunction<? super V>, int, int, KMeansInitialization<? super V>) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansElkan
-
Constructor.
- KMeansElkan.Parameterizer<V extends NumberVector> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans
-
Parameterization class.
- KMeansElkan.Parameterizer() - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansElkan.Parameterizer
-
- KMeansHamerly<V extends NumberVector> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans
-
Hamerly's fast k-means by exploiting the triangle inequality.
- KMeansHamerly(NumberVectorDistanceFunction<? super V>, int, int, KMeansInitialization<? super V>) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansHamerly
-
Constructor.
- KMeansHamerly.Parameterizer<V extends NumberVector> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans
-
Parameterization class.
- KMeansHamerly.Parameterizer() - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansHamerly.Parameterizer
-
- KMeansHybridLloydMacQueen<V extends NumberVector> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans
-
A hybrid k-means algorithm, alternating between MacQueen-style incremental
processing and Lloyd-Style batch steps.
- KMeansHybridLloydMacQueen(NumberVectorDistanceFunction<? super V>, int, int, KMeansInitialization<? super V>) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansHybridLloydMacQueen
-
Constructor.
- KMeansHybridLloydMacQueen.Parameterizer<V extends NumberVector> - 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 extends NumberVector> - Interface in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization
-
Interface for initializing K-Means
- KMeansLloyd<V extends NumberVector> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans
-
The standard k-means algorithm, using Lloyd-style bulk iterations.
- KMeansLloyd(NumberVectorDistanceFunction<? super V>, int, int, KMeansInitialization<? super V>) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansLloyd
-
Constructor.
- KMeansLloyd.Parameterizer<V extends NumberVector> - 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> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans
-
The original k-means algorithm, using MacQueen style incremental updates;
making this effectively an "online" (streaming) algorithm.
- KMeansMacQueen(NumberVectorDistanceFunction<? super V>, int, int, KMeansInitialization<? super V>) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansMacQueen
-
Constructor.
- KMeansMacQueen.Parameterizer<V extends NumberVector> - 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 - 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(Vector, double) - Constructor for class de.lmu.ifi.dbs.elki.data.model.KMeansModel
-
Constructor with mean.
- KMeansOutlierDetection<O extends NumberVector> - Class in de.lmu.ifi.dbs.elki.algorithm.outlier.clustering
-
Outlier detection by using k-means clustering.
- KMeansOutlierDetection(KMeans<O, ?>) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.outlier.clustering.KMeansOutlierDetection
-
Constructor.
- KMeansOutlierDetection.Parameterizer<O extends NumberVector> - Class in de.lmu.ifi.dbs.elki.algorithm.outlier.clustering
-
Parameterizer.
- KMeansOutlierDetection.Parameterizer() - Constructor for class de.lmu.ifi.dbs.elki.algorithm.outlier.clustering.KMeansOutlierDetection.Parameterizer
-
- KMeansPlusPlusInitialMeans<O> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization
-
K-Means++ initialization for k-means.
- KMeansPlusPlusInitialMeans(RandomFactory) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization.KMeansPlusPlusInitialMeans
-
Constructor.
- KMeansPlusPlusInitialMeans.Parameterizer<V> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization
-
Parameterization class.
- KMeansPlusPlusInitialMeans.Parameterizer() - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization.KMeansPlusPlusInitialMeans.Parameterizer
-
- KMeansProcessor<V extends NumberVector> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.parallel
-
Parallel k-means implementation.
- KMeansProcessor(Relation<V>, NumberVectorDistanceFunction<? super V>, WritableIntegerDataStore, double[]) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.parallel.KMeansProcessor
-
Constructor.
- KMeansProcessor.Instance<V extends NumberVector> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.parallel
-
Instance to process part of the data set, for a single iteration.
- KMeansProcessor.Instance(Relation<V>, NumberVectorDistanceFunction<? super V>, WritableIntegerDataStore, List<? extends NumberVector>) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.parallel.KMeansProcessor.Instance
-
Constructor.
- KMeansQualityMeasure<O extends NumberVector> - Interface in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.quality
-
Interface for computing the quality of a K-Means clustering.
- kMeansVariant - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.BestOfMultipleKMeans.Parameterizer
-
Variant of kMeans to use.
- kMeansVariant - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansBisecting.Parameterizer
-
Variant of kMeans
- KMediansLloyd<V extends NumberVector> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans
-
k-medians clustering algorithm, but using Lloyd-style bulk iterations instead
of the more complicated approach suggested by Kaufman and Rousseeuw (see
KMedoidsPAM
instead).
- KMediansLloyd(NumberVectorDistanceFunction<? super V>, int, int, KMeansInitialization<? super V>) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMediansLloyd
-
Constructor.
- KMediansLloyd.Parameterizer<V extends NumberVector> - 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> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans
-
A k-medoids clustering algorithm, implemented as EM-style bulk algorithm.
- KMedoidsEM(DistanceFunction<? super V>, int, int, KMedoidsInitialization<V>) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMedoidsEM
-
Constructor.
- KMedoidsEM.Parameterizer<V> - 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.initialization
-
Interface for initializing K-Medoids.
- KMedoidsPAM<V> - Class in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans
-
The original PAM algorithm or k-medoids clustering, as proposed by Kaufman
and Rousseeuw in "Partitioning Around Medoids".
- KMedoidsPAM(DistanceFunction<? super V>, int, int, KMedoidsInitialization<V>) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMedoidsPAM
-
Constructor.
- KMedoidsPAM.Parameterizer<V> - 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
-
- kmin - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.KDEOS
-
Minimum and maximum number of neighbors to use.
- kmin - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.KDEOS.Parameterizer
-
Minimum and maximum number of neighbors to use.
- KMIN_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.KDEOS.Parameterizer
-
Minimum value of k to analyze.
- KMLOutputHandler - Class in de.lmu.ifi.dbs.elki.result
-
Class to handle KML output.
- KMLOutputHandler(File, 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> - Class in de.lmu.ifi.dbs.elki.algorithm.benchmark
-
Benchmarking algorithm that computes the k nearest neighbors for each query
point.
- KNNBenchmarkAlgorithm(DistanceFunction<? super O>, int, DatabaseConnection, double, RandomFactory) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.benchmark.KNNBenchmarkAlgorithm
-
Constructor.
- KNNBenchmarkAlgorithm.Parameterizer<O> - Class in de.lmu.ifi.dbs.elki.algorithm.benchmark
-
Parameterization class
- 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
-
- KNNClassifier<O> - Class in de.lmu.ifi.dbs.elki.algorithm.classification
-
KNNClassifier classifies instances based on the class distribution among the
k nearest neighbors in a database.
- KNNClassifier(DistanceFunction<? super O>, int) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.classification.KNNClassifier
-
Constructor.
- KNNClassifier.Parameterizer<O> - Class in de.lmu.ifi.dbs.elki.algorithm.classification
-
Parameterization class
- KNNClassifier.Parameterizer() - Constructor for class de.lmu.ifi.dbs.elki.algorithm.classification.KNNClassifier.Parameterizer
-
- KNNDIST - Static variable in class de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.selection.DistanceFunctionVisualization.Instance
-
- knnDistance - Variable in class de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkmax.MkMaxDirectoryEntry
-
The aggregated k-nearest neighbor distance of the underlying MkMax-Tree
node.
- knnDistance - Variable in class de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkmax.MkMaxLeafEntry
-
The k-nearest neighbor distance of the underlying data object.
- kNNDistance() - Method in class de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkmax.MkMaxTreeNode
-
Determines and returns the k-nearest neighbor distance of this node as the
maximum of the k-nearest neighbor distances of all entries.
- knnDistance - Variable in class de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rdknn.RdKNNDirectoryEntry
-
The aggregated knn distance of this entry.
- knnDistance - Variable in class de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rdknn.RdKNNLeafEntry
-
The knn distance of the underlying data object.
- kNNDistance() - Method in class de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rdknn.RdKNNNode
-
Computes and returns the aggregated knn distance of this node
- kNNdistanceAdjustment(E, Map<DBID, KNNList>) - Method in class de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.AbstractMkTreeUnified
-
Performs a distance adjustment in the subtree of the specified root entry.
- kNNdistanceAdjustment(MkMaxEntry, Map<DBID, KNNList>) - Method in class de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkmax.MkMaxTree
-
Adjusts the knn distance in the subtree of the specified root entry.
- kNNdistanceAdjustment(MkTabEntry, Map<DBID, KNNList>) - Method in class de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mktab.MkTabTree
-
- knnDistanceApproximation() - Method in class de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkapp.MkAppTreeNode
-
Determines and returns the polynomial approximation for the knn distances
of this node as the maximum of the polynomial approximations of all
entries.
- knnDistances - Variable in class de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mktab.MkTabDirectoryEntry
-
The aggregated knn distances of the underlying node.
- knnDistances - Variable in class de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mktab.MkTabLeafEntry
-
The knn distances of the underlying data object.
- knnDistances(O) - Method in class de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mktab.MkTabTreeIndex
-
Returns the knn distance of the object with the specified id.
- kNNDistances() - Method in class de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mktab.MkTabTreeNode
-
Determines and returns the knn distance of this node as the maximum knn
distance of all entries.
- KNNDistancesSampler<O> - Class in de.lmu.ifi.dbs.elki.algorithm
-
Provides an order of the kNN-distances for all objects within the database.
- KNNDistancesSampler(DistanceFunction<? super O>, int, double, RandomFactory) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.KNNDistancesSampler
-
Constructor.
- KNNDistancesSampler.KNNDistanceOrderResult - Class in de.lmu.ifi.dbs.elki.algorithm
-
Curve result for a list containing the knn distances.
- KNNDistancesSampler.KNNDistanceOrderResult(double[], int) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.KNNDistancesSampler.KNNDistanceOrderResult
-
Construct result
- KNNDistancesSampler.Parameterizer<O> - Class in de.lmu.ifi.dbs.elki.algorithm
-
Parameterization class.
- KNNDistancesSampler.Parameterizer() - Constructor for class de.lmu.ifi.dbs.elki.algorithm.KNNDistancesSampler.Parameterizer
-
Constructor.
- KNNHeap - Interface in de.lmu.ifi.dbs.elki.database.ids
-
Interface for kNN heaps.
- KNNIndex<O> - Interface in de.lmu.ifi.dbs.elki.index
-
Index with support for kNN queries.
- knnJoin - Variable in class de.lmu.ifi.dbs.elki.algorithm.clustering.optics.DeLiClu
-
Holds the knnJoin algorithm.
- KNNJoin<V extends NumberVector,N extends SpatialNode<N,E>,E extends SpatialEntry> - Class in de.lmu.ifi.dbs.elki.algorithm
-
Joins in a given spatial database to each object its k-nearest neighbors.
- KNNJoin(DistanceFunction<? super V>, int) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.KNNJoin
-
Constructor.
- KNNJoin.Parameterizer<V extends NumberVector,N extends SpatialNode<N,E>,E extends SpatialEntry> - Class in de.lmu.ifi.dbs.elki.algorithm
-
Parameterization class.
- KNNJoin.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(double, int, int) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.KNNJoin.Task
-
Constructor.
- KNNJoinMaterializeKNNPreprocessor<V extends NumberVector> - Class in de.lmu.ifi.dbs.elki.index.preprocessed.knn
-
Class to materialize the kNN using a spatial join on an R-tree.
- KNNJoinMaterializeKNNPreprocessor(Relation<V>, DistanceFunction<? super V>, int) - Constructor for class de.lmu.ifi.dbs.elki.index.preprocessed.knn.KNNJoinMaterializeKNNPreprocessor
-
Constructor.
- KNNJoinMaterializeKNNPreprocessor.Factory<O extends NumberVector> - Class in de.lmu.ifi.dbs.elki.index.preprocessed.knn
-
The parameterizable factory.
- KNNJoinMaterializeKNNPreprocessor.Factory(int, DistanceFunction<? super O>) - Constructor for class de.lmu.ifi.dbs.elki.index.preprocessed.knn.KNNJoinMaterializeKNNPreprocessor.Factory
-
Constructor.
- KNNJoinMaterializeKNNPreprocessor.Factory.Parameterizer<O extends NumberVector> - 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 - Interface in de.lmu.ifi.dbs.elki.database.ids
-
Interface for kNN results.
- KNNListener - Interface in de.lmu.ifi.dbs.elki.index.preprocessed.knn
-
Listener interface invoked when the k nearest neighbors (kNNs) of some
objects have been changed due to insertion or removals of objects.
- KNNMARKER - Static variable in class de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.selection.DistanceFunctionVisualization.Instance
-
Generic tags to indicate the type of element.
- KNNOutlier<O> - Class in de.lmu.ifi.dbs.elki.algorithm.outlier.distance
-
Outlier Detection based on the distance of an object to its k nearest
neighbor.
- KNNOutlier(DistanceFunction<? super O>, int) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.KNNOutlier
-
Constructor for a single kNN query.
- KNNOutlier.Parameterizer<O> - Class in de.lmu.ifi.dbs.elki.algorithm.outlier.distance
-
Parameterization class.
- KNNOutlier.Parameterizer() - Constructor for class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.KNNOutlier.Parameterizer
-
- knnperf - Variable in class de.lmu.ifi.dbs.elki.algorithm.statistics.EvaluateRetrievalPerformance.RetrievalPerformanceResult
-
KNN performance
- KNNProcessor<O> - Class in de.lmu.ifi.dbs.elki.parallel.processor
-
Processor to compute the kNN of each object.
- KNNProcessor(int, KNNQuery<O>) - Constructor for class de.lmu.ifi.dbs.elki.parallel.processor.KNNProcessor
-
Constructor.
- KNNProcessor.Instance<O> - Class in de.lmu.ifi.dbs.elki.parallel.processor
-
Instance for precomputing the kNN.
- KNNProcessor.Instance(int, KNNQuery<O>, SharedObject.Instance<KNNList>) - Constructor for class de.lmu.ifi.dbs.elki.parallel.processor.KNNProcessor.Instance
-
Constructor.
- knnq - Variable in class de.lmu.ifi.dbs.elki.algorithm.classification.KNNClassifier
-
kNN query class.
- knnq - Variable in class de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.AbstractMkTree
-
Internal class for performing knn queries
- knnq - Variable in class de.lmu.ifi.dbs.elki.parallel.processor.KNNProcessor.Instance
-
kNN query
- knnq - Variable in class de.lmu.ifi.dbs.elki.parallel.processor.KNNProcessor
-
KNN query object
- knnQueries - Variable in class de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.AbstractMTree.Statistics
-
For counting the number of knn queries answered.
- knnQueries - Variable in class de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.AbstractRStarTree.Statistics
-
For counting the number of knn queries answered.
- KNNQuery<O> - Interface in de.lmu.ifi.dbs.elki.database.query.knn
-
The interface of an actual instance.
- knnQuery - Variable in class de.lmu.ifi.dbs.elki.database.query.rknn.LinearScanRKNNQuery
-
KNN query we use.
- knnQuery - Variable in class de.lmu.ifi.dbs.elki.index.preprocessed.knn.MaterializeKNNPreprocessor
-
KNNQuery instance to use.
- knnQuery - Variable in class de.lmu.ifi.dbs.elki.index.preprocessed.localpca.KNNQueryFilteredPCAIndex
-
The kNN query instance we use.
- knnQuery - Variable in class de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rdknn.RdKNNTree
-
Internal knn query object, for updating the rKNN.
- KNNQueryFilteredPCAIndex<NV extends NumberVector> - Class in de.lmu.ifi.dbs.elki.index.preprocessed.localpca
-
Provides the local neighborhood to be considered in the PCA as the k nearest
neighbors of an object.
- KNNQueryFilteredPCAIndex(Relation<NV>, PCAFilteredRunner, KNNQuery<NV>, int) - Constructor for class de.lmu.ifi.dbs.elki.index.preprocessed.localpca.KNNQueryFilteredPCAIndex
-
Constructor.
- KNNQueryFilteredPCAIndex.Factory<V extends NumberVector> - Class in de.lmu.ifi.dbs.elki.index.preprocessed.localpca
-
Factory class.
- KNNQueryFilteredPCAIndex.Factory(DistanceFunction<V>, PCAFilteredRunner, int) - 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.
- knns - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.parallel.LOFProcessor
-
KNN store
- knns - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.parallel.LRDProcessor
-
KNN store
- knns - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.parallel.SimplifiedLRDProcessor
-
KNN store
- kNNsChanged(KNNChangeEvent) - Method in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.OnlineLOF.LOFKNNListener
-
- kNNsChanged(KNNChangeEvent, KNNChangeEvent) - Method in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.OnlineLOF.LOFKNNListener
-
Invoked after the events of both preprocessors have been received, i.e.
- kNNsChanged(KNNChangeEvent) - Method in interface de.lmu.ifi.dbs.elki.index.preprocessed.knn.KNNListener
-
Invoked after kNNs have been updated, inserted or removed
in some way.
- kNNsInserted(DBIDs, DBIDs, DBIDs, FlexibleLOF.LOFResult<O>) - Method in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.OnlineLOF.LOFKNNListener
-
Invoked after kNNs have been inserted and updated, updates the result.
- kNNsRemoved(DBIDs, DBIDs, DBIDs, FlexibleLOF.LOFResult<O>) - Method in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.OnlineLOF.LOFKNNListener
-
Invoked after kNNs have been removed and updated, updates the result.
- KNNSubList - Class in de.lmu.ifi.dbs.elki.database.ids.generic
-
Sublist of an existing result to contain only the first k elements.
- KNNSubList(KNNList, 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> - Class in de.lmu.ifi.dbs.elki.algorithm.outlier.distance
-
Outlier Detection based on the accumulated distances of a point to its k
nearest neighbors.
- KNNWeightOutlier(DistanceFunction<? super O>, int) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.KNNWeightOutlier
-
Constructor with parameters.
- KNNWeightOutlier.Parameterizer<O> - Class in de.lmu.ifi.dbs.elki.algorithm.outlier.distance
-
Parameterization class.
- KNNWeightOutlier.Parameterizer() - Constructor for class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.KNNWeightOutlier.Parameterizer
-
- KNNWeightProcessor - Class in de.lmu.ifi.dbs.elki.algorithm.outlier.distance.parallel
-
- KNNWeightProcessor(int) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.parallel.KNNWeightProcessor
-
Constructor.
- KNNWeightProcessor.Instance - Class in de.lmu.ifi.dbs.elki.algorithm.outlier.distance.parallel
-
Instance for precomputing the kNN.
- KNNWeightProcessor.Instance(int, SharedObject.Instance<? extends KNNList>, SharedDouble.Instance) - Constructor for class de.lmu.ifi.dbs.elki.algorithm.outlier.distance.parallel.KNNWeightProcessor.Instance
-
Constructor.
- knownParameterizables - Variable in class de.lmu.ifi.dbs.elki.application.internal.CheckParameterizables
-
Known parameterizable classes/interfaces.
- KolmogorovSmirnovDistanceFunction - Class in de.lmu.ifi.dbs.elki.distance.distancefunction.histogram
-
Distance function based on the Kolmogorov-Smirnov goodness of fit test.
- KolmogorovSmirnovDistanceFunction() - Constructor for class de.lmu.ifi.dbs.elki.distance.distancefunction.histogram.KolmogorovSmirnovDistanceFunction
-
Deprecated.
Use static instance!
- KolmogorovSmirnovDistanceFunction.Parameterizer - Class in de.lmu.ifi.dbs.elki.distance.distancefunction.histogram
-
Parameterization class, using the static instance.
- 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
-
- KR_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.intrinsic.IDOS.Parameterizer
-
Parameter to specify the neighborhood size to use for the averaging.
- krate - Variable in class de.lmu.ifi.dbs.elki.algorithm.statistics.EstimateIntrinsicDimensionality
-
Number of neighbors to use.
- krate - Variable in class de.lmu.ifi.dbs.elki.algorithm.statistics.EstimateIntrinsicDimensionality.Parameterizer
-
Number of neighbors to use.
- KRATE_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.statistics.EstimateIntrinsicDimensionality.Parameterizer
-
Number of kNN to use for each object.
- kreach - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.FlexibleLOF
-
Number of neighbors used for reachability distance.
- kreach - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.FlexibleLOF.Parameterizer
-
The set size to use for reachability distance.
- kreach - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LoOP
-
Reachability neighborhood size.
- kreach - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LoOP.Parameterizer
-
- KREACH_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.FlexibleLOF.Parameterizer
-
Parameter to specify the number of nearest neighbors of an object to be
considered for computing its reachability distance.
- KREACH_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LoOP.Parameterizer
-
Parameter to specify the number of nearest neighbors of an object to be
considered for computing its LOOP_SCORE, must be an integer greater than
1.
- KREF_ID - Static variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.FlexibleLOF.Parameterizer
-
Parameter to specify the number of nearest neighbors of an object to be
considered for computing its LOF score, must be an integer greater or
equal to 1.
- krefer - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.FlexibleLOF
-
Number of neighbors in comparison set.
- krefer - Variable in class de.lmu.ifi.dbs.elki.algorithm.outlier.lof.FlexibleLOF.Parameterizer
-
The reference set size to use.
- 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
-
- 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
-
- 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
-
- 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
-