Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.NumberVectorDistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Threshold for output accuracy fraction digits.
Default: 4
Threshold for the size of the random sample to use. Default value is size of the complete dataset.
Flag to use random sample (use knn query around centroid, if flag is not set).
Default: false
The class to compute (filtered) PCA.
Class Restriction: extends math.linearalgebra.pca.PCARunner
Default: PCARunner
Known implementations:
Filter class to determine the strong and weak eigenvectors.
Class Restriction: implements math.linearalgebra.pca.filter.EigenPairFilter
Default: PercentageEigenPairFilter
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Specifies the distance of the k-distant object to be assessed, ignoring the query object.
The percentage of objects to use for sampling, or the absolute number of samples.
Default: 1.0
Random generator seed for sampling.
Default: use global random seed
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Specifies the k-nearest neighbors to be assigned.
Default: 1
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Number of neighbors to retreive for kNN benchmarking.
Data source for the queries. If not set, the queries are taken from the database.
Class Restriction: implements datasource.DatabaseConnection
Known implementations:
Sampling size parameter. If the value is less or equal 1, it is assumed to be the relative share. Larger values will be interpreted as integer sizes. By default, all data will be used.
Random generator for sampling.
Default: use global random seed
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Data source for the queries. If not set, the queries are taken from the database.
Class Restriction: implements datasource.DatabaseConnection
Known implementations:
Sampling size parameter. If the value is less or equal 1, it is assumed to be the relative share. Larger values will be interpreted as integer sizes. By default, all data will be used.
Random generator for sampling.
Default: use global random seed
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Number of neighbors to retreive for kNN benchmarking.
Pattern to select query points.
Data source for the queries. If not set, the queries are taken from the database.
Class Restriction: implements datasource.DatabaseConnection
Known implementations:
Sampling size parameter. If the value is less or equal 1, it is assumed to be the relative share. Larger values will be interpreted as integer sizes. By default, all data will be used.
Force the use of linear scanning as reference.
Default: false
Random generator for sampling.
Default: use global random seed
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
The number of neighbors to take into account for classification.
Default: 1
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Inclusion threshold for canopy clustering. t1 >= t2!
Removal threshold for canopy clustering. t1 >= t2!
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
The maximum radius of the neighborhood to be considered.
Threshold for minimum number of points in the epsilon-neighborhood of a point. The suggested value is '2 * dim - 1'.
Distance function to determine the distance between database objects.
Class Restriction: extends distance.distancefunction.minkowski.LPNormDistanceFunction
Default: EuclideanDistanceFunction
Known implementations:
The maximum radius of the neighborhood to be considered.
Threshold for minimum number of points in the epsilon-neighborhood of a point. The suggested value is '2 * dim - 1'.
Width of the grid used, must be at least two times epsilon.
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Maximum distance from leading object.
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Kernel function to use with mean-shift clustering.
Class Restriction: implements math.statistics.kernelfunctions.KernelDensityFunction
Default: EpanechnikovKernelDensityFunction
Known implementations:
Range of the kernel to use (aka: radius, bandwidth).
The minimum SNN density.
Threshold for minimum number of points in the epsilon-SNN-neighborhood of a point.
Similarity matrix initialization..
Class Restriction: implements algorithm.clustering.affinitypropagation.AffinityPropagationInitialization
Default: DistanceBasedInitializationWithMedian
Known implementations:
Dampening factor lambda. Usually 0.5 to 1.
Default: 0.5
Number of stable iterations for convergence.
Default: 15
Maximum number of iterations.
Default: 1000
Distance function to use.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.SquaredEuclideanDistanceFunction
Known implementations:
Quantile to use for diagonal entries.
Default: 0.5
Similarity function to use.
Class Restriction: implements distance.similarityfunction.SimilarityFunction
Default: kernel.LinearKernelFunction
Known implementations:
Quantile to use for diagonal entries.
Default: 0.5
Threshold value to determine the maximal acceptable score (mean squared residue) of a bicluster.
The number of biclusters to be found.
Default: 1
Parameter for multiple node deletion to accelerate the algorithm.
Default: 1.0
Distribution of replacement values when masking found clusters.
Class Restriction: implements math.statistics.distribution.Distribution
Default: UniformDistribution
Known implementations:
Threshold for minimum number of points in a cluster.
The maximum level for splitting the hypercube.
The minimum dimensionality of the subspaces to be found.
Default: 1
The maximum jitter for distance values.
Flag to indicate that an adjustment of the applied heuristic for choosing an interval is performed after an interval is selected.
Default: false
Number of neighbors to use for PCA.
The class to compute (filtered) PCA.
Class Restriction: extends math.linearalgebra.pca.PCARunner
Default: PCARunner
Known implementations:
Filter class to determine the strong and weak eigenvectors.
Class Restriction: implements math.linearalgebra.pca.filter.EigenPairFilter
Default: PercentageEigenPairFilter
Known implementations:
The maximum radius of the neighborhood to be considered.
Threshold for minimum number of points in the epsilon-neighborhood of a point. The suggested value is '2 * dim - 1'.
Number of neighbors to use for PCA.
The class to compute (filtered) PCA.
Class Restriction: extends math.linearalgebra.pca.PCARunner
Default: PCARunner
Known implementations:
Filter class to determine the strong and weak eigenvectors.
Class Restriction: implements math.linearalgebra.pca.filter.EigenPairFilter
Default: PercentageEigenPairFilter
Known implementations:
Threshold for approximate linear dependency: the strong eigenvectors of q are approximately linear dependent from the strong eigenvectors p if the following condition holds for all stroneg eigenvectors q_i of q (lambda_q < lambda_p): q_i' * M^check_p * q_i <= delta^2.
Default: 0.1
Threshold for the maximum distance between two approximately linear dependent subspaces of two objects p and q (lambda_q < lambda_p) before considering them as parallel.
Default: 0.1
Threshold for minimum number of points in the epsilon-neighborhood of a point. The suggested value is '2 * dim - 1'.
The maximum radius of the neighborhood to be considered.
Threshold for minimum number of points in the epsilon-neighborhood of a point. The suggested value is '2 * dim - 1'.
Flag to mark delta as an absolute value.
Default: false
The threshold for strong Eigenvalues. If not otherwise specified, delta is a relative value w.r.t. the (absolute) highest Eigenvalues and has to be a double between 0 and 1. To mark delta as an absolute value, use the option -pca.filter.absolute.
Default: 0.1
Penalty factor for deviations in preferred (low-variance) dimensions.
Default: 20.0
Maximum dimensionality to consider for core points.
Specifies the smoothing factor. The mu-nearest neighbor is used to compute the correlation reachability of an object.
Optional parameter to specify the number of nearest neighbors considered in the PCA. If this parameter is not set, k is set to the value of parameter mu.
Threshold of a distance between a vector q and a given space that indicates that q adds a new dimension to the space.
Default: 0.25
The threshold for 'strong' eigenvectors: the 'strong' eigenvectors explain a portion of at least alpha of the total variance.
Default: 0.85
Maximum linear manifold dimension to search.
Minimum cluster size to allow.
A number used to determine how many samples are taken in each search.
Default: 100
Threshold to determine if a cluster was found.
Random generator seed.
Default: use global random seed
The number of clusters to find.
The multiplier for the initial number of seeds.
Default: 30
The dimensionality of the clusters to find.
The factor for reducing the number of current clusters in each iteration.
Default: 0.5
The random number generator seed.
Default: use global random seed
The class to compute (filtered) PCA.
Class Restriction: extends math.linearalgebra.pca.PCARunner
Default: PCARunner
Known implementations:
Method to choose the initial cluster centers.
Class Restriction: implements algorithm.clustering.kmeans.initialization.KMeansInitialization
Default: RandomlyChosenInitialMeans
Known implementations:
The number of clusters to find.
Model factory.
Class Restriction: implements algorithm.clustering.em.EMClusterModelFactory
Default: MultivariateGaussianModelFactory
Known implementations:
The termination criterion for maximization of E(M): E(M) - E(M') < em.delta
Default: 1.0E-7
The maximum number of iterations to do. 0 means no limit.
Regularization factor for MAP estimation.
Method to choose the initial cluster centers.
Class Restriction: implements algorithm.clustering.kmeans.initialization.KMeansInitialization
Default: RandomlyChosenInitialMeans
Known implementations:
Method to choose the initial cluster centers.
Class Restriction: implements algorithm.clustering.kmeans.initialization.KMeansInitialization
Default: RandomlyChosenInitialMeans
Known implementations:
Method to choose the initial cluster centers.
Class Restriction: implements algorithm.clustering.kmeans.initialization.KMeansInitialization
Default: RandomlyChosenInitialMeans
Known implementations:
Method to choose the initial cluster centers.
Class Restriction: implements algorithm.clustering.kmeans.initialization.KMeansInitialization
Default: RandomlyChosenInitialMeans
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
The maximum radius of the neighborhood to be considered.
Neighborhood predicate for Generalized DBSCAN
Class Restriction: implements algorithm.clustering.gdbscan.NeighborPredicate
Default: EpsilonNeighborPredicate
Known implementations:
Core point predicate for Generalized DBSCAN
Class Restriction: implements algorithm.clustering.gdbscan.CorePredicate
Default: MinPtsCorePredicate
Known implementations:
Use a model that keeps track of core points. Needs more memory.
Default: false
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Neighborhood size (k)
Density difference factor
Threshold for minimum number of points in the epsilon-neighborhood of a point. The suggested value is '2 * dim - 1'.
Similarity function to use.
Class Restriction: implements distance.similarityfunction.SimilarityFunction
Known implementations:
Minimum similarity of points to cluster.
Neighborhood predicate for Generalized DBSCAN
Class Restriction: implements algorithm.clustering.gdbscan.NeighborPredicate
Default: EpsilonNeighborPredicate
Known implementations:
Core point predicate for Generalized DBSCAN
Class Restriction: implements algorithm.clustering.gdbscan.CorePredicate
Default: MinPtsCorePredicate
Known implementations:
Use a model that keeps track of core points. Needs more memory.
Default: false
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.SquaredEuclideanDistanceFunction
Known implementations:
Linkage method to use (e.g. Ward, Single-Link)
Class Restriction: implements algorithm.clustering.hierarchical.linkage.Linkage
Default: WardLinkage
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.SquaredEuclideanDistanceFunction
Known implementations:
Linkage method to use (e.g. Ward, Single-Link)
Class Restriction: implements algorithm.clustering.hierarchical.linkage.Linkage
Default: WardLinkage
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Threshold for minimum number of points in the epsilon-neighborhood of a point (including this point).
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.SquaredEuclideanDistanceFunction
Known implementations:
Linkage method to use (e.g. Ward, Single-Link)
Class Restriction: implements algorithm.clustering.hierarchical.linkage.Linkage
Default: WardLinkage
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Threshold for minimum number of points in the epsilon-neighborhood of a point (including this point).
Distance function to use for node assignment.
Class Restriction: implements algorithm.clustering.hierarchical.birch.BIRCHDistance
Default: VarianceIncreaseDistance
Known implementations:
Absorption criterion to use.
Class Restriction: implements algorithm.clustering.hierarchical.birch.BIRCHAbsorptionCriterion
Default: DiameterCriterion
Known implementations:
Threshold for adding points to existing nodes in the CF-Tree.
Maximum branching factor of the CF-Tree
Default: 64
Maximum number of leaves (if less than 1, the values is assumed to be relative)
Default: 0.05
Algorithm to run.
Class Restriction: implements algorithm.clustering.hierarchical.HierarchicalClusteringAlgorithm
Known implementations:
The number of clusters to extract.
The minimum cluster size.
Algorithm to run.
Class Restriction: implements algorithm.clustering.hierarchical.HierarchicalClusteringAlgorithm
Known implementations:
Generate a truncated hierarchical clustering result (or strict partitions).
Default: false
The threshold level for which to extract the clusters.
Algorithm to run.
Class Restriction: implements algorithm.clustering.hierarchical.HierarchicalClusteringAlgorithm
Known implementations:
Generate a truncated hierarchical clustering result (or strict partitions).
Default: false
The minimum number of clusters to extract (there may be more clusters when tied, and singletons may be merged into a noise cluster).
Algorithm to run.
Class Restriction: implements algorithm.clustering.hierarchical.HierarchicalClusteringAlgorithm
Known implementations:
The minimum cluster size.
Default: 1
Produce a hierarchical output.
Default: false
Algorithm to run.
Class Restriction: implements algorithm.clustering.hierarchical.HierarchicalClusteringAlgorithm
Known implementations:
The minimum cluster size.
Default: 1
Beta for the Lance-Williams flexible beta approach.
Default: -0.25
The number of trials to run.
KMeans variant to run multiple times.
Class Restriction: implements algorithm.clustering.kmeans.KMeans
Known implementations:
Quality measure variant for deciding which run to keep.
Class Restriction: implements algorithm.clustering.kmeans.quality.KMeansQualityMeasure
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
The number of clusters to find.
Method to choose the initial means.
Class Restriction: implements algorithm.clustering.kmeans.initialization.KMedoidsInitialization
Default: PAMInitialMeans
Known implementations:
The maximum number of iterations to do. 0 means no limit.
Default: 0
Number of samples (iterations) to run.
Default: 5
The size of the sample.
Default: 40.0
Draw independent samples (default is to keep the previous best medoids in the sample).
Default: false
Random generator seed.
Default: use global random seed
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
The number of clusters to find.
Number of samples (restarts) to run.
Default: 2
Number of tries to find a neighbor.
Default: 0.0125
Random generator seed.
Default: use global random seed
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
The number of clusters to find.
Method to choose the initial means.
Class Restriction: implements algorithm.clustering.kmeans.initialization.KMedoidsInitialization
Default: LABInitialMeans
Known implementations:
The maximum number of iterations to do. 0 means no limit.
Default: 0
Tolerance for optimistically performing additional swaps, where 1 executes all fast swaps, 0 only those that are unaffected by the primary swaps.
Default: 1.0
Number of samples (iterations) to run.
Default: 5
The size of the sample.
Default: 80.0
Draw independent samples (default is to keep the previous best medoids in the sample).
Default: false
Random generator seed.
Default: use global random seed
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
The number of clusters to find.
Number of samples (restarts) to run.
Default: 2
Number of tries to find a neighbor.
Default: 0.025
Random generator seed.
Default: use global random seed
The number of clusters to find.
Method to choose the initial means.
Class Restriction: implements algorithm.clustering.kmeans.initialization.KMeansInitialization
Default: RandomlyChosenInitialMeans
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.PrimitiveDistanceFunction
Default: minkowski.SquaredEuclideanDistanceFunction
Known implementations:
The maximum number of iterations to do. 0 means no limit.
Default: 0
Compute the final clustering variance statistic. Needs an additional full pass over the data set.
Default: false
The number of clusters to find.
KMeans variant
Class Restriction: implements algorithm.clustering.kmeans.KMeans
Default: BestOfMultipleKMeans
Known implementations:
The number of clusters to find.
Method to choose the initial means.
Class Restriction: implements algorithm.clustering.kmeans.initialization.KMeansInitialization
Default: RandomlyChosenInitialMeans
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.PrimitiveDistanceFunction
Default: minkowski.SquaredEuclideanDistanceFunction
Known implementations:
The maximum number of iterations to do. 0 means no limit.
Default: 0
The number of clusters to find.
Method to choose the initial means.
Class Restriction: implements algorithm.clustering.kmeans.initialization.KMeansInitialization
Default: RandomlyChosenInitialMeans
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.PrimitiveDistanceFunction
Default: minkowski.SquaredEuclideanDistanceFunction
Known implementations:
The maximum number of iterations to do. 0 means no limit.
Default: 0
Compute the final clustering variance statistic. Needs an additional full pass over the data set.
Default: false
The number of clusters to find.
Method to choose the initial means.
Class Restriction: implements algorithm.clustering.kmeans.initialization.KMeansInitialization
Default: RandomlyChosenInitialMeans
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.PrimitiveDistanceFunction
Default: minkowski.SquaredEuclideanDistanceFunction
Known implementations:
The maximum number of iterations to do. 0 means no limit.
Default: 0
Compute the final clustering variance statistic. Needs an additional full pass over the data set.
Default: false
The number of clusters to find.
Method to choose the initial means.
Class Restriction: implements algorithm.clustering.kmeans.initialization.KMeansInitialization
Default: RandomlyChosenInitialMeans
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.PrimitiveDistanceFunction
Default: minkowski.SquaredEuclideanDistanceFunction
Known implementations:
The maximum number of iterations to do. 0 means no limit.
Default: 0
Compute the final clustering variance statistic. Needs an additional full pass over the data set.
Default: false
The number of clusters to find.
Method to choose the initial means.
Class Restriction: implements algorithm.clustering.kmeans.initialization.KMeansInitialization
Default: RandomlyChosenInitialMeans
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.PrimitiveDistanceFunction
Default: minkowski.SquaredEuclideanDistanceFunction
Known implementations:
The maximum number of iterations to do. 0 means no limit.
Default: 0
The number of clusters to find.
Method to choose the initial means.
Class Restriction: implements algorithm.clustering.kmeans.initialization.KMeansInitialization
Default: RandomlyChosenInitialMeans
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.PrimitiveDistanceFunction
Default: minkowski.SquaredEuclideanDistanceFunction
Known implementations:
The maximum number of iterations to do. 0 means no limit.
Default: 0
The number of clusters to find.
Method to choose the initial means.
Class Restriction: implements algorithm.clustering.kmeans.initialization.KMeansInitialization
Default: RandomlyChosenInitialMeans
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.PrimitiveDistanceFunction
Default: minkowski.SquaredEuclideanDistanceFunction
Known implementations:
The maximum number of iterations to do. 0 means no limit.
Default: 0
Number of outliers to ignore, or (if less than 1) a relative rate.
Default: 0.05
Create a noise cluster, instead of assigning the noise objects.
Default: false
The number of clusters to find.
Method to choose the initial means.
Class Restriction: implements algorithm.clustering.kmeans.initialization.KMeansInitialization
Default: RandomlyChosenInitialMeans
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.PrimitiveDistanceFunction
Default: minkowski.SquaredEuclideanDistanceFunction
Known implementations:
The maximum number of iterations to do. 0 means no limit.
Default: 0
Compute the final clustering variance statistic. Needs an additional full pass over the data set.
Default: false
The number of clusters to find.
Method to choose the initial means.
Class Restriction: implements algorithm.clustering.kmeans.initialization.KMeansInitialization
Default: RandomlyChosenInitialMeans
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.PrimitiveDistanceFunction
Default: minkowski.SquaredEuclideanDistanceFunction
Known implementations:
The maximum number of iterations to do. 0 means no limit.
Default: 0
The number of clusters to find.
Method to choose the initial means.
Class Restriction: implements algorithm.clustering.kmeans.initialization.KMeansInitialization
Default: RandomlyChosenInitialMeans
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.PrimitiveDistanceFunction
Default: minkowski.SquaredEuclideanDistanceFunction
Known implementations:
The maximum number of iterations to do. 0 means no limit.
Default: 0
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
The number of clusters to find.
Method to choose the initial means.
Class Restriction: implements algorithm.clustering.kmeans.initialization.KMedoidsInitialization
Default: LABInitialMeans
Known implementations:
The maximum number of iterations to do. 0 means no limit.
Default: 0
Tolerance for optimistically performing additional swaps, where 1 executes all fast swaps, 0 only those that are unaffected by the primary swaps.
Default: 1.0
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
The number of clusters to find.
Method to choose the initial means.
Class Restriction: implements algorithm.clustering.kmeans.initialization.KMedoidsInitialization
Default: PAMInitialMeans
Known implementations:
The maximum number of iterations to do. 0 means no limit.
Default: 0
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
The number of clusters to find.
Method to choose the initial means.
Class Restriction: implements algorithm.clustering.kmeans.initialization.KMedoidsInitialization
Default: PAMInitialMeans
Known implementations:
The maximum number of iterations to do. 0 means no limit.
Default: 0
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
The number of clusters to find.
Method to choose the initial means.
Class Restriction: implements algorithm.clustering.kmeans.initialization.KMedoidsInitialization
Default: PAMInitialMeans
Known implementations:
The maximum number of iterations to do. 0 means no limit.
Default: 0
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
The number of clusters to find.
Method to choose the initial means.
Class Restriction: implements algorithm.clustering.kmeans.initialization.KMedoidsInitialization
Default: ParkInitialMeans
Known implementations:
The maximum number of iterations to do. 0 means no limit.
Default: 0
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.PrimitiveDistanceFunction
Default: minkowski.SquaredEuclideanDistanceFunction
Known implementations:
The number of clusters to find.
Method to choose the initial means.
Class Restriction: implements algorithm.clustering.kmeans.initialization.KMeansInitialization
Default: RandomlyChosenInitialMeans
Known implementations:
The minimum number of clusters to find.
Default: 2
The number of clusters to find.
Method to choose the initial means.
Class Restriction: implements algorithm.clustering.kmeans.initialization.KMeansInitialization
Default: RandomlyChosenInitialMeans
Known implementations:
The maximum number of iterations to do. 0 means no limit.
Default: 0
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.PrimitiveDistanceFunction
Default: minkowski.SquaredEuclideanDistanceFunction
Known implementations:
Random seed for splitting clusters.
Default: use global random seed
kMeans algorithm to use.
Class Restriction: implements algorithm.clustering.kmeans.KMeans
Default: KMeansLloyd
Known implementations:
The quality measure to evaluate splits (e.g. AIC, BIC)
Class Restriction: implements algorithm.clustering.kmeans.quality.KMeansQualityMeasure
Known implementations:
The random number generator seed.
Default: use global random seed
Keep the first object chosen (which is chosen randomly) for the farthest points heuristic.
Default: false
The random number generator seed.
Default: use global random seed
Keep the first object chosen (which is chosen randomly) for the farthest points heuristic.
Default: false
The random number generator seed.
Default: use global random seed
The random number generator seed.
Default: use global random seed
The random number generator seed.
Default: use global random seed
Initial means for k-means.
The random number generator seed.
Default: use global random seed
The random number generator seed.
Default: use global random seed
The random number generator seed.
Default: use global random seed
The random number generator seed.
Default: use global random seed
KMeans variant to run multiple times.
Class Restriction: implements algorithm.clustering.kmeans.KMeans
Known implementations:
Sample set size (if > 1) or sampling rante (if < 1).
The number of clusters to find.
Method to choose the initial means.
Class Restriction: implements algorithm.clustering.kmeans.initialization.KMeansInitialization
Default: RandomlyChosenInitialMeans
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.PrimitiveDistanceFunction
Default: minkowski.SquaredEuclideanDistanceFunction
Known implementations:
The maximum number of iterations to do. 0 means no limit.
Default: 0
The file name containing the (external) cluster vector.
Dimension to use for clustering. For one-dimensional data, use 0.
Default: 0
Kernel function for density estimation.
Class Restriction: implements math.statistics.kernelfunctions.KernelDensityFunction
Default: EpanechnikovKernelDensityFunction
Known implementations:
Kernel density estimation mode (baloon estimator vs. sample point estimator).
Default: BALLOON
Number of nearest neighbors to use for bandwidth estimation.
Half width of sliding window to find local minima.
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Threshold for minimum number of points within a cluster.
Threshold for minimum number of points in the epsilon-neighborhood of a point.
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
The maximum radius of the neighborhood to be considered.
Threshold for minimum number of points in the epsilon-neighborhood of a point.
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
The maximum radius of the neighborhood to be considered.
Threshold for minimum number of points in the epsilon-neighborhood of a point.
Threshold for the steepness requirement.
The actual OPTICS-type algorithm to use.
Class Restriction: implements algorithm.clustering.optics.OPTICSTypeAlgorithm
Default: OPTICSHeap
Known implementations:
Disable the predecessor correction.
Default: false
Keep the steep up/down areas of the plot.
Default: false
The number of intervals (units) in each dimension.
The density threshold for the selectivity of a unit, where the selectivity is the fraction of total feature vectors contained in this unit.
Flag to indicate that only subspaces with large coverage (i.e. the fraction of the database that is covered by the dense units) are selected, the rest will be pruned.
Default: false
Minimum relative density for a set of points to be considered a cluster (|C|>=doc.alpha*|S|).
Default: 0.2
Preference of cluster size versus number of relevant dimensions (higher value means higher priority on larger clusters).
Default: 0.8
Maximum extent of scattering of points along a single attribute for the attribute to be considered relevant.
Default: 0.05
Random seed, for reproducible experiments.
Default: use global random seed
The maximum radius of the neighborhood to be considered in each dimension for determination of the preference vector.
Default: 0.001
The minimum number of points as a smoothing factor to avoid the single-link-effekt.
Default: 1
Minimum relative density for a set of points to be considered a cluster (|C|>=doc.alpha*|S|).
Default: 0.2
Preference of cluster size versus number of relevant dimensions (higher value means higher priority on larger clusters).
Default: 0.8
Maximum extent of scattering of points along a single attribute for the attribute to be considered relevant.
Default: 0.05
Random seed, for reproducible experiments.
Default: use global random seed
Parameter for FastDOC, setting the number of relevant attributes which, when found for a cluster, are deemed enough to stop iterating.
Default: 5
The significance level for uniform testing in the initial binning step.
Default: 0.001
The threshold value for the poisson test used when merging signatures.
Default: 1.0E-4
The maximum number of iterations for the EM step. Use -1 to run until delta convergence.
Default: 20
The change delta for the EM step below which to stop.
Default: 1.0E-5
The minimum size of a cluster, otherwise it is seen as noise (this is a cheat, it is not mentioned in the paper).
Default: 1
The number of clusters to find.
The multiplier for the initial number of seeds.
Default: 30
The dimensionality of the clusters to find.
The multiplier for the initial number of medoids.
Default: 10
The random number generator seed.
Default: use global random seed
The maximum radius of the neighborhood to be considered.
Threshold for minimum number of points in the epsilon-neighborhood of a point. The suggested value is '2 * dim - 1'.
A double specifying the variance threshold for small Eigenvalues.
Penalty factor for deviations in preferred (low-variance) dimensions.
Default: 20.0
Maximum dimensionality to consider for core points.
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.subspace.DimensionSelectingSubspaceDistanceFunction
Default: SubspaceEuclideanDistanceFunction
Known implementations:
The maximum radius of the neighborhood to be considered.
Threshold for minimum number of points in the epsilon-neighborhood of a point.
Minimum dimensionality to generate clusters for.
Flag to indicate that only subspaces with large coverage (i.e. the fraction of the database that is covered by the dense units) are selected, the rest will be pruned.
Default: false
Pattern to recognize noise classes by their label.
Pattern to recognize noise models by their label.
Algorithm to run.
Class Restriction: implements algorithm.clustering.kmeans.KMeans
Default: KMeansHamerly
Known implementations:
Algorithm to run.
Class Restriction: implements algorithm.clustering.ClusteringAlgorithm
Known implementations:
The maximum radius of the neighborhood to be considered.
Threshold for minimum number of points in the epsilon-neighborhood of a point. The suggested value is '2 * dim - 1'.
The number of samples to draw from each uncertain object to determine the epsilon-neighborhood.
The amount of samples that have to be epsilon-close for two objects to be neighbors.
Default: 0.5
Random generator used to draw samples.
Default: use global random seed
The maximum radius of the neighborhood to be considered.
The number of samples to draw from each uncertain object to determine the epsilon-neighborhood.
The amount of samples that have to be epsilon-close for two objects to be neighbors.
Default: 0.5
Random generator used to draw samples.
Default: use global random seed
Distance measure of clusterings.
Class Restriction: implements distance.similarityfunction.cluster.ClusteringDistanceSimilarityFunction
Default: ClusteringAdjustedRandIndexSimilarityFunction
Known implementations:
Algorithm used to aggregate clustering results. Must be a distance-based clustering algorithm.
Class Restriction: implements algorithm.clustering.ClusteringAlgorithm
Default: kmeans.KMedoidsPAM
Known implementations:
Clustering algorithm used on the samples.
Class Restriction: implements algorithm.clustering.ClusteringAlgorithm
Known implementations:
Number of clusterings to produce on samples.
Default: 10
Retain all sampled relations, not only the representative results.
Default: false
Random generator used for sampling.
Default: use global random seed
Alpha threshold for estimating the confidence probability.
Default: 0.95
The number of clusters to find.
The maximum number of iterations to do. 0 means no limit.
Default: 0
The random number generator seed.
Default: use global random seed
Threshold for minimum support as minimally required number of transactions (if > 1) or the minimum frequency (if <= 1).
Minimum length of frequent itemsets to report. This can help to reduce the output size to only the most interesting patterns.
Maximum length of frequent itemsets to report. This can help to reduce the output size to only the most interesting patterns.
Threshold for minimum support as minimally required number of transactions (if > 1) or the minimum frequency (if <= 1).
Minimum length of frequent itemsets to report. This can help to reduce the output size to only the most interesting patterns.
Maximum length of frequent itemsets to report. This can help to reduce the output size to only the most interesting patterns.
Threshold for minimum support as minimally required number of transactions (if > 1) or the minimum frequency (if <= 1).
Minimum length of frequent itemsets to report. This can help to reduce the output size to only the most interesting patterns.
Maximum length of frequent itemsets to report. This can help to reduce the output size to only the most interesting patterns.
Algorithm to be used for frequent itemset mining.
Class Restriction: extends algorithm.itemsetmining.AbstractFrequentItemsetAlgorithm
Default: FPGrowth
Known implementations:
Interestingness measure to be used
Class Restriction: implements algorithm.itemsetmining.associationrules.interest.InterestingnessMeasure
Default: Confidence
Known implementations:
Minimum threshold for specified interstingness measure
Maximum threshold for specified interstingness measure
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
The number of nearest neighbors of an object to be considered for computing its COP_SCORE.
The assumed distribution of squared distances. ChiSquared is faster, Gamma expected to be more accurate but could also overfit.
Default: GAMMA
Expected share of outliers. Only affect score normalization.
Default: 0.001
The class to compute (filtered) PCA.
Class Restriction: extends math.linearalgebra.pca.PCARunner
Default: PCARunner
Known implementations:
Include COP models (error vectors) in output. This needs more memory.
Default: false
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Number of neighbors to get for DWOF score outlier detection.
Radius increase factor.
Default: 1.1
Invert the value range to [0:1], with 1 being outliers instead of 0.
Default: false
cutoff
Default: 1.0E-7
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Threshold for minimum number of points in the epsilon-neighborhood of a point.
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
The number of nearest neighbors of an object to be considered for computing its COP_SCORE.
The class to compute (filtered) PCA.
Class Restriction: extends math.linearalgebra.pca.PCARunner
Default: PCARunner
Known implementations:
Filter class to determine the strong and weak eigenvectors.
Class Restriction: implements math.linearalgebra.pca.filter.EigenPairFilter
Default: PercentageEigenPairFilter
Known implementations:
Kernel function to use.
Class Restriction: implements distance.similarityfunction.SimilarityFunction
Default: kernel.PolynomialKernelFunction
Known implementations:
Kernel function to use.
Class Restriction: implements distance.similarityfunction.SimilarityFunction
Default: kernel.PolynomialKernelFunction
Known implementations:
Number of nearest neighbors to use for ABOD.
Kernel function to use.
Class Restriction: implements distance.similarityfunction.SimilarityFunction
Default: kernel.PolynomialKernelFunction
Known implementations:
Number of nearest neighbors to use for ABOD.
Number of top outliers to compute.
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.NumberVectorDistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
The ratio of the data that should be included in the large clusters
The ratio of the data that should be included in the large clusters
Clustering algorithm to use for detecting outliers.
Class Restriction: implements algorithm.clustering.ClusteringAlgorithm
Default: kmeans.KMeansSort
Known implementations:
Clustering algorithm to use for detecting outliers.
Class Restriction: implements algorithm.clustering.kmeans.KMeans
Default: KMeansLloyd
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Clustering algorithm to use for the silhouette coefficients.
Class Restriction: implements algorithm.clustering.ClusteringAlgorithm
Known implementations:
Control how noise should be treated.
Default: TREAT_NOISE_AS_SINGLETONS
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
size of the D-neighborhood
minimum fraction of objects that must be outside the D-neighborhood of an outlier
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
size of the D-neighborhood
Compute up to k next neighbors
Default: 5
Compute n outliers
Default: 10
Max. Hilbert-Level
Default: 32
Distance function to determine the distance between database objects.
Class Restriction: extends distance.distancefunction.minkowski.LPNormDistanceFunction
Default: EuclideanDistanceFunction
Known implementations:
output of Top n or all elements
Default: TopN
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
The k nearest neighbor, excluding the query point (i.e. query point is the 0-nearest-neighbor)
Default: 1
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
The k nearest neighbor, excluding the query point (i.e. query point is the 0-nearest-neighbor)
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Number of neighbors to use. Should be about 3x the desired perplexity.
Default: 15
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
The k nearest neighbor, excluding the query point (i.e. query point is the 0-nearest-neighbor)
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
The k nearest neighbor, excluding the query point (i.e. query point is the 0-nearest-neighbor)
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Number of neighbors to use for kNN graph.
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.NumberVectorDistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
The number of nearest neighbors
The heuristic for finding reference points.
Class Restriction: implements utilities.referencepoints.ReferencePointsHeuristic
Default: GridBasedReferencePoints
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Perplexity to use.
Default: 4.5
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
The k nearest neighbor, excluding the query point (i.e. query point is the 0-nearest-neighbor)
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
The k nearest neighbor, excluding the query point (i.e. query point is the 0-nearest-neighbor)
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Estimator of intrinsic dimensionality.
Class Restriction: implements math.statistics.intrinsicdimensionality.IntrinsicDimensionalityEstimator
Default: ALIDEstimator
Known implementations:
Context set size (ID estimation).
Reference set size.
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Number of neighbors to use. Should be about 3x the desired perplexity.
Default: 100
Estimator for intrinsic dimensionality.
Class Restriction: implements math.statistics.intrinsicdimensionality.IntrinsicDimensionalityEstimator
Default: AggregatedHillEstimator
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Number of nearest neighbors to use for ID estimation (usually 20-100).
Class to estimate ID from distance distribution.
Class Restriction: implements math.statistics.intrinsicdimensionality.IntrinsicDimensionalityEstimator
Default: MOMEstimator
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.NumberVectorDistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Minimum neighborhood size to be considered.
Default: 20
The number of Grids to use.
Default: 1
The seed to use for initializing Random.
Default: use global random seed
Scaling factor for averaging neighborhood
Default: 4
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
The number of neighbors (not including the query object) to use for computing the COF score.
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
The number of nearest neighbors of an object to be considered for computing its LOF score.
The number of nearest neighbors of an object to be considered for computing its LOF score.
Distance function to determine the reachability distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
The pruning threshold
Default: 1.0
The number of nearest neighbors of an object to be considered for computing its INFLO score.
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Kernel density function to use.
Class Restriction: implements math.statistics.kernelfunctions.KernelDensityFunction
Default: GaussianKernelDensityFunction
Known implementations:
Minimum value of k to analyze.
Maximum value of k to analyze.
Scaling factor for the kernel function.
Default: 0.25
Minimum bandwidth for kernel density estimation.
Intrinsic dimensionality of this data set. Use -1 for using the true data dimensionality, but values such as 0-2 often offer better performance.
Default: 1
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Number of neighbors to use for LDF.
Kernel to use for LDF.
Class Restriction: implements math.statistics.kernelfunctions.KernelDensityFunction
Default: GaussianKernelDensityFunction
Known implementations:
Kernel bandwidth multiplier for LDF.
Score scaling parameter for LDF.
Default: 0.1
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
The number of nearest neighbors of an object to be considered for computing its LDOF_SCORE.
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
The maximum radius of the neighborhood to be considered.
Minimum neighborhood size to be considered.
Default: 20
Scaling factor for averaging neighborhood
Default: 0.5
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
The number of nearest neighbors (not including the query point) of an object to be considered for computing its LOF score.
The number of nearest neighbors of an object to be considered for computing its LOOP_SCORE.
Distance function to determine the reference set of an object.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
The number of nearest neighbors of an object to be used for the PRD value.
Distance function to determine the density of an object.
Class Restriction: implements distance.distancefunction.DistanceFunction
Known implementations:
The number of standard deviations to consider for density computation.
Default: 2.0
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
The number of nearest neighbors of an object to be considered for computing its LOF score.
The number of nearest neighbors of an object to be considered for computing its LOF score.
Distance function to determine the reachability distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
The number of nearest neighbors (not including the query point) of an object to be considered for computing its LOF score.
Kernel to use for kernel density LOF.
Class Restriction: implements math.statistics.kernelfunctions.KernelDensityFunction
Default: EpanechnikovKernelDensityFunction
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
The number of nearest neighbors (not including the query point) of an object to be considered for computing its LOF score.
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
The number of nearest neighbors (not including the query point) of an object to be considered for computing its VOV score.
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
The number of nearest neighbors (not including the query point) of an object to be considered for computing its LOF score.
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
The number of nearest neighbors (not including the query point) of an object to be considered for computing its LOF score.
The file name containing the (external) outlier scores.
The pattern to match object ID prefix
Default: ^ID=
The pattern to match object score prefix
Flag to signal an inverted outlier score.
Default: false
Class to use as scaling function.
Class Restriction: implements utilities.scaling.ScalingFunction
Default: IdentityScaling
Known implementations:
The number of nearest neighbors (not including the query point) of an object to be considered for computing its LOF score.
The number of instances to use in the ensemble.
Use the breadth first combinations instead of the cumulative sum approach
Default: false
Specify a particular random seed.
Default: use global random seed
The number of iterations in the Monte-Carlo processing.
Default: 50
The discriminance value that determines the size of the test statistic .
Default: 0.1
The Algorithm that performs the actual outlier detection on the resulting set of subspace
Class Restriction: implements algorithm.outlier.OutlierAlgorithm
Default: lof.LOF
Known implementations:
The statistical test that is used to calculate the deviation of two data samples
Class Restriction: implements math.statistics.tests.GoodnessOfFitTest
Default: KolmogorovSmirnovTest
Known implementations:
The threshold that determines how many d-dimensional subspace candidates to retain in each step of the generation
Default: 100
The random seed.
Default: use global random seed
Algorithm to run.
Class Restriction: implements algorithm.outlier.OutlierAlgorithm
Known implementations:
Class to use as scaling function.
Class Restriction: implements utilities.scaling.ScalingFunction
Known implementations:
Algorithm to run.
Class Restriction: implements algorithm.outlier.OutlierAlgorithm
Known implementations:
Voting strategy to use in the ensemble.
Class Restriction: implements utilities.ensemble.EnsembleVoting
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Significance niveau
k nearest neighbors to use
The neighborhood predicate to use in comparison step.
Class Restriction: implements algorithm.outlier.spatial.neighborhood.NeighborSetPredicate
Known implementations:
The neighborhood predicate to use in comparison step.
Class Restriction: implements algorithm.outlier.spatial.neighborhood.NeighborSetPredicate
Known implementations:
The neighborhood predicate to use in comparison step.
Class Restriction: implements algorithm.outlier.spatial.neighborhood.NeighborSetPredicate
Known implementations:
The neighborhood predicate to use in comparison step.
Class Restriction: implements algorithm.outlier.spatial.neighborhood.NeighborSetPredicate
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Number of nearest neighbors to use.
Scaling exponent for value differences.
Default: 0.5
The damping parameter c.
The neighborhood predicate to use in comparison step.
Class Restriction: implements algorithm.outlier.spatial.neighborhood.NeighborSetPredicate
Known implementations:
The neighborhood predicate to use in comparison step.
Class Restriction: implements algorithm.outlier.spatial.neighborhood.NeighborSetPredicate
Known implementations:
The neighborhood predicate to use in comparison step.
Class Restriction: implements algorithm.outlier.spatial.neighborhood.NeighborSetPredicate
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.PrimitiveDistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
The neighborhood predicate to use in comparison step.
Class Restriction: implements algorithm.outlier.spatial.neighborhood.NeighborSetPredicate
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.PrimitiveDistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
The neighborhood predicate to use in comparison step.
Class Restriction: implements algorithm.outlier.spatial.neighborhood.NeighborSetPredicate
Known implementations:
the percentile parameter
The inner neighborhood predicate to use.
Class Restriction: implements algorithm.outlier.spatial.neighborhood.NeighborSetPredicate
Known implementations:
The number of steps allowed in the neighborhood graph.
The file listing the neighbors.
the number of neighbors
the distance function to use
Class Restriction: implements distance.distancefunction.DistanceFunction
Known implementations:
The inner neighborhood predicate to use.
Class Restriction: implements algorithm.outlier.spatial.neighborhood.NeighborSetPredicate
Known implementations:
The number of steps allowed in the neighborhood graph.
Parameter for the non-weighted neighborhood to use.
Class Restriction: implements algorithm.outlier.spatial.neighborhood.NeighborSetPredicate
Known implementations:
Subspace dimensionality to search for.
The number of equi-depth grid ranges to use in each dimension.
Population size for evolutionary algorithm.
The random number generator seed.
Default: use global random seed
Subspace dimensionality to search for.
The number of equi-depth grid ranges to use in each dimension.
Range value for OUTRES in 2 dimensions.
The similarity function used for the neighborhood set.
Class Restriction: implements distance.similarityfunction.SimilarityFunction
Default: SharedNearestNeighborSimilarityFunction
Known implementations:
The number of most snn-similar objects to use as reference set for learning the subspace properties.
The multiplier for the discriminance value for discerning small from large variances.
Default: 1.1
Report the models computed by SOD (default: report only scores).
Default: false
Kernel to use with SVM.
Default: RBF
SVM nu parameter.
Default: 0.05
Label pattern to match outliers.
Default: .*(Outlier|Noise).*
Expected amount of outliers, for making the scores more intuitive. When the value is 1, the CDF will be given instead.
Default: 0.01
Affinity matrix builder.
Class Restriction: implements algorithm.projection.AffinityMatrixBuilder
Default: NearestNeighborAffinityMatrixBuilder
Known implementations:
Output dimensionality.
Default: 2
The final momentum to use.
Default: 0.8
Learning rate of the method.
Default: 200.0
Number of iterations to perform.
Default: 1000
Random generator seed
Default: use global random seed
Retain the original data.
Default: false
Approximation quality parameter
Default: 0.5
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.SquaredEuclideanDistanceFunction
Known implementations:
Gaussian kernel standard deviation.
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.SquaredEuclideanDistanceFunction
Known implementations:
Desired perplexity (approximately the number of neighbors to preserve)
Default: 40.0
Estimator for intrinsic dimensionality.
Class Restriction: implements math.statistics.intrinsicdimensionality.IntrinsicDimensionalityEstimator
Default: AggregatedHillEstimator
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.SquaredEuclideanDistanceFunction
Known implementations:
Desired perplexity (approximately the number of neighbors to preserve)
Default: 40.0
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.SquaredEuclideanDistanceFunction
Known implementations:
Desired perplexity (approximately the number of neighbors to preserve)
Default: 40.0
Affinity matrix builder.
Class Restriction: implements algorithm.projection.AffinityMatrixBuilder
Default: PerplexityAffinityMatrixBuilder
Known implementations:
Output dimensionality.
Default: 2
The final momentum to use.
Default: 0.8
Learning rate of the method.
Default: 200.0
Number of iterations to perform.
Default: 1000
Random generator seed
Default: use global random seed
Retain the original data.
Default: false
Affinity matrix builder.
Class Restriction: implements algorithm.projection.AffinityMatrixBuilder
Default: PerplexityAffinityMatrixBuilder
Known implementations:
Output dimensionality.
Default: 2
The final momentum to use.
Default: 0.8
Learning rate of the method.
Default: 200.0
Number of iterations to perform.
Default: 1000
Random generator seed
Default: use global random seed
Retain the original data.
Default: false
Forcibly set the scales to the given range.
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
K to compute the average precision at.
Relative amount of object to sample.
Random seed for deterministic sampling.
Default: use global random seed
Include the query object in the evaluation.
Default: false
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Quantile to compute.
Default: 0.1
Number of distances to compute, either relative (values less than 1), or absolute.
Ignore zero distances, beneficial for data sets with many duplicates.
Default: false
Random generator seed.
Default: use global random seed
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Number of bins to use in the histogram. By default, it is only guaranteed to be within 1*n and 2*n of the given number.
Default: 20
In a first pass, compute the exact minimum and maximum, at the cost of O(2*n*n) instead of O(n*n). The number of resulting bins is guaranteed to be as requested.
Default: false
Enable sampling of O(n) size to determine the minimum and maximum distances approximately. The resulting number of bins can be larger than the given n.
Default: false
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Estimation method for intrinsic dimensionality.
Class Restriction: implements math.statistics.intrinsicdimensionality.IntrinsicDimensionalityEstimator
Default: GEDEstimator
Known implementations:
Number of kNN (absolute or relative)
Default: 50.0
Sample size (absolute or relative)
Default: 0.1
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Number of bins to use in the histogram
Default: 20
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Relative amount of object to sample.
Random seed for deterministic sampling.
Default: use global random seed
Include the query object in the evaluation.
Default: false
Maximum value of k for kNN evaluation.
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.NumberVectorDistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
The number of times to repeat the experiment (default: 1)
Default: 1
Nearest neighbor to use for the statistic
Default: 1
Number of object / random samples to analyze.
The random number generator.
Default: use global random seed
Minimum values in each dimension. If no value is specified, the minimum value in each dimension will be used. If only one value is specified, this value will be used for all dimensions.
Maximum values in each dimension. If no value is specified, the maximum value in each dimension will be used. If only one value is specified, this value will be used for all dimensions.
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Radius to use for selectivity estimation.
Relative amount of object to sample.
Random seed for deterministic sampling.
Default: use global random seed
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Number of bins to use in the histogram
Default: 100
Number of samples to draw for bootstrapping the confidence estimate.
Default: 1000
Confidence level to use with bootstrap sampling.
Default: 0.9975
Random generator seed for bootstrap sampling.
Default: use global random seed
Half-life time: number of time steps until data has lost half its weight.
Adjustment for chance: a small constant corresponding to background noise levels.
Significance threshold for reporting
a comma separated array of integer values d_i, where 0 <= d_i < the dimensionality of the feature space specifying the dimensions to be considered for projection. If this parameter is not set, no dimensions will be considered, i.e. the projection is a zero-dimensional feature space
Earth model to use for projection. Default: spherical model.
Class Restriction: implements math.geodesy.EarthModel
Default: SphericalVincentyEarthModel
Known implementations:
Earth model to use for projection. Default: spherical model.
Class Restriction: implements math.geodesy.EarthModel
Default: SphericalVincentyEarthModel
Known implementations:
a comma separated array of integer values d_i, where 0 <= d_i < the dimensionality of the feature space specifying the dimensions to be considered for projection. If this parameter is not set, no dimensions will be considered, i.e. the projection is a zero-dimensional feature space
Projection family to use.
Class Restriction: implements data.projection.random.RandomProjectionFamily
Default: AchlioptasRandomProjectionFamily
Known implementations:
Amount of dimensions to project to.
Random generator seed.
Default: use global random seed
Frequency of zeros in the projection matrix.
Default: 3.0
Random generator seed.
Default: use global random seed
Random generator seed.
Default: use global random seed
Random generator seed.
Default: use global random seed
Random generator seed.
Default: use global random seed
Minimum 3-sigma deviation of uncertain region.
Default: 0.0
Maximum 3-sigma deviation of uncertain region.
Generate a symetric uncertain region, centered around the exact data.
Default: false
Minimum deviation of uncertain bounding box.
Default: 0.0
Maximum deviation of uncertain bounding box.
Generate a symetric uncertain region, centered around the exact data.
Default: false
Class to generate the point distribution.
Class Restriction: implements data.uncertain.uncertainifier.Uncertainifier
Known implementations:
Maximum points per uncertain object.
Default: 10
Minimum points per uncertain object (defaults to maximum.
Class to generate the point distribution.
Class Restriction: implements data.uncertain.uncertainifier.Uncertainifier
Known implementations:
Maximum points per uncertain object.
Default: 10
Minimum points per uncertain object (defaults to maximum.
Database connection class.
Class Restriction: implements datasource.DatabaseConnection
Default: FileBasedDatabaseConnection
Known implementations:
Database indexes to add.
Class Restriction: implements index.IndexFactory
Known implementations:
Database connection class.
Class Restriction: implements datasource.DatabaseConnection
Default: FileBasedDatabaseConnection
Known implementations:
Database indexes to add.
Class Restriction: implements index.IndexFactory
Known implementations:
The filters to apply to the input data.
Class Restriction: implements datasource.filter.ObjectFilter
Known implementations:
Bundle file to load the data from.
The name of the input file to be parsed.
The filters to apply to the input data.
Class Restriction: implements datasource.filter.ObjectFilter
Known implementations:
Parser to provide the database.
Class Restriction: implements datasource.parser.Parser
Default: NumberVectorLabelParser
Known implementations:
First integer DBID to generate.
Default: 0
Number of DBID to generate.
The filters to apply to the input data.
Class Restriction: implements datasource.filter.ObjectFilter
Known implementations:
The data sources to join.
Class Restriction: implements datasource.DatabaseConnection
Known implementations:
The name of the input file to be parsed.
Parser to provide the database.
Class Restriction: implements datasource.parser.Parser
Default: NumberVectorLabelParser
Known implementations:
The filters to apply to the input data.
Class Restriction: implements datasource.filter.ObjectFilter
Known implementations:
The generator specification file.
Factor for scaling the specified cluster sizes.
Default: 1.0
Pattern to specify clusters to reassign.
The random generator seed.
Default: use global random seed
The filters to apply to the input data.
Class Restriction: implements datasource.filter.ObjectFilter
Known implementations:
Input stream to read. Defaults to standard input.
Class Restriction: extends java.io.InputStream
Parser to provide the database.
Class Restriction: implements datasource.parser.Parser
Default: NumberVectorLabelParser
Known implementations:
The filters to apply to the input data.
Class Restriction: implements datasource.filter.ObjectFilter
Known implementations:
The filters to apply to the input data.
Class Restriction: implements datasource.filter.ObjectFilter
Known implementations:
The data sources to join.
Class Restriction: implements datasource.DatabaseConnection
Known implementations:
The filters to apply to the input data.
Class Restriction: implements datasource.filter.ObjectFilter
Known implementations:
The data sources to join.
Class Restriction: implements datasource.DatabaseConnection
Known implementations:
The filters to apply to the input data.
Class Restriction: implements datasource.filter.ObjectFilter
Known implementations:
Dimensionality of the vectors to generate.
Database size to generate.
Seed for randomly generating vectors
Default: use global random seed
Object ID to start counting with
Default: 0
Distribution to sample replacement values from.
Class Restriction: implements math.statistics.distribution.Distribution
Known implementations:
Dimensionality of vectors to retain.
A list of the distribution estimators to try.
Class Restriction: implements math.statistics.distribution.estimator.DistributionEstimator
Default: meta.BestFitEstimator
Known implementations:
Alpha parameter to control the shape of the output distribution.
Default: 0.1
A list of the distribution estimators to try.
Class Restriction: implements math.statistics.distribution.estimator.DistributionEstimator
Default: meta.BestFitEstimator
Known implementations:
a comma separated concatenation of the minimum values in each dimension that are mapped to 0. If no value is specified, the minimum value of the attribute range in this dimension will be taken.
a comma separated concatenation of the maximum values in each dimension that are mapped to 1. If no value is specified, the maximum value of the attribute range in this dimension will be taken.
a comma separated concatenation of the mean values in each dimension that are mapped to 0. If no value is specified, the mean value of the attribute range in this dimension will be taken.
a comma separated concatenation of the standard deviations in each dimension that are scaled to 1. If no value is specified, the standard deviation of the attribute range in this dimension will be taken.
Minimum value to assign to objects.
Default: 0.0
Maximum value to assign to objects.
Default: 1.0
Norm (length function) to use for computing the vector length.
Class Restriction: implements distance.distancefunction.Norm
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Boosting factor. Larger values will yield a steeper curve.
Default: 1.0
The filter pattern to use.
Flag to invert pattern.
Default: false
Number of objects to keep.
Sampling probability. Each object has a chance of being sampled with this probability.
Random generator seed for sampling.
Default: use global random seed
Seed for randomly shuffling the rows for the database. If the parameter is not set, a random seed will be used.
Default: use global random seed
Output dimensionality.
Distance function to use.
Class Restriction: implements distance.distancefunction.PrimitiveDistanceFunction
Default: minkowski.SquaredEuclideanDistanceFunction
Known implementations:
The type of vectors to create.
Class Restriction: implements data.NumberVector
Default: DoubleVector
Known implementations:
Output dimensionality.
Distance function to use.
Class Restriction: implements distance.distancefunction.PrimitiveDistanceFunction
Default: minkowski.SquaredEuclideanDistanceFunction
Known implementations:
Random seed for fast MDS.
Default: use global random seed
The type of vectors to create.
Class Restriction: implements data.NumberVector
Default: DoubleVector
Known implementations:
Filter to use for dimensionality reduction.
Class Restriction: implements math.linearalgebra.pca.filter.EigenPairFilter
Known implementations:
Operation mode: full, or rotate only.
Default: FULL
Jitter amount relative to data.
Jitter random seed.
Default: use global random seed
Earth model to use for projection. Default: spherical model.
Class Restriction: implements math.geodesy.EarthModel
Default: SphericalVincentyEarthModel
Known implementations:
Projection dimensionality
Default: 2
Earth model to use for projection. Default: spherical model.
Class Restriction: implements math.geodesy.EarthModel
Default: SphericalVincentyEarthModel
Known implementations:
a comma separated array of integer values d_i, where 0 <= d_i < the dimensionality of the feature space specifying the dimensions to be considered for projection. If this parameter is not set, no dimensions will be considered, i.e. the projection is a zero-dimensional feature space
number of selected attributes
Default: 1
Seed for random selection of projection attributes.
Default: use global random seed
The reference for scaling the Gaussian noise. Default is UNITCUBE, parameter perturbationfilter.percentage will then directly define the standard deviation of all noise Gaussians. For options STDDEV and MINMAX, the percentage of the attributewise standard deviation or extension, repectively, will define the attributewise standard deviation of the noise Gaussians.
Default: UNITCUBE
The nature of the noise distribution, default is UNIFORM
Default: UNIFORM
Percentage of the standard deviation of the random Gaussian noise generation per attribute, given the standard deviation of the corresponding attribute in the original data distribution (assuming a Gaussian distribution there).
Default: 0.01
Seed for random noise generation.
Only used, if MINMAX is set as scaling reference: a comma separated concatenation of the minimum values in each dimension assumed as a reference. If no value is specified, the minimum value of the attribute range in this dimension will be taken.
Only used, if MINMAX is set as scaling reference: a comma separated concatenation of the maximum values in each dimension assumed as a reference. If no value is specified, the maximum value of the attribute range in this dimension will be taken.
Projection to use.
Class Restriction: implements data.projection.Projection
Known implementations:
The index of the label to be used as class label. The first label is 0, negative indexes are relative to the end.
Class label class to use.
Class Restriction: extends data.ClassLabel
Default: SimpleClassLabel
Known implementations:
Regular expression to identify positive objects.
Class label to use for positive instances.
Default: positive
Class label to use for negative instances.
Default: negative
The index of the label to be used as external Id. The first label is 0; negative indexes are relative to the end.
Number of variates this time series has.
Dimensions to split into the first relation.
Dimensionality of the data set (used for splitting).
Generator to derive uncertain objects from certain vectors.
Class Restriction: implements data.uncertain.uncertainifier.Uncertainifier
Known implementations:
Keep the original data as well.
Default: false
Random seed for uncertainification.
Default: use global random seed
Dimensionality of the data set (used for splitting).
Column in which the probability is stored, starting at 0. -1 is the last column.
Pattern to recognize external ID attributes.
Default: (External-?ID)
Pattern to recognize class label attributes.
Default: (Class|Class-?Label)
Column separator pattern. The default assumes whitespace separated data.
Default: \s*[,;\s]\s*
Quotation characters. By default, both double and single ASCII quotes are accepted.
Default: "'
Ignore lines in the input file that satisfy this pattern.
Default: ^\s*(#|//|;).*$
A comma separated list of the indices of labels (may be numeric), counting whitespace separated entries in a line starting with 0. The corresponding entries will be treated as a label.
The type of vectors to create for numerical attributes.
Class Restriction: implements data.NumberVector
Default: DoubleVector
Known implementations:
The type of vectors to create for numerical attributes.
Class Restriction: implements data.SparseNumberVector
Default: SparseFloatVector
Known implementations:
A comma separated list of the indices of labels (may be numeric), counting whitespace separated entries in a line starting with 0. The corresponding entries will be treated as a label.
The type of vectors to create for numerical attributes.
Class Restriction: implements data.NumberVector
Default: DoubleVector
Known implementations:
A comma separated list of the indices of labels (may be numeric), counting whitespace separated entries in a line starting with 0. The corresponding entries will be treated as a label.
The type of vectors to create for numerical attributes.
Class Restriction: implements data.SparseNumberVector
Default: SparseFloatVector
Known implementations:
Ignore lines in the input file that satisfy this pattern.
Default: ^\s*#.*$
Remove leading and trailing whitespace from each line.
Default: false
A comma separated list of the indices of labels (may be numeric), counting whitespace separated entries in a line starting with 0. The corresponding entries will be treated as a label.
The type of vectors to create for numerical attributes.
Class Restriction: implements data.SparseNumberVector
Default: SparseFloatVector
Known implementations:
Normalize vectors to manhattan length 1 (convert term counts to term frequencies)
Default: false
Distance index to use.
Class Restriction: implements index.preprocessed.snn.SharedNearestNeighborIndex
Default: SharedNearestNeighborPreprocessor
Known implementations:
Weights to use for the distance function.
Similarity function to derive the distance between database objects from.
Class Restriction: implements distance.similarityfunction.NormalizedSimilarityFunction
Known implementations:
Similarity function to derive the distance between database objects from.
Class Restriction: implements distance.similarityfunction.NormalizedSimilarityFunction
Known implementations:
Similarity function to derive the distance between database objects from.
Class Restriction: implements distance.similarityfunction.NormalizedSimilarityFunction
Known implementations:
The dimensionality of the histogram in hue, saturation and brightness.
The dimensionality of the histogram in each color
Weights to use for the distance function.
Weights to use for the distance function.
The name of the file containing the distance matrix.
The name of the file containing the distance matrix.
The name of the file containing the distance matrix.
Parser used to load the distance matrix.
Class Restriction: implements distance.distancefunction.external.DistanceParser
Default: AsciiDistanceParser
Known implementations:
Default distance to use for undefined values.
Default: Infinity
The name of the file containing the distance matrix.
Parser used to load the distance matrix.
Class Restriction: implements distance.distancefunction.external.DistanceParser
Default: AsciiDistanceParser
Known implementations:
Default distance to use for undefined values.
Default: Infinity
The dimension containing the latitude.
The dimension containing the longitude.
Earth model to use for projection. Default: spherical model.
Class Restriction: implements math.geodesy.EarthModel
Default: SphericalVincentyEarthModel
Known implementations:
Earth model to use for projection. Default: spherical model.
Class Restriction: implements math.geodesy.EarthModel
Default: SphericalVincentyEarthModel
Known implementations:
Earth model to use for projection. Default: spherical model.
Class Restriction: implements math.geodesy.EarthModel
Default: SphericalVincentyEarthModel
Known implementations:
Degree p of the L_p-Norm (positive number)
Degree p of the L_p-Norm (positive number)
Degree p of the L_p-Norm (positive number)
Weights to use for the distance function.
Degree p of the L_p-Norm (positive number)
Weights to use for the distance function.
Weights to use for the distance function.
Weights to use for the distance function.
Weights to use for the distance function.
an integer between 1 and the dimensionality of the feature space 1 specifying the dimension to be considered for distance computation.
a comma separated array of integer values, where 0 <= d_i < the dimensionality of the feature space specifying the dimensions to be considered for distance computation. If this parameter is not set, no dimensions will be considered, i.e. the distance between two objects is always 0.
Degree p of the L_p-Norm (positive number)
a comma separated array of integer values, where 0 <= d_i < the dimensionality of the feature space specifying the dimensions to be considered for distance computation. If this parameter is not set, no dimensions will be considered, i.e. the distance between two objects is always 0.
a comma separated array of integer values, where 0 <= d_i < the dimensionality of the feature space specifying the dimensions to be considered for distance computation. If this parameter is not set, no dimensions will be considered, i.e. the distance between two objects is always 0.
a comma separated array of integer values, where 0 <= d_i < the dimensionality of the feature space specifying the dimensions to be considered for distance computation. If this parameter is not set, no dimensions will be considered, i.e. the distance between two objects is always 0.
The band size for time series alignment. By default, no constraint is used. If the value is larger than 0, it will be considered absolute, otherwise relative to the longer sequence. Note that 0 does not make sense: use Euclidean distance then instead.
The band size for time series alignment. By default, no constraint is used. If the value is larger than 0, it will be considered absolute, otherwise relative to the longer sequence. Note that 0 does not make sense: use Euclidean distance then instead.
the delta parameter (similarity threshold) for EDR (positive number)
Default: 1.0
The band size for time series alignment. By default, no constraint is used. If the value is larger than 0, it will be considered absolute, otherwise relative to the longer sequence. Note that 0 does not make sense: use Euclidean distance then instead.
The g parameter of ERP - comparison value to use in gaps.
Default: 0.0
the allowed deviation in x direction for LCSS alignment (positive double value, 0 <= pDelta <= 1)
Default: 0.1
the allowed deviation in y direction for LCSS alignment (positive double value, 0 <= pEpsilon <= 1)
Default: 0.05
Preprocessor to use.
Class Restriction: implements index.preprocessed.snn.SharedNearestNeighborIndex
Default: SharedNearestNeighborPreprocessor
Known implementations:
Preprocessor to use.
Class Restriction: implements index.preprocessed.snn.SharedNearestNeighborIndex
Default: SharedNearestNeighborPreprocessor
Known implementations:
Standard deviation of the laplace RBF kernel.
Default: 1.0
The degree of the polynomial kernel function. Default: 2
Default: 2
The bias of the polynomial kernel, a constant that is added to the scalar product.
Standard deviation of the Gaussian RBF kernel.
Default: 1.0
Constant term in the rational quadratic kernel.
Default: 1.0
Sigmoid c parameter (scaling).
Default: 1.0
Sigmoid theta parameter (bias).
Default: 0.0
Random generator seed for holdout evaluation.
Default: use global random seed
Number of folds for cross-validation.
Default: 10
Random generator seed for holdout evaluation.
Default: use global random seed
positive number of folds for cross-validation
Default: 10
Number of folds for cross-validation
Default: 10
Reference clustering to compare with. Defaults to a by-label clustering.
Class Restriction: implements algorithm.clustering.ClusteringAlgorithm
Default: trivial.ByLabelOrAllInOneClustering
Known implementations:
Use special handling for noise clusters.
Default: false
Enable self-pairing for cluster comparison.
Default: false
Distance function to use for computing the c-index.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Control how noise should be treated.
Default: TREAT_NOISE_AS_SINGLETONS
Distance function to use for measuring concordant and discordant pairs.
Class Restriction: implements distance.distancefunction.PrimitiveDistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Control how noise should be treated.
Default: TREAT_NOISE_AS_SINGLETONS
Distance function to use for computing the dbcv.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Distance function to use for computing the davies-bouldin index.
Class Restriction: implements distance.distancefunction.NumberVectorDistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Control how noise should be treated.
Default: TREAT_NOISE_AS_SINGLETONS
Distance function to use for computing PBM.
Class Restriction: implements distance.distancefunction.NumberVectorDistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Control how noise should be treated.
Default: TREAT_NOISE_AS_SINGLETONS
Distance function to use for computing the silhouette.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Control how noise should be treated.
Default: TREAT_NOISE_AS_SINGLETONS
Distance function to use for computing the silhouette.
Class Restriction: implements distance.distancefunction.NumberVectorDistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Control how noise should be treated.
Default: TREAT_NOISE_AS_SINGLETONS
Distance function to use for computing the SSQ.
Class Restriction: implements distance.distancefunction.NumberVectorDistanceFunction
Default: minkowski.SquaredEuclideanDistanceFunction
Known implementations:
Control how noise should be treated.
Default: TREAT_NOISE_AS_SINGLETONS
Control how noise should be treated.
Default: TREAT_NOISE_AS_SINGLETONS
Class label for the 'positive' class.
number of bins
Default: 50
Class to use as scaling function.
Class Restriction: implements utilities.scaling.ScalingFunction
Default: IdentityScaling
Known implementations:
Use separate frequencies for outliers and non-outliers.
Default: false
Class label for the 'positive' class.
Class to use as scaling function.
Class Restriction: implements utilities.scaling.ScalingFunction
Default: IdentityScaling
Known implementations:
Class label for the 'positive' class.
Maximum value of 'k' to compute the curve up to.
Class label for the 'positive' class.
Class label for the 'positive' class.
Class label for the 'positive' class.
Class label for the 'positive' class.
Class to use as scaling function.
Class Restriction: implements utilities.scaling.ScalingFunction
Default: IdentityScaling
Known implementations:
Threshold(s) to apply.
k value for precision@k. Can be set to 0, to get R-precision, or the precision-recall-break-even-point.
Default: 0
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Class to use as scaling function.
Class Restriction: implements utilities.scaling.ScalingFunction
Known implementations:
Skip zero values when computing the colors to increase contrast.
Default: false
Distance function for the precomputed distance matrix.
Class Restriction: implements distance.distancefunction.DistanceFunction
Known implementations:
Similarity function for the precomputed similarity matrix.
Class Restriction: implements distance.similarityfunction.SimilarityFunction
Known implementations:
Distance function to build the index for.
Class Restriction: implements distance.distancefunction.DistanceFunction
Known implementations:
Method to choose the reference points.
Class Restriction: implements algorithm.clustering.kmeans.initialization.KMedoidsInitialization
Known implementations:
Number of reference points to use.
Hash function family to use for LSH.
Class Restriction: implements index.lsh.hashfamilies.LocalitySensitiveHashFunctionFamily
Known implementations:
Number of hash tables to use.
Number of hash buckets to use.
Default: 7919
Random seed for generating the projections.
Default: use global random seed
Number of projections to use for each hash function.
Random seed for generating the projections.
Default: use global random seed
Bin width for random projections.
Number of projections to use for each hash function.
Random seed for generating the projections.
Default: use global random seed
Bin width for random projections.
Number of projections to use for each hash function.
Random seed for generating projections.
Default: use global random seed
The number of nearest neighbors of an object to be materialized.
the distance function to materialize the nearest neighbors
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Filename with the precomputed k nearest neighbors.
The number of nearest neighbors of an object to be materialized.
the distance function to materialize the nearest neighbors
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
The number of nearest neighbors of an object to be materialized.
the distance function to materialize the nearest neighbors
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
The number of nearest neighbors of an object to be materialized.
the distance function to materialize the nearest neighbors
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
The number of nearest neighbors of an object to be materialized.
the distance function to materialize the nearest neighbors
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
The number of nearest neighbors of an object to be materialized.
the distance function to materialize the nearest neighbors
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
The random number seed.
Default: use global random seed
The early termination parameter.
Default: 0.001
The sample rate parameter
Default: 1.0
Do not use initial neighbors.
Default: false
maximum number of iterations
Default: 100
Window size multiplicator.
Default: 10.0
Number of projections to use.
Random projection family to use. The default is to use the original axes.
Class Restriction: implements data.projection.random.RandomProjectionFamily
Known implementations:
Random generator.
Default: use global random seed
The number of nearest neighbors of an object to be materialized.
the distance function to materialize the nearest neighbors
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
The number of partitions to use for approximate kNN.
The random number generator seed.
Default: use global random seed
The number of nearest neighbors of an object to be materialized.
the distance function to materialize the nearest neighbors
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
The relative amount of objects to consider for kNN computations.
The random number seed.
Default: use global random seed
Space filling curve generators to use for kNN approximation.
Class Restriction: implements math.spacefillingcurves.SpatialSorter
Known implementations:
Window size multiplicator.
Default: 10.0
Number of curve variants to generate.
Default: 1
Number of dimensions to use for each curve.
Random projection to use.
Class Restriction: implements data.projection.random.RandomProjectionFamily
Known implementations:
Random generator.
Default: use global random seed
The number of nearest neighbors of an object to be materialized.
the distance function to materialize the nearest neighbors
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Space filling curve generators to use for kNN approximation.
Class Restriction: implements math.spacefillingcurves.SpatialSorter
Known implementations:
Window size multiplicator.
Default: 10.0
Number of curve variants to generate.
Default: 1
Random generator.
Default: use global random seed
The number of nearest neighbors of an object to be materialized.
the distance function to materialize the nearest neighbors
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
The distance function used to select objects for running PCA.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
The class to compute (filtered) PCA.
Class Restriction: extends math.linearalgebra.pca.PCARunner
Default: PCARunner
Known implementations:
Filter class to determine the strong and weak eigenvectors.
Class Restriction: implements math.linearalgebra.pca.filter.EigenPairFilter
Default: PercentageEigenPairFilter
Known implementations:
The number of nearest neighbors considered in the PCA. If this parameter is not set, k ist set to three times of the dimensionality of the database objects.
The strategy for determination of the preference vector, available strategies are: [APRIORI| MAX_INTERSECTION](default is MAX_INTERSECTION)
Default: MAX_INTERSECTION
Positive threshold for minumum numbers of points in the epsilon-neighborhood of a point. The value of the preference vector in dimension d_i is set to 1 if the epsilon neighborhood contains more than dish.minpts points and the following condition holds: for all dimensions d_j: |neighbors(d_i) intersection neighbors(d_j)| >= dish.minpts.
A comma separated list of positive doubles specifying the maximum radius of the neighborhood to be considered in each dimension for determination of the preference vector (default is 0.001 in each dimension). If only one value is specified, this value will be used for each dimension.
Default: 0.001
The maximum absolute variance along a coordinate axis.
Default: 0.01
The number of nearest neighbors considered to determine the preference vector. If this value is not defined, k ist set to three times of the dimensionality of the database objects.
number of nearest neighbors to consider (at least 1)
the distance function to asses the nearest neighbors
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Earth model to use for projection. Default: spherical model.
Class Restriction: implements math.geodesy.EarthModel
Default: SphericalVincentyEarthModel
Known implementations:
Index to use on the projected data.
Class Restriction: implements index.IndexFactory
Known implementations:
Flag to materialize the projected data.
Default: false
Flag to disable refinement of distances.
Default: false
Earth model to use for projection. Default: spherical model.
Class Restriction: implements math.geodesy.EarthModel
Default: SphericalVincentyEarthModel
Known implementations:
Index to use on the projected data.
Class Restriction: implements index.IndexFactory
Known implementations:
Flag to materialize the projected data.
Default: false
Flag to disable refinement of distances.
Default: false
Index to use on the projected data.
Class Restriction: implements index.IndexFactory
Known implementations:
Target dimensionality.
Sparsity of the random projection.
Default: 1.0
Multiplicator for neighborhood size.
Default: 3.0
Random generator seed.
Default: use global random seed
Projection to use for the projected index.
Class Restriction: implements data.projection.Projection
Known implementations:
Index to use on the projected data.
Class Restriction: implements index.IndexFactory
Known implementations:
Flag to materialize the projected data.
Default: false
Flag to disable refinement of distances.
Default: false
Multiplier for k.
Default: 1.0
Distance function to determine the distance between objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Known implementations:
Truncate tree when branches have less than this number of instances.
Default: 10
Expansion rate of the tree (Default: 1.3).
Default: 1.3
Distance function to determine the distance between objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Known implementations:
Truncate tree when branches have less than this number of instances.
Default: 10
Expansion rate of the tree (Default: 1.3).
Default: 1.3
The pagefile factory for storing the index.
Class Restriction: implements persistent.PageFileFactory
Default: MemoryPageFileFactory
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Split strategy to use for constructing the M-tree.
Class Restriction: implements index.tree.metrical.mtreevariants.strategies.split.MTreeSplit
Default: MLBDistSplit
Known implementations:
Insertion strategy to use for constructing the M-tree.
Class Restriction: implements index.tree.metrical.mtreevariants.strategies.insert.MTreeInsert
Default: MinimumEnlargementInsert
Known implementations:
positive integer specifying the maximum number k of reverse k nearest neighbors to be supported.
positive integer specifying the order of the polynomial approximation.
Flag to indicate that the approximation is done in the ''normal'' space instead of the log-log space (which is default).
Default: false
The pagefile factory for storing the index.
Class Restriction: implements persistent.PageFileFactory
Default: MemoryPageFileFactory
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Split strategy to use for constructing the M-tree.
Class Restriction: implements index.tree.metrical.mtreevariants.strategies.split.MTreeSplit
Default: MLBDistSplit
Known implementations:
Insertion strategy to use for constructing the M-tree.
Class Restriction: implements index.tree.metrical.mtreevariants.strategies.insert.MTreeInsert
Default: MinimumEnlargementInsert
Known implementations:
positive integer specifying the maximum number k of reverse k nearest neighbors to be supported.
The pagefile factory for storing the index.
Class Restriction: implements persistent.PageFileFactory
Default: MemoryPageFileFactory
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Split strategy to use for constructing the M-tree.
Class Restriction: implements index.tree.metrical.mtreevariants.strategies.split.MTreeSplit
Default: MLBDistSplit
Known implementations:
Insertion strategy to use for constructing the M-tree.
Class Restriction: implements index.tree.metrical.mtreevariants.strategies.insert.MTreeInsert
Default: MinimumEnlargementInsert
Known implementations:
Specifies the maximal number k of reverse k nearest neighbors to be supported.
The pagefile factory for storing the index.
Class Restriction: implements persistent.PageFileFactory
Default: MemoryPageFileFactory
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Split strategy to use for constructing the M-tree.
Class Restriction: implements index.tree.metrical.mtreevariants.strategies.split.MTreeSplit
Default: MLBDistSplit
Known implementations:
Insertion strategy to use for constructing the M-tree.
Class Restriction: implements index.tree.metrical.mtreevariants.strategies.insert.MTreeInsert
Default: MinimumEnlargementInsert
Known implementations:
Specifies the maximal number k of reverse k nearest neighbors to be supported.
The pagefile factory for storing the index.
Class Restriction: implements persistent.PageFileFactory
Default: MemoryPageFileFactory
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Split strategy to use for constructing the M-tree.
Class Restriction: implements index.tree.metrical.mtreevariants.strategies.split.MTreeSplit
Default: MLBDistSplit
Known implementations:
Insertion strategy to use for constructing the M-tree.
Class Restriction: implements index.tree.metrical.mtreevariants.strategies.insert.MTreeInsert
Default: MinimumEnlargementInsert
Known implementations:
Distribution strategy for mtree entries during splitting.
Class Restriction: implements index.tree.metrical.mtreevariants.strategies.split.distribution.DistributionStrategy
Default: GeneralizedHyperplaneDistribution
Known implementations:
Distribution strategy for mtree entries during splitting.
Class Restriction: implements index.tree.metrical.mtreevariants.strategies.split.distribution.DistributionStrategy
Default: GeneralizedHyperplaneDistribution
Known implementations:
Distribution strategy for mtree entries during splitting.
Class Restriction: implements index.tree.metrical.mtreevariants.strategies.split.distribution.DistributionStrategy
Default: GeneralizedHyperplaneDistribution
Known implementations:
Distribution strategy for mtree entries during splitting.
Class Restriction: implements index.tree.metrical.mtreevariants.strategies.split.distribution.DistributionStrategy
Default: GeneralizedHyperplaneDistribution
Known implementations:
Distribution strategy for mtree entries during splitting.
Class Restriction: implements index.tree.metrical.mtreevariants.strategies.split.distribution.DistributionStrategy
Default: GeneralizedHyperplaneDistribution
Known implementations:
Random generator / seed for the randomized split.
Default: use global random seed
Maximum leaf size for the k-d-tree. Nodes will be split until their size is smaller than this threshold.
Default: 1
Maximum leaf size for the k-d-tree. Nodes will be split until their size is smaller than this threshold.
Default: 1
The pagefile factory for storing the index.
Class Restriction: implements persistent.PageFileFactory
Default: MemoryPageFileFactory
Known implementations:
The strategy to use for object insertion.
Class Restriction: implements index.tree.spatial.rstarvariants.strategies.insert.InsertionStrategy
Default: CombinedInsertionStrategy
Known implementations:
The strategy to use for node splitting.
Class Restriction: implements index.tree.spatial.rstarvariants.strategies.split.SplitStrategy
Default: TopologicalSplitter
Known implementations:
Minimum relative fill required for data pages.
Default: 0.4
The strategy to use for handling overflows.
Class Restriction: implements index.tree.spatial.rstarvariants.strategies.overflow.OverflowTreatment
Default: LimitedReinsertOverflowTreatment
Known implementations:
The class to perform the bulk split with.
Class Restriction: implements index.tree.spatial.rstarvariants.strategies.bulk.BulkSplit
Known implementations:
The pagefile factory for storing the index.
Class Restriction: implements persistent.PageFileFactory
Default: MemoryPageFileFactory
Known implementations:
The strategy to use for object insertion.
Class Restriction: implements index.tree.spatial.rstarvariants.strategies.insert.InsertionStrategy
Default: CombinedInsertionStrategy
Known implementations:
The strategy to use for node splitting.
Class Restriction: implements index.tree.spatial.rstarvariants.strategies.split.SplitStrategy
Default: TopologicalSplitter
Known implementations:
Minimum relative fill required for data pages.
Default: 0.4
The strategy to use for handling overflows.
Class Restriction: implements index.tree.spatial.rstarvariants.strategies.overflow.OverflowTreatment
Default: LimitedReinsertOverflowTreatment
Known implementations:
The class to perform the bulk split with.
Class Restriction: implements index.tree.spatial.rstarvariants.strategies.bulk.BulkSplit
Known implementations:
The pagefile factory for storing the index.
Class Restriction: implements persistent.PageFileFactory
Default: MemoryPageFileFactory
Known implementations:
The strategy to use for object insertion.
Class Restriction: implements index.tree.spatial.rstarvariants.strategies.insert.InsertionStrategy
Default: CombinedInsertionStrategy
Known implementations:
The strategy to use for node splitting.
Class Restriction: implements index.tree.spatial.rstarvariants.strategies.split.SplitStrategy
Default: TopologicalSplitter
Known implementations:
Minimum relative fill required for data pages.
Default: 0.4
The strategy to use for handling overflows.
Class Restriction: implements index.tree.spatial.rstarvariants.strategies.overflow.OverflowTreatment
Default: LimitedReinsertOverflowTreatment
Known implementations:
The class to perform the bulk split with.
Class Restriction: implements index.tree.spatial.rstarvariants.strategies.bulk.BulkSplit
Known implementations:
positive integer specifying the maximal number k of reverse k nearest neighbors to be supported.
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.SpatialPrimitiveDistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
The pagefile factory for storing the index.
Class Restriction: implements persistent.PageFileFactory
Default: MemoryPageFileFactory
Known implementations:
The strategy to use for object insertion.
Class Restriction: implements index.tree.spatial.rstarvariants.strategies.insert.InsertionStrategy
Default: CombinedInsertionStrategy
Known implementations:
The strategy to use for node splitting.
Class Restriction: implements index.tree.spatial.rstarvariants.strategies.split.SplitStrategy
Default: TopologicalSplitter
Known implementations:
Minimum relative fill required for data pages.
Default: 0.4
The strategy to use for handling overflows.
Class Restriction: implements index.tree.spatial.rstarvariants.strategies.overflow.OverflowTreatment
Default: LimitedReinsertOverflowTreatment
Known implementations:
The class to perform the bulk split with.
Class Restriction: implements index.tree.spatial.rstarvariants.strategies.bulk.BulkSplit
Known implementations:
Strategy for spatial sorting in bulk loading.
Class Restriction: implements math.spacefillingcurves.SpatialSorter
Known implementations:
defines how many children are tested for finding the child generating the least overlap when inserting an object.
Default: 32
Insertion strategy for directory nodes.
Class Restriction: implements index.tree.spatial.rstarvariants.strategies.insert.InsertionStrategy
Default: LeastEnlargementWithAreaInsertionStrategy
Known implementations:
Insertion strategy for leaf nodes.
Class Restriction: implements index.tree.spatial.rstarvariants.strategies.insert.InsertionStrategy
Default: LeastOverlapInsertionStrategy
Known implementations:
The strategy to select candidates for reinsertion.
Class Restriction: implements index.tree.spatial.rstarvariants.strategies.reinsert.ReinsertStrategy
Default: CloseReinsert
Known implementations:
The amount of entries to reinsert.
Default: 0.3
The distance function to compute reinsertion candidates by.
Class Restriction: implements distance.distancefunction.SpatialPrimitiveDistanceFunction
Default: minkowski.SquaredEuclideanDistanceFunction
Known implementations:
The amount of entries to reinsert.
Default: 0.3
The distance function to compute reinsertion candidates by.
Class Restriction: implements distance.distancefunction.SpatialPrimitiveDistanceFunction
Default: minkowski.SquaredEuclideanDistanceFunction
Known implementations:
The size of a page in bytes.
Default: 1024
Number of partitions to use in each dimension.
The size of a page in bytes.
Default: 1024
Number of partitions to use in each dimension.
Class used to compute the covariance matrix.
Class Restriction: implements math.linearalgebra.pca.CovarianceMatrixBuilder
Default: StandardCovarianceMatrixBuilder
Known implementations:
Filter for selecting eigenvectors during autotuning PCA.
Class Restriction: implements math.linearalgebra.pca.filter.EigenPairFilter
Default: SignificantEigenPairFilter
Known implementations:
Class used to compute the covariance matrix.
Class Restriction: implements math.linearalgebra.pca.CovarianceMatrixBuilder
Default: StandardCovarianceMatrixBuilder
Known implementations:
The number of iterations to perform.
Default: 1000
Random seed (optional).
Default: use global random seed
Weight function to use in weighted PCA.
Class Restriction: implements math.linearalgebra.pca.weightfunctions.WeightFunction
Default: ConstantWeight
Known implementations:
The minimum strength of the statistically expected variance (1/n) share an eigenvector needs to have to be considered 'strong'.
Default: 0.0
The number of strong eigenvectors: n eigenvectors with the n highest eigenvalues are marked as strong eigenvectors.
Flag to mark delta as an absolute value.
Default: false
The threshold for strong Eigenvalues. If not otherwise specified, delta is a relative value w.r.t. the (absolute) highest Eigenvalues and has to be a double between 0 and 1. To mark delta as an absolute value, use the option -pca.filter.absolute.
Default: 0.01
The share (0.0 to 1.0) of variance that needs to be explained by the 'strong' eigenvectors. The filter class will choose the number of strong eigenvectors by this share.
Default: 0.85
The share (0.0 to 1.0) of variance that needs to be explained by the 'strong' eigenvectors. The filter class will choose the number of strong eigenvectors by this share.
Default: 0.5
The minimum strength of the statistically expected variance (1/n) share an eigenvector needs to have to be considered 'strong'.
Default: 0.95
The sensitivity niveau for weak eigenvectors: An eigenvector which is at less than the given share of the statistical average variance is considered weak.
Default: 1.1
The minimum strength of the statistically expected variance (1/n) share an eigenvector needs to have to be considered 'strong'.
Default: 0.0
The minimum strength of the statistically expected variance (1/n) share an eigenvector needs to have to be considered 'strong'.
Default: 0.95
The number of iterations in the Monte-Carlo processing.
Default: 50
The discriminance value that determines the size of the test statistic .
Default: 0.1
The statistical test that is used to calculate the deviation of two data samples
Class Restriction: implements math.statistics.tests.GoodnessOfFitTest
Default: KolmogorovSmirnovTest
Known implementations:
The random seed.
Default: use global random seed
Random generation data source.
Default: use global random seed
Beta distribution alpha parameter
Beta distribution beta parameter
Random generation data source.
Default: use global random seed
Distribution location parameter
Cauchy distribution gamma/shape parameter.
Random generation data source.
Default: use global random seed
Chi distribution degrees of freedom parameter.
Random generation data source.
Default: use global random seed
Chi distribution degrees of freedom parameter.
Constant value.
Random generation data source.
Default: use global random seed
Gamma distribution k = alpha parameter.
Gamma distribution theta = 1/beta parameter.
Shift offset parameter.
Default: 0.0
Random generation data source.
Default: use global random seed
Distribution location parameter
Default: 0.0
Exponential distribution rate (lambda) parameter (inverse of scale).
Random generation data source.
Default: use global random seed
Distribution location parameter
Default: 0.0
Distribution scale parameter
Exponential distribution rate (lambda) parameter (inverse of scale).
Random generation data source.
Default: use global random seed
Gamma distribution k = alpha parameter.
Gamma distribution theta = 1/beta parameter.
Random generation data source.
Default: use global random seed
Distribution location parameter
Distribution scale parameter
Distribution shape parameter
Random generation data source.
Default: use global random seed
Distribution location parameter
Distribution scale parameter
Distribution shape parameter
Random generation data source.
Default: use global random seed
Distribution location parameter
Distribution scale parameter
Distribution shape parameter
Random generation data source.
Default: use global random seed
Distribution location parameter
Distribution scale parameter
Distribution shape parameter
Random generation data source.
Default: use global random seed
Distribution location parameter
Distribution shape parameter
Random generation data source.
Default: use global random seed
Minimum value of distribution.
Maximum value of distribution.
Random generation data source.
Default: use global random seed
Distribution location parameter
Distribution shape parameter
Random generation data source.
Default: use global random seed
Distribution location parameter
Distribution scale parameter
First shape parameter of kappa distribution.
Second shape parameter of kappa distribution.
Random generation data source.
Default: use global random seed
Distribution location parameter
Laplace distribution rate (lambda) parameter (inverse of scale).
Random generation data source.
Default: use global random seed
Gamma distribution k = alpha parameter.
Gamma distribution theta = 1/beta parameter.
Shift offset parameter.
Random generation data source.
Default: use global random seed
Distribution shape parameter
Distribution location parameter
Default: 0.0
Distribution scale parameter
Default: 1.0
Random generation data source.
Default: use global random seed
Mean of the distribution before logscaling.
Standard deviation of the distribution before logscaling.
Shifting offset, so the distribution does not begin at 0.
Default: 0.0
Random generation data source.
Default: use global random seed
Distribution scale parameter
Distribution location parameter
Random generation data source.
Default: use global random seed
Distribution location parameter
Distribution scale parameter
Random generation data source.
Default: use global random seed
Number of trials.
Success probability.
Random generation data source.
Default: use global random seed
Distribution location parameter
Default: 0.0
Distribution scale parameter
Random generation data source.
Default: use global random seed
Distribution location parameter
Distribution scale parameter
Skew of the distribution.
Random generation data source.
Default: use global random seed
Degrees of freedom.
Random generation data source.
Default: use global random seed
Minimum value of distribution.
Maximum value of distribution.
Random generation data source.
Default: use global random seed
Distribution location parameter
Default: 0.0
Distribution scale parameter
Distribution shape parameter
Estimator to use on the trimmed data.
Class Restriction: implements math.statistics.distribution.estimator.DistributionEstimator
Known implementations:
Relative amount of data to trim on each end, must be 0 < trim < 0.5
Estimator to use on the winsorized data.
Class Restriction: implements math.statistics.distribution.estimator.DistributionEstimator
Known implementations:
Relative amount of data to winsorize on each end, must be 0 < winsorize < 0.5
The backing pagefile for the cache.
Class Restriction: implements persistent.PageFileFactory
Default: PersistentPageFileFactory
Known implementations:
The size of the cache in bytes.
The size of a page in bytes.
Default: 4000
The size of a page in bytes.
Default: 4000
The name of the file storing the page file.
The size of a page in bytes.
Default: 4000
The name of the file storing the page file.
Title to use for visualization window.
Embed visualizers in a single window, not using thumbnails and detail views.
Default: false
Output file name. When not given, the result will be written to stdout.
Always append to the output file.
Default: false
Parameter to override the clustering label, mostly to give a more descriptive label.
The output folder.
The width/heigh ratio of the output.
Default: 1.33
File format. Note that some formats requrie additional libraries, only SVG and PNG are default.
Default: SVG
Filename the KMZ file (compressed KML) is written to.
Additional scaling function for KML colorization.
Class Restriction: implements utilities.scaling.outlier.OutlierScaling
Default: OutlierLinearScaling
Known implementations:
Use simpler KML objects, compatibility mode.
Default: false
Automatically open the result file.
Default: false
Directory name (or name of an existing file) to write the obtained results in. If this parameter is omitted, per default the output will sequentially be given to STDOUT.
Enable gzip compression of output files.
Default: false
Silently overwrite output files.
Default: false
Filter pattern for output selection. Only output streams that match the given pattern will be written.
Quantile to use in median voting.
Default: 0.5
Scale the data space extension by the given factor.
Default: 1.0
The number of partitions in each dimension. Points will be placed on the edges of the grid, except for a grid size of 0, where only the mean is generated as reference point.
Default: 1
Scale the grid by the given factor. This can be used to obtain reference points outside the used data space.
Default: 1.0
The number of reference points to be generated.
Scale the grid by the given factor. This can be used to obtain reference points outside the used data space.
Default: 1.0
Random generator seed.
Default: use global random seed
The number of samples to draw.
Random generator seed.
Default: use global random seed
Do not use the center as extra reference point.
Default: false
Scale the reference points by the given factor. This can be used to obtain reference points outside the used data space.
Default: 1.0
Minimum value to allow.
Maximum value to allow.
Gamma value for scaling.
Phi parameter, expected rate of outliers. Set to 0 to use raw CDF values.
Fixed mean to use in standard deviation scaling.
Significance level to use for error function.
Default: 3.0
Regularize scores before using Gamma scaling.
Default: false
Fixed minimum to use in linear scaling.
Fixed maximum to use in linear scaling.
Use the mean as minimum for scaling.
Default: false
Ignore zero entries when computing the minimum and maximum.
Default: false
Fixed minimum to use in sqrt scaling.
Fixed maximum to use in sqrt scaling.
Fixed minimum to use in sqrt scaling.
Fixed mean to use in standard deviation scaling.
Significance level to use for error function.
Default: 3.0
Fixed mean to use in standard deviation scaling.
Significance level to use for error function.
Default: 3.0
Number of outliers to keep.
Make the top k a binary scaling.
Default: false
Maximum number of objects to visualize by default (for performance reasons).
Default: 10000
Style properties file to use, included properties: classic, default, greyscale, neon, presentation, print
Default: default
Visualizers to enable by default.
Layouting method for 3DPC.
Class Restriction: implements visualization.parallel3d.layout.Layouter3DPC
Default: SimpleCircularMSTLayout3DPC
Known implementations:
Similarity measure for spanning tree.
Class Restriction: implements math.statistics.dependence.DependenceMeasure
Default: CorrelationDependenceMeasure
Known implementations:
Similarity measure for spanning tree.
Class Restriction: implements math.statistics.dependence.DependenceMeasure
Default: CorrelationDependenceMeasure
Known implementations:
Similarity measure for spanning tree.
Class Restriction: implements math.statistics.dependence.DependenceMeasure
Default: CorrelationDependenceMeasure
Known implementations:
Maximum number of dimensions to display.
Default: 10
Maximum number of dimensions to display.
Default: 10
Use curves instead of the stacked histogram style.
Default: false
Number of bins in the distribution histogram
Default: 80
Draw straight lines
Default: false
Alpha value for hull drawing (in projected space!).
Default: Infinity
Partially transparent filling of index pages.
Default: true
Number of digits to show (e.g. when visualizing outlier scores)
Default: 4
Alpha value for hull drawing (in projected space!).
Default: Infinity
Mode for drawing the voronoi cells (and/or delaunay triangulation)
Default: VORONOI
Partially transparent filling of index pages.
Default: false
Partially transparent filling of index pages.
Default: false
Half-transparent filling of bubbles.
Default: false
Additional scaling function for bubbles.
Class Restriction: implements utilities.scaling.ScalingFunction
Default: outlier.OutlierLinearScaling
Known implementations:
Use wireframe style for selection ranges.
Default: false
Drawing style for dendrograms.
Default: RECTANGULAR
Positioning logic for dendrograms.
Default: HALF_POS
Enable logging of runtime data. Do not combine with more verbose logging, since verbose logging can significantly impact performance.
Default: false
Algorithm to run.
Class Restriction: implements algorithm.Algorithm
Known implementations:
Class to evaluate the results with.
Class Restriction: implements evaluation.Evaluator
Default: AutomaticEvaluation
Known implementations:
Database class.
Class Restriction: implements database.Database
Default: StaticArrayDatabase
Known implementations:
Enable verbose messages.
Default: false
Parameter to enable debugging for particular packages.
Result handler class.
Class Restriction: implements result.ResultHandler
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
The minimum number of clusters to extract (there may be more clusters when tied, and singletons may be merged into a noise cluster).
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
The minimum number of clusters to extract (there may be more clusters when tied, and singletons may be merged into a noise cluster).
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
The minimum number of clusters to extract (there may be more clusters when tied, and singletons may be merged into a noise cluster).
Parameter to choose the linkage strategy.
Default: WARD
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Parameter to choose the linkage strategy.
Default: WARD
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.NumberVectorDistanceFunction
Default: minkowski.SquaredEuclideanDistanceFunction
Known implementations:
The number of clusters to find.
Method to choose the initial means.
Class Restriction: implements algorithm.clustering.kmeans.initialization.KMeansInitialization
Default: KMeansPlusPlusInitialMeans
Known implementations:
The maximum number of iterations to do. 0 means no limit.
Default: -1
The exponents to use for this distance function
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Number of neighbors to get for stddev based outlier detection.
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Number of neighbors to use for kNN graph.