Threshold for minimum frequency as percentage value (alternatively to parameter apriori.minsupp).
Threshold for minimum support as minimally required number of transactions (alternatively to parameter apriori.minfreq - setting apriori.minsupp is slightly preferable over setting apriori.minfreq in terms of efficiency).
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.PrimitiveDistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.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
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction
Known implementations:
Specifies the distance of the k-distant object to be assessed.
Default: 1
The average percentage of distances randomly choosen to be provided in the result.
Default: 1.0
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.PrimitiveDistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.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 de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.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 de.lmu.ifi.dbs.elki.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: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction
Known implementations:
Data source for the queries. If not set, the queries are taken from the database.
Class Restriction: implements de.lmu.ifi.dbs.elki.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: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.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 de.lmu.ifi.dbs.elki.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: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.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 de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.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 de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction
Known implementations:
Threshold for minimum number of points within a cluster.
The number of clusters to find.
Method to choose the initial means.
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansInitialization
Default: de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.RandomlyGeneratedInitialMeans
Known implementations:
The termination criterion for maximization of E(M): E(M) - E(M') < em.delta
Default: 0.0
The maximum number of iterations to do. 0 means no limit.
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction
Known implementations:
Kernel function to use with mean-shift clustering.
Class Restriction: implements de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions.KernelDensityFunction
Default: de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions.EpanechnikovKernelDensityFunction
Known implementations:
Range of the kernel to use (aka: radius, bandwidth).
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.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 de.lmu.ifi.dbs.elki.algorithm.clustering.OPTICSTypeAlgorithm
Default: de.lmu.ifi.dbs.elki.algorithm.clustering.OPTICS
Known implementations:
The minimum SNN density.
Threshold for minimum number of points in the epsilon-SNN-neighborhood of a point.
Similarity matrix initialization..
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.clustering.affinitypropagation.AffinityPropagationInitialization
Default: de.lmu.ifi.dbs.elki.algorithm.clustering.affinitypropagation.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 de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.SquaredEuclideanDistanceFunction
Known implementations:
Quantile to use for diagonal entries.
Default: 0.5
Similarity function to use.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.similarityfunction.SimilarityFunction
Default: de.lmu.ifi.dbs.elki.distance.similarityfunction.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 de.lmu.ifi.dbs.elki.math.statistics.distribution.Distribution
Default: de.lmu.ifi.dbs.elki.math.statistics.distribution.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
Local PCA Preprocessor to derive partition criterion.
Class Restriction: implements de.lmu.ifi.dbs.elki.index.preprocessed.LocalProjectionIndex$Factory
Known implementations:
Distance to use for the inner algorithms.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.FilteredLocalPCABasedDistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.LocallyWeightedDistanceFunction
Known implementations:
Clustering algorithm to apply to each partition.
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.clustering.ClusteringAlgorithm
Known implementations:
Distance function to determine the neighbors for variance analysis.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.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: extends de.lmu.ifi.dbs.elki.distance.distancefunction.LocallyWeightedDistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.LocallyWeightedDistanceFunction
Known implementations:
The intrinsic dimensionality of the clusters to find.
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: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
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: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
Neighborhood predicate for GDBSCAN
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan.NeighborPredicate
Default: de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan.EpsilonNeighborPredicate
Known implementations:
Core point predicate for GDBSCAN
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan.CorePredicate
Default: de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan.MinPtsCorePredicate
Known implementations:
Use a model that keeps track of core points. Needs more memory.
Default: false
Algorithm to run.
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.HierarchicalClusteringAlgorithm
Known implementations:
The thresholding mode to use for extracting clusters: by desired number of clusters, or by distance threshold.
Default: BY_MINCLUSTERS
The minimum number of clusters to extract (there may be more clusters when tied).
The threshold level for which to extract the clusters.
The output mode: a truncated cluster hierarchy, or a strict (flat) partitioning of the data set.
Do not avoid singleton clusters. This produces a more complex hierarchy.
Default: false
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.SquaredEuclideanDistanceFunction
Known implementations:
Linkage method to use (e.g. Ward, Single-Link)
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.LinkageMethod
Default: de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.WardLinkageMethod
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction
Known implementations:
The number of trials to run.
KMeans variant to run multiple times.
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeans
Known implementations:
Quality measure variant for deciding which run to keep.
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.quality.KMeansQualityMeasure
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.PrimitiveDistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.SquaredEuclideanDistanceFunction
Known implementations:
The number of clusters to find.
Method to choose the initial means.
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansInitialization
Default: de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.RandomlyChosenInitialMeans
Known implementations:
The maximum number of iterations to do. 0 means no limit.
Default: 0
Number of blocks to use for processing. Means will be recomputed after each block.
Default: 10
Random source for producing blocks.
Default: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
The number of clusters to find.
KMeans variant
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeans
Default: de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.BestOfMultipleKMeans
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.PrimitiveDistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.SquaredEuclideanDistanceFunction
Known implementations:
The number of clusters to find.
Method to choose the initial means.
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansInitialization
Default: de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.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 de.lmu.ifi.dbs.elki.distance.distancefunction.PrimitiveDistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.SquaredEuclideanDistanceFunction
Known implementations:
The number of clusters to find.
Method to choose the initial means.
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansInitialization
Default: de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.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 de.lmu.ifi.dbs.elki.distance.distancefunction.PrimitiveDistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.SquaredEuclideanDistanceFunction
Known implementations:
The number of clusters to find.
Method to choose the initial means.
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansInitialization
Default: de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.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 de.lmu.ifi.dbs.elki.distance.distancefunction.PrimitiveDistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.SquaredEuclideanDistanceFunction
Known implementations:
The number of clusters to find.
Method to choose the initial means.
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansInitialization
Default: de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.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 de.lmu.ifi.dbs.elki.distance.distancefunction.PrimitiveDistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction
Known implementations:
The number of clusters to find.
Method to choose the initial means.
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMedoidsInitialization
Default: de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.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 de.lmu.ifi.dbs.elki.distance.distancefunction.PrimitiveDistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction
Known implementations:
The number of clusters to find.
Method to choose the initial means.
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMedoidsInitialization
Default: de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.PAMInitialMeans
Known implementations:
The maximum number of iterations to do. 0 means no limit.
Default: 0
Dimension to use for clustering. For one-dimensional data, use 0.
Default: 0
Kernel function for density estimation.
Class Restriction: implements de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions.KernelDensityFunction
Default: de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions.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.
The number of intervals (units) in each dimension.
The density threshold for the selectivity of a unit, where the selectivity isthe 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
Use heuristics as described, thus using the FastDOC algorithm (not yet implemented).
Default: false
Random seed, for reproducible experiments.
Default: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
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
The maximum absolute variance along a coordinate axis.
Default: 0.01
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: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
Distance function to determine the neighbors for variance analysis.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.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: extends de.lmu.ifi.dbs.elki.distance.distancefunction.LocallyWeightedDistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.LocallyWeightedDistanceFunction
Known implementations:
The intrinsic dimensionality of the clusters to find.
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.subspace.DimensionSelectingSubspaceDistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.subspace.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.
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.
Kernel function to use.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.similarityfunction.SimilarityFunction
Default: de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel.PolynomialKernelFunction
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: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
Subspace dimensionality to search for.
The number of equi-depth grid ranges to use in each dimension.
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.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 de.lmu.ifi.dbs.elki.math.linearalgebra.pca.PCARunner
Default: de.lmu.ifi.dbs.elki.math.linearalgebra.pca.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 de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.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 de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction
Known implementations:
size of the D-neighborhood
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction
Known implementations:
Number of neighbors to get for DWOF score outlier detection.
Radius increase factor.
Default: 1.1
Kernel function to use.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.similarityfunction.SimilarityFunction
Default: de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel.PolynomialKernelFunction
Known implementations:
Number of nearest neighbors to use for ABOD.
Invert the value range to [0:1], with 1 being outliers instead of 0.
Default: false
cutoff
Default: 1.0E-7
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 de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.LPNormDistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction
Known implementations:
output of Top n or all elements
Default: TopN
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction
Known implementations:
k nearest neighbor
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction
Known implementations:
k nearest neighbor
Kernel function to use.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.similarityfunction.SimilarityFunction
Default: de.lmu.ifi.dbs.elki.distance.similarityfunction.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 de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction
Known implementations:
Number of neighbors to use for kNN graph.
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.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 de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction
Known implementations:
The number of nearest neighbors
The heuristic for finding reference points.
Class Restriction: implements de.lmu.ifi.dbs.elki.utilities.referencepoints.ReferencePointsHeuristic
Default: de.lmu.ifi.dbs.elki.utilities.referencepoints.GridBasedReferencePoints
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.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 de.lmu.ifi.dbs.elki.math.linearalgebra.pca.PCAFilteredRunner
Default: de.lmu.ifi.dbs.elki.math.linearalgebra.pca.PCAFilteredRunner
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.NumberVectorDistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.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: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
Scaling factor for averaging neighborhood
Default: 4
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.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 de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction
Known implementations:
The 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 de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction
Known implementations:
Number of neighbors to use for LDF.
Kernel to use for LDF.
Class Restriction: implements de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions.KernelDensityFunction
Default: de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions.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 de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.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 de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.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 de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.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 LOOP_SCORE.
Distance function to determine the reference set of an object.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.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 de.lmu.ifi.dbs.elki.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 de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.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 de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction
Known implementations:
The number of nearest neighbors of an object to be considered for computing its LOF_SCORE.
Kernel to use for kernel density LOF.
Class Restriction: implements de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions.KernelDensityFunction
Default: de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions.EpanechnikovKernelDensityFunction
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction
Known implementations:
The number of nearest neighbors 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 de.lmu.ifi.dbs.elki.utilities.scaling.ScalingFunction
Default: de.lmu.ifi.dbs.elki.utilities.scaling.IdentityScaling
Known implementations:
The number of nearest neighbors 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: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
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 de.lmu.ifi.dbs.elki.algorithm.outlier.OutlierAlgorithm
Default: de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LOF
Known implementations:
The statistical test that is used to calculate the deviation of two data samples
Class Restriction: implements de.lmu.ifi.dbs.elki.math.statistics.tests.GoodnessOfFitTest
Default: de.lmu.ifi.dbs.elki.math.statistics.tests.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: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
Algorithm to run.
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.outlier.OutlierAlgorithm
Known implementations:
Class to use as scaling function.
Class Restriction: implements de.lmu.ifi.dbs.elki.utilities.scaling.ScalingFunction
Known implementations:
Algorithm to run.
Voting strategy to use in the ensemble.
Class Restriction: implements de.lmu.ifi.dbs.elki.utilities.ensemble.EnsembleVoting
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction
Known implementations:
Significance niveau
k nearest neighbors to use
The neighborhood predicate to use in comparison step.
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood.NeighborSetPredicate$Factory
Known implementations:
The neighborhood predicate to use in comparison step.
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood.NeighborSetPredicate$Factory
Known implementations:
The neighborhood predicate to use in comparison step.
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood.NeighborSetPredicate$Factory
Known implementations:
The neighborhood predicate to use in comparison step.
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood.NeighborSetPredicate$Factory
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction
Known implementations:
Number of nearest neighbors to use.
Scaling exponent for value differences.
Default: 0.5
The damping parameter c.
The neighborhood predicate to use in comparison step.
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood.NeighborSetPredicate$Factory
Known implementations:
The neighborhood predicate to use in comparison step.
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood.NeighborSetPredicate$Factory
Known implementations:
The neighborhood predicate to use in comparison step.
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood.NeighborSetPredicate$Factory
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.PrimitiveDistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction
Known implementations:
The neighborhood predicate to use in comparison step.
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood.NeighborSetPredicate$Factory
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.PrimitiveDistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction
Known implementations:
The neighborhood predicate to use in comparison step.
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood.NeighborSetPredicate$Factory
Known implementations:
the percentile parameter
The inner neighborhood predicate to use.
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood.NeighborSetPredicate$Factory
Known implementations:
The number of steps allowed in the neighborhood graph.
The file listing the neighbors.
the number of neighbors
the distance function to use
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Known implementations:
The inner neighborhood predicate to use.
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood.NeighborSetPredicate$Factory
Known implementations:
The number of steps allowed in the neighborhood graph.
Parameter for the non-weighted neighborhood to use.
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood.NeighborSetPredicate$Factory
Known implementations:
Range value for OUTRES in 2 dimensions.
Subspace clustering algorithm to use.
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.SubspaceClusteringAlgorithm
Known implementations:
Alpha parameter for S1 score.
Default: 0.25
The similarity function used for the neighborhood set.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.similarityfunction.SimilarityFunction
Default: de.lmu.ifi.dbs.elki.distance.similarityfunction.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
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
Forcibly set the scales to the given range.
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction
Known implementations:
K to compute the average precision at.
Relative amount of object to sample.
Random seed for deterministic sampling.
Include the query object in the evaluation.
Default: false
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.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 de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.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 de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction
Known implementations:
Number of bins to use in the histogram
Default: 100
Port for the JSON web server to listen on.
Default: 8080
Bins per plane for HSV/HSB histogram. This will result in bpp ** 3 bins.
Bins per plane for HSV/HSB histogram. This will result in bpp ** 3 bins.
Bins per plane for RGB histogram. This will result in bpp ** 3 bins.
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 de.lmu.ifi.dbs.elki.math.geodesy.EarthModel
Default: de.lmu.ifi.dbs.elki.math.geodesy.SphericalVincentyEarthModel
Known implementations:
Earth model to use for projection. Default: spherical model.
Class Restriction: implements de.lmu.ifi.dbs.elki.math.geodesy.EarthModel
Default: de.lmu.ifi.dbs.elki.math.geodesy.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 de.lmu.ifi.dbs.elki.math.linearalgebra.randomprojections.RandomProjectionFamily
Default: de.lmu.ifi.dbs.elki.math.linearalgebra.randomprojections.AchlioptasRandomProjectionFamily
Known implementations:
Amount of dimensions to project to.
Database connection class.
Class Restriction: implements de.lmu.ifi.dbs.elki.datasource.DatabaseConnection
Default: de.lmu.ifi.dbs.elki.datasource.FileBasedDatabaseConnection
Known implementations:
Database indexes to add.
Database connection class.
Class Restriction: implements de.lmu.ifi.dbs.elki.datasource.DatabaseConnection
Default: de.lmu.ifi.dbs.elki.datasource.FileBasedDatabaseConnection
Known implementations:
Database indexes to add.
The filters to apply to the input data.
Bundle file to load the data from.
The name of the input file to be parsed.
The filters to apply to the input data.
Parser to provide the database.
Class Restriction: implements de.lmu.ifi.dbs.elki.datasource.parser.Parser
Default: de.lmu.ifi.dbs.elki.datasource.parser.NumberVectorLabelParser
Known implementations:
First integer DBID to generate.
Default: 0
Number of DBID to generate.
The filters to apply to the input data.
The data sources to join.
The name of the input file to be parsed.
Parser to provide the database.
Class Restriction: implements de.lmu.ifi.dbs.elki.datasource.parser.Parser
Default: de.lmu.ifi.dbs.elki.datasource.parser.NumberVectorLabelParser
Known implementations:
The filters to apply to the input data.
The generator specification file.
Factor for scaling the specified cluster sizes.
Default: 1.0
The random generator seed.
Default: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
Parser to provide the database.
Class Restriction: implements de.lmu.ifi.dbs.elki.datasource.parser.Parser
Default: de.lmu.ifi.dbs.elki.datasource.parser.NumberVectorLabelParser
Known implementations:
The filters to apply to the input data.
The filters to apply to the input data.
The data sources to join.
The filters to apply to the input data.
The data sources to join.
The filters to apply to the input data.
Dimensionality of the vectors to generate.
Database size to generate.
Seed for randomly generating vectors
Default: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
A list of the distribution estimators to try.
Default: [class de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.meta.BestFitEstimator]
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.
Norm (length function) to use for computing the vector length.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DoubleNorm
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction
Known implementations:
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*(#|//|;).*$
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 de.lmu.ifi.dbs.elki.data.NumberVector$Factory
Default: de.lmu.ifi.dbs.elki.data.DoubleVector.Factory
Known implementations:
Column separator pattern. The default assumes whitespace separated data.
Default: (\s+|\s*[,;]\s*)
Quotation characters. By default, both double and single ASCII quotes are accepted.
Default: "'
Ignore lines in the input file that satisfy this pattern.
Default: ^\s*(#|//|;).*$
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.
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.
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 de.lmu.ifi.dbs.elki.data.NumberVector$Factory
Default: de.lmu.ifi.dbs.elki.data.DoubleVector.Factory
Known implementations:
Column separator pattern. The default assumes whitespace separated data.
Default: \s+
Quotation characters. By default, both double and single ASCII quotes are accepted.
Default: "'
Ignore lines in the input file that satisfy this pattern.
Default: ^\s*(#|//|;).*$
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*(#|//|;).*$
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 de.lmu.ifi.dbs.elki.data.SparseNumberVector$Factory
Default: de.lmu.ifi.dbs.elki.data.SparseFloatVector.Factory
Known implementations:
Column separator pattern. The default assumes whitespace separated data.
Default: (\s+|\s*[,;]\s*)
Quotation characters. By default, both double and single ASCII quotes are accepted.
Default: "'
Ignore lines in the input file that satisfy this pattern.
Default: ^\s*(#|//|;).*$
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 de.lmu.ifi.dbs.elki.data.SparseNumberVector$Factory
Default: de.lmu.ifi.dbs.elki.data.SparseFloatVector.Factory
Known implementations:
Ignore lines in the input file that satisfy this pattern.
Default: ^\s*#.*$
Remove leading and trailing whitespace from each line.
Default: false
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 de.lmu.ifi.dbs.elki.data.SparseNumberVector$Factory
Default: de.lmu.ifi.dbs.elki.data.SparseFloatVector.Factory
Known implementations:
Normalize vectors to manhattan length 1 (convert term counts to term frequencies)
Default: false
Distance index to use.
Class Restriction: implements de.lmu.ifi.dbs.elki.index.preprocessed.LocalProjectionIndex$Factory
Default: de.lmu.ifi.dbs.elki.index.preprocessed.localpca.KNNQueryFilteredPCAIndex.Factory
Known implementations:
The number of nearest neighbors of an object to be considered for computing its reachability distance.
Base distance function to use.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction
Known implementations:
Distance index to use.
Class Restriction: implements de.lmu.ifi.dbs.elki.index.preprocessed.snn.SharedNearestNeighborIndex$Factory
Default: de.lmu.ifi.dbs.elki.index.preprocessed.snn.SharedNearestNeighborPreprocessor.Factory
Known implementations:
Similarity function to derive the distance between database objects from.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.similarityfunction.NormalizedSimilarityFunction
Default: de.lmu.ifi.dbs.elki.distance.similarityfunction.FractionalSharedNearestNeighborSimilarityFunction
Known implementations:
Similarity function to derive the distance between database objects from.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.similarityfunction.NormalizedSimilarityFunction
Default: de.lmu.ifi.dbs.elki.distance.similarityfunction.FractionalSharedNearestNeighborSimilarityFunction
Known implementations:
Similarity function to derive the distance between database objects from.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.similarityfunction.NormalizedSimilarityFunction
Default: de.lmu.ifi.dbs.elki.distance.similarityfunction.FractionalSharedNearestNeighborSimilarityFunction
Known implementations:
The dimensionality of the histogram in hue, saturation and brightness.
The dimensionality of the histogram in each color
Distance index to use.
Class Restriction: implements de.lmu.ifi.dbs.elki.index.preprocessed.localpca.FilteredLocalPCAIndex$Factory
Default: de.lmu.ifi.dbs.elki.index.preprocessed.localpca.KNNQueryFilteredPCAIndex.Factory
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
Distance index to use.
Class Restriction: implements de.lmu.ifi.dbs.elki.index.preprocessed.localpca.FilteredLocalPCAIndex$Factory
Default: de.lmu.ifi.dbs.elki.index.preprocessed.localpca.KNNQueryFilteredPCAIndex.Factory
Known implementations:
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 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 de.lmu.ifi.dbs.elki.distance.distancefunction.external.DistanceParser
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.external.NumberDistanceParser
Known implementations:
The name of the file containing the distance matrix.
Parser used to load the distance matrix.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.external.DistanceParser
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.external.NumberDistanceParser
Known implementations:
The dimension containing the latitude.
The dimension containing the longitude.
Earth model to use for projection. Default: spherical model.
Class Restriction: implements de.lmu.ifi.dbs.elki.math.geodesy.EarthModel
Default: de.lmu.ifi.dbs.elki.math.geodesy.SphericalVincentyEarthModel
Known implementations:
Earth model to use for projection. Default: spherical model.
Class Restriction: implements de.lmu.ifi.dbs.elki.math.geodesy.EarthModel
Default: de.lmu.ifi.dbs.elki.math.geodesy.SphericalVincentyEarthModel
Known implementations:
Earth model to use for projection. Default: spherical model.
Class Restriction: implements de.lmu.ifi.dbs.elki.math.geodesy.EarthModel
Default: de.lmu.ifi.dbs.elki.math.geodesy.SphericalVincentyEarthModel
Known implementations:
the degree of the L-P-Norm (positive number)
the degree of the L-P-Norm (positive number)
the degree of the L-P-Norm (positive number)
The maximum distance between two vectors with equal preference vectors before considering them as parallel.
Default: 0.001
an integer between 1 and the dimensionality of the feature space 1 specifying the dimension to be considered for distance computation.
The maximum distance between two vectors with equal preference vectors before considering them as parallel.
Default: 0.001
Distance index to use.
Class Restriction: implements de.lmu.ifi.dbs.elki.index.preprocessed.LocalProjectionIndex$Factory
Default: de.lmu.ifi.dbs.elki.index.preprocessed.localpca.KNNQueryFilteredPCAIndex.Factory
Known implementations:
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 degree 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 Edit Distance alignment (positive double value, 0 <= bandSize <= 1)
Default: 0.1
the band size for Edit Distance alignment (positive double value, 0 <= bandSize <= 1)
Default: 0.1
the delta parameter (similarity threshold) for EDR (positive number)
Default: 1.0
the band size for Edit Distance alignment (positive double value, 0 <= bandSize <= 1)
Default: 0.1
the g parameter ERP (positive number)
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 directionfor LCSS alignment (positive double value, 0 <= pEpsilon <= 1)
Default: 0.05
Preprocessor to use.
Class Restriction: implements de.lmu.ifi.dbs.elki.index.preprocessed.snn.SharedNearestNeighborIndex$Factory
Default: de.lmu.ifi.dbs.elki.index.preprocessed.snn.SharedNearestNeighborPreprocessor.Factory
Known implementations:
Preprocessor to use.
Class Restriction: implements de.lmu.ifi.dbs.elki.index.preprocessed.snn.SharedNearestNeighborIndex$Factory
Default: de.lmu.ifi.dbs.elki.index.preprocessed.snn.SharedNearestNeighborPreprocessor.Factory
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
Hash function family to use for LSH.
Class Restriction: implements de.lmu.ifi.dbs.elki.index.lsh.hashfamilies.LocalitySensitiveHashFunctionFamily
Known implementations:
Number of hash tables to use.
Number of hash buckets to use.
Default: 7919
The number of nearest neighbors of an object to be materialized.
the distance function to materialize the nearest neighbors
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.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 de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.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 de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.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 de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.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 de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.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 de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction
Known implementations:
The number of partitions to use for approximate kNN.
The random number generator seed.
Default: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
The number of nearest neighbors of an object to be materialized.
the distance function to materialize the nearest neighbors
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction
Known implementations:
The relative amount of objects to consider for kNN computations.
The random number seed.
Default: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
The number of nearest neighbors of an object to be materialized.
the distance function to materialize the nearest neighbors
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction
Known implementations:
The distance function used to select objects for running PCA.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction
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 distance function used to select objects for running PCA.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction
Known implementations:
The maximum radius of the neighborhood to be considered in the PCA.
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 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.
The maximum absolute variance along a coordinate axis.
Default: 0.01
number of nearest neighbors to consider (at least 1)
the distance function to asses the nearest neighbors
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction
Known implementations:
Distance function to determine the neighbors for variance analysis.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.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.
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
Distance function to determine the neighbors for variance analysis.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.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.
a double between 0 and 1 specifying the threshold for small Eigenvalues (default is delta = 0.01).
Default: 0.01
Earth model to use for projection. Default: spherical model.
Class Restriction: implements de.lmu.ifi.dbs.elki.math.geodesy.EarthModel
Default: de.lmu.ifi.dbs.elki.math.geodesy.SphericalVincentyEarthModel
Known implementations:
Index to use on the projected data.
Class Restriction: implements de.lmu.ifi.dbs.elki.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 de.lmu.ifi.dbs.elki.math.geodesy.EarthModel
Default: de.lmu.ifi.dbs.elki.math.geodesy.SphericalVincentyEarthModel
Known implementations:
Index to use on the projected data.
Class Restriction: implements de.lmu.ifi.dbs.elki.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 de.lmu.ifi.dbs.elki.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: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
Projection to use for the projected index.
Class Restriction: implements de.lmu.ifi.dbs.elki.data.projection.Projection
Known implementations:
Index to use on the projected data.
Class Restriction: implements de.lmu.ifi.dbs.elki.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
The pagefile factory for storing the index.
Class Restriction: implements de.lmu.ifi.dbs.elki.persistent.PageFileFactory
Default: de.lmu.ifi.dbs.elki.persistent.MemoryPageFileFactory
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction
Known implementations:
Split strategy to use for constructing the M-tree.
Class Restriction: extends de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.split.MTreeSplit
Default: de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.split.MMRadSplit
Known implementations:
Insertion strategy to use for constructing the M-tree.
Class Restriction: implements de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.insert.MTreeInsert
Default: de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.insert.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 de.lmu.ifi.dbs.elki.persistent.PageFileFactory
Default: de.lmu.ifi.dbs.elki.persistent.MemoryPageFileFactory
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction
Known implementations:
Split strategy to use for constructing the M-tree.
Class Restriction: extends de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.split.MTreeSplit
Default: de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.split.MMRadSplit
Known implementations:
Insertion strategy to use for constructing the M-tree.
Class Restriction: implements de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.insert.MTreeInsert
Default: de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.insert.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 de.lmu.ifi.dbs.elki.persistent.PageFileFactory
Default: de.lmu.ifi.dbs.elki.persistent.MemoryPageFileFactory
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction
Known implementations:
Split strategy to use for constructing the M-tree.
Class Restriction: extends de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.split.MTreeSplit
Default: de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.split.MMRadSplit
Known implementations:
Insertion strategy to use for constructing the M-tree.
Class Restriction: implements de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.insert.MTreeInsert
Default: de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.insert.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 de.lmu.ifi.dbs.elki.persistent.PageFileFactory
Default: de.lmu.ifi.dbs.elki.persistent.MemoryPageFileFactory
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction
Known implementations:
Split strategy to use for constructing the M-tree.
Class Restriction: extends de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.split.MTreeSplit
Default: de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.split.MMRadSplit
Known implementations:
Insertion strategy to use for constructing the M-tree.
Class Restriction: implements de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.insert.MTreeInsert
Default: de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.insert.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 de.lmu.ifi.dbs.elki.persistent.PageFileFactory
Default: de.lmu.ifi.dbs.elki.persistent.MemoryPageFileFactory
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction
Known implementations:
Split strategy to use for constructing the M-tree.
Class Restriction: extends de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.split.MTreeSplit
Default: de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.split.MMRadSplit
Known implementations:
Insertion strategy to use for constructing the M-tree.
Class Restriction: implements de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.insert.MTreeInsert
Default: de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.insert.MinimumEnlargementInsert
Known implementations:
The pagefile factory for storing the index.
Class Restriction: implements de.lmu.ifi.dbs.elki.persistent.PageFileFactory
Default: de.lmu.ifi.dbs.elki.persistent.MemoryPageFileFactory
Known implementations:
The strategy to use for object insertion.
Class Restriction: implements de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.insert.InsertionStrategy
Default: de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.insert.CombinedInsertionStrategy
Known implementations:
The strategy to use for node splitting.
Class Restriction: implements de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.split.SplitStrategy
Default: de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.split.TopologicalSplitter
Known implementations:
Minimum relative fill required for data pages.
Default: 0.4
The strategy to use for handling overflows.
Class Restriction: implements de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.overflow.OverflowTreatment
Known implementations:
The class to perform the bulk split with.
Class Restriction: implements de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.bulk.BulkSplit
Known implementations:
The pagefile factory for storing the index.
Class Restriction: implements de.lmu.ifi.dbs.elki.persistent.PageFileFactory
Default: de.lmu.ifi.dbs.elki.persistent.MemoryPageFileFactory
Known implementations:
The strategy to use for object insertion.
Class Restriction: implements de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.insert.InsertionStrategy
Default: de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.insert.CombinedInsertionStrategy
Known implementations:
The strategy to use for node splitting.
Class Restriction: implements de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.split.SplitStrategy
Default: de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.split.TopologicalSplitter
Known implementations:
Minimum relative fill required for data pages.
Default: 0.4
The strategy to use for handling overflows.
Class Restriction: implements de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.overflow.OverflowTreatment
Known implementations:
The class to perform the bulk split with.
Class Restriction: implements de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.bulk.BulkSplit
Known implementations:
Strategy for spatial sorting in bulk loading.
Class Restriction: implements de.lmu.ifi.dbs.elki.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 de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.insert.InsertionStrategy
Known implementations:
Insertion strategy for leaf nodes.
Class Restriction: implements de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.insert.InsertionStrategy
Default: de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.insert.LeastOverlapInsertionStrategy
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.
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 de.lmu.ifi.dbs.elki.math.statistics.tests.GoodnessOfFitTest
Default: de.lmu.ifi.dbs.elki.math.statistics.tests.KolmogorovSmirnovTest
Known implementations:
The random seed.
Default: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
A comma separated list of the class names of the filters to be used. The specified filters will be applied sequentially in the given order.
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 highesteigenvalues 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
Class used to compute the covariance matrix.
Class Restriction: implements de.lmu.ifi.dbs.elki.math.linearalgebra.pca.CovarianceMatrixBuilder
Default: de.lmu.ifi.dbs.elki.math.linearalgebra.pca.StandardCovarianceMatrixBuilder
Known implementations:
Filter class to determine the strong and weak eigenvectors.
Class Restriction: implements de.lmu.ifi.dbs.elki.math.linearalgebra.pca.EigenPairFilter
Default: de.lmu.ifi.dbs.elki.math.linearalgebra.pca.PercentageEigenPairFilter
Known implementations:
A constant big value to reset high eigenvalues.
Default: 1.0
A constant small value to reset low eigenvalues.
Default: 0.0
Class used to compute the covariance matrix.
Class Restriction: implements de.lmu.ifi.dbs.elki.math.linearalgebra.pca.CovarianceMatrixBuilder
Default: de.lmu.ifi.dbs.elki.math.linearalgebra.pca.StandardCovarianceMatrixBuilder
Known implementations:
Filter class to determine the strong and weak eigenvectors.
Class Restriction: implements de.lmu.ifi.dbs.elki.math.linearalgebra.pca.EigenPairFilter
Default: de.lmu.ifi.dbs.elki.math.linearalgebra.pca.PercentageEigenPairFilter
Known implementations:
A constant big value to reset high eigenvalues.
Default: 1.0
A constant small value to reset low eigenvalues.
Default: 0.0
Class used to compute the covariance matrix.
Class Restriction: implements de.lmu.ifi.dbs.elki.math.linearalgebra.pca.CovarianceMatrixBuilder
Default: de.lmu.ifi.dbs.elki.math.linearalgebra.pca.StandardCovarianceMatrixBuilder
Known implementations:
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 number of iterations to perform.
Default: 1000
Random seed (optional).
Default: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
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
Weight function to use in weighted PCA.
Class Restriction: implements de.lmu.ifi.dbs.elki.math.linearalgebra.pca.weightfunctions.WeightFunction
Default: de.lmu.ifi.dbs.elki.math.linearalgebra.pca.weightfunctions.ConstantWeight
Known implementations:
Random generation data source.
Default: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
Beta distribution alpha parameter
Beta distribution beta parameter
Random generation data source.
Default: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
Distribution location parameter
Cauchy distribution gamma/shape parameter.
Random generation data source.
Default: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
Chi distribution degrees of freedom parameter.
Random generation data source.
Default: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
Chi distribution degrees of freedom parameter.
Constant value.
Random generation data source.
Default: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
Distribution location parameter
Exponential distribution rate (lambda) parameter (inverse of scale).
Random generation data source.
Default: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
Distribution location parameter
Distribution scale parameter
Exponential distribution rate (lambda) parameter (inverse of scale).
Random generation data source.
Default: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
Gamma distribution k = alpha parameter.
Gamma distribution theta = 1/beta parameter.
Random generation data source.
Default: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
Distribution location parameter
Distribution scale parameter
Distribution shape parameter
Random generation data source.
Default: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
Distribution location parameter
Distribution scale parameter
Distribution shape parameter
Random generation data source.
Default: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
Distribution location parameter
Distribution scale parameter
Distribution shape parameter
Random generation data source.
Default: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
Distribution location parameter
Distribution shape parameter
Random generation data source.
Default: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
Minimum value of distribution.
Maximum value of distribution.
Random generation data source.
Default: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
Distribution location parameter
Distribution scale parameter
First shape parameter of kappa distribution.
Second shape parameter of kappa distribution.
Random generation data source.
Default: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
Distribution location parameter
Laplace distribution rate (lambda) parameter (inverse of scale).
Random generation data source.
Default: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
Gamma distribution k = alpha parameter.
Gamma distribution theta = 1/beta parameter.
Shift offset parameter.
Random generation data source.
Default: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
Gamma distribution k = alpha parameter.
Gamma distribution theta = 1/beta parameter.
Shift offset parameter.
Random generation data source.
Default: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
Distribution scale parameter
Distribution shape parameter
Random generation data source.
Default: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
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: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
Distribution scale parameter
Distribution location parameter
Random generation data source.
Default: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
Distribution location parameter
Distribution scale parameter
Random generation data source.
Default: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
Number of trials.
Success probability.
Random generation data source.
Default: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
Distribution location parameter
Default: 0.0
Distribution scale parameter
Random generation data source.
Default: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
Distribution location parameter
Distribution scale parameter
Skew of the distribution.
Random generation data source.
Default: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
Degrees of freedom.
Random generation data source.
Default: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
Minimum value of distribution.
Maximum value of distribution.
Random generation data source.
Default: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
Distribution location parameter
Distribution shape parameter
Random generation data source.
Default: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
Distribution location parameter
Default: 0.0
Distribution scale parameter
Distribution shape parameter
Filename the KMZ file (compressed KML) is written to.
Additional scaling function for KML colorization.
Class Restriction: implements de.lmu.ifi.dbs.elki.utilities.scaling.outlier.OutlierScalingFunction
Default: de.lmu.ifi.dbs.elki.utilities.scaling.outlier.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
The number of samples to draw.
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
The output folder.
The width/heigh ratio of the output.
Default: 1.33
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.
Default: ^\Qde.lmu.ifi.dbs.elki.visualization\E\..*
Title to use for visualization window.
Embed visualizers in a single window, not using thumbnails and detail views.
Default: false
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: 50
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
Visualize mean-based clusters using stars.
Default: false
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 de.lmu.ifi.dbs.elki.utilities.scaling.outlier.OutlierScalingFunction
Known implementations:
Use wireframe style for selection ranges.
Default: false
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 to evaluate the results with.
Default: [class de.lmu.ifi.dbs.elki.evaluation.AutomaticEvaluation]
Database class.
Class Restriction: implements de.lmu.ifi.dbs.elki.database.Database
Default: de.lmu.ifi.dbs.elki.database.StaticArrayDatabase
Known implementations:
Enable verbose messages.
Default: false
Parameter to enable debugging for particular packages.
Result handler class.
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction
Known implementations:
The minimum number of clusters to extract (there may be more clusters when tied).
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction
Known implementations:
The minimum number of clusters to extract (there may be more clusters when tied).
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction
Known implementations:
The minimum number of clusters to extract (there may be more clusters when tied).
Parameter to choose the linkage strategy.
Default: WARD
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction
Known implementations:
Parameter to choose the linkage strategy.
Default: WARD
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.PrimitiveDoubleDistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.SquaredEuclideanDistanceFunction
Known implementations:
The number of clusters to find.
Method to choose the initial means.
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansInitialization
Default: de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.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 de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.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 de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction
Known implementations:
Number of neighbors to use for kNN graph.