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.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.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.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.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.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.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
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.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:
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.EuclideanDistanceFunction
Known implementations:
The maximum number of clusters to extract.
The minimum SNN density.
Threshold for minimum number of points in the epsilon-SNN-neighborhood of a point.
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.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.
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.
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:
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.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.KMeansInitialization
Default: de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.RandomlyGeneratedInitialMeans
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.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.KMeansInitialization
Default: de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.RandomlyGeneratedInitialMeans
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.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.KMeansInitialization
Default: de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.RandomlyGeneratedInitialMeans
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.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.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
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
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 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.
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.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: extends de.lmu.ifi.dbs.elki.distance.distancefunction.subspace.AbstractDimensionsSelectingDoubleDistanceFunction
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.
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.EuclideanDistanceFunction
Known implementations:
Parameter k for kNN queries.
Default: 30
Sample size to enable fast mode.
Kernel function to use.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.similarityfunction.PrimitiveSimilarityFunction
Default: de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel.PolynomialKernelFunction
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.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.
Scaling factor for averaging neighborhood
Default: 4
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.
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.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.EuclideanDistanceFunction
Known implementations:
size of the D-neighborhood
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.LPNormDistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.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.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.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.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.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.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.EuclideanDistanceFunction
Known implementations:
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:
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.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.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.EuclideanDistanceFunction
Known implementations:
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.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:
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.
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
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.
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:
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.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$Factory
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
Number of nearest neighbors to use.
Scaling exponent for value differences.
Default: 0.5
The damping parameter c.
The neighborhood predicate to use in comparison step.
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood.NeighborSetPredicate$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.EuclideanDistanceFunction
Known implementations:
The neighborhood predicate to use in comparison step.
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood.NeighborSetPredicate$Factory
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.PrimitiveDistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
The neighborhood predicate to use in comparison step.
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood.NeighborSetPredicate$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
Label pattern to match outliers.
Default: .*(Outlier|Noise).*
Expected amount of outliers, for making the scores more intuitive.
Default: 0.01
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.EuclideanDistanceFunction
Known implementations:
K to compute the average precision at.
Relative amount of object to sample.
Random seed for deterministic sampling.
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.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.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.EuclideanDistanceFunction
Known implementations:
Number of bins to use in the histogram
Default: 100
Enable verbose messages.
Default: false
Class that is used to generate a color histogram.
Class Restriction: implements de.lmu.ifi.dbs.elki.data.images.ComputeColorHistogram
Default: de.lmu.ifi.dbs.elki.data.images.ComputeNaiveRGBColorHistogram
Known implementations:
Input image file for color histogram.
Input mask image file.
Enable verbose messages.
Default: false
the file to write the generated data set into, if the file already exists, the generated points will be appended to this file.
Enable verbose messages.
Default: false
Enable verbose messages.
Default: false
Database connection class.
Class Restriction: implements de.lmu.ifi.dbs.elki.database.Database
Default: de.lmu.ifi.dbs.elki.database.StaticArrayDatabase
Known implementations:
Distance function to cache.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Known implementations:
File name of the disk cache to create.
Enable verbose messages.
Default: false
Database connection class.
Class Restriction: implements de.lmu.ifi.dbs.elki.database.Database
Default: de.lmu.ifi.dbs.elki.database.StaticArrayDatabase
Known implementations:
Distance function to cache.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Known implementations:
File name of the disk cache to create.
Enable verbose messages.
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.EuclideanDistanceFunction
Known implementations:
Step size for k.
Minimum value for k.
Maximum value for k.
File to output the resulting score vectors to.
Enable verbose messages.
Default: false
Enable verbose messages.
Default: false
Port for the JSON web server to listen on.
Default: 8080
Enable verbose messages.
Default: false
Database connection class.
Class Restriction: implements de.lmu.ifi.dbs.elki.database.Database
Default: de.lmu.ifi.dbs.elki.database.StaticArrayDatabase
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.EuclideanDistanceFunction
Known implementations:
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.
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 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:
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.
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
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.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 character. The default is to use a double quote.
Default: "
Column separator pattern. The default assumes whitespace separated data.
Default: (\s+|\s*[,;]\s*)
Quotation character. The default is to use a double quote.
Default: "
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 character. The default is to use a double quote.
Default: "
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 character. The default is to use a double quote.
Default: "
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
Default: de.lmu.ifi.dbs.elki.data.DoubleVector
Known implementations:
Column separator pattern. The default assumes whitespace separated data.
Default: (\s+|\s*[,;]\s*)
Quotation character. The default is to use a double quote.
Default: "
Column separator pattern. The default assumes whitespace separated data.
Default: (\s+|\s*[,;]\s*)
Quotation character. The default is to use a double quote.
Default: "
Column separator pattern. The default assumes whitespace separated data.
Default: (\s+|\s*[,;]\s*)
Quotation character. The default is to use a double quote.
Default: "
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
Default: de.lmu.ifi.dbs.elki.data.SparseFloatVector
Known implementations:
Column separator pattern. The default assumes whitespace separated data.
Default: (\s+|\s*[,;]\s*)
Quotation character. The default is to use a double quote.
Default: "
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
Default: de.lmu.ifi.dbs.elki.data.SparseFloatVector
Known implementations:
Column separator pattern. The default assumes whitespace separated data.
Default: (\s+|\s*[,;]\s*)
Quotation character. The default is to use a double quote.
Default: "
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
Default: de.lmu.ifi.dbs.elki.data.DoubleVector
Known implementations:
Normalize vectors to manhattan length 1 (convert term counts to term frequencies)
Default: false
the degree of the L-P-Norm (positive number)
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.EuclideanDistanceFunction
Known implementations:
Distance index to use.
Class Restriction: implements de.lmu.ifi.dbs.elki.index.preprocessed.snn.SharedNearestNeighborIndex$Factory
Default: de.lmu.ifi.dbs.elki.index.preprocessed.snn.SharedNearestNeighborPreprocessor.Factory
Known implementations:
the degree of the L-P-Norm (positive number)
Similarity function to derive the distance between database objects from.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.similarityfunction.NormalizedSimilarityFunction
Default: de.lmu.ifi.dbs.elki.distance.similarityfunction.FractionalSharedNearestNeighborSimilarityFunction
Known implementations:
Similarity function to derive the distance between database objects from.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.similarityfunction.NormalizedSimilarityFunction
Default: de.lmu.ifi.dbs.elki.distance.similarityfunction.FractionalSharedNearestNeighborSimilarityFunction
Known implementations:
Similarity function to derive the distance between database objects from.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.similarityfunction.NormalizedSimilarityFunction
Default: de.lmu.ifi.dbs.elki.distance.similarityfunction.FractionalSharedNearestNeighborSimilarityFunction
Known implementations:
The dimensionality of the histogram in hue, saturation and brightness.
The dimensionality of the histogram in each color
Distance index to use.
Class Restriction: implements de.lmu.ifi.dbs.elki.index.preprocessed.localpca.FilteredLocalPCAIndex$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$Factory
Default: de.lmu.ifi.dbs.elki.index.preprocessed.localpca.KNNQueryFilteredPCAIndex.Factory
Known implementations:
Threshold of a distance between a vector q and a given space that indicates that q adds a new dimension to the space.
Default: 0.25
The name of the file containing the distance matrix.
The name of the file containing the distance matrix.
The name of the file containing the distance matrix.
Parser used to load the distance matrix.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.external.DistanceParser
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.external.NumberDistanceParser
Known implementations:
The name of the file containing the distance matrix.
Parser used to load the distance matrix.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.external.DistanceParser
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.external.NumberDistanceParser
Known implementations:
The dimension containing the latitude.
The dimension containing the longitude.
The maximum distance between two vectors with equal preference vectors before considering them as parallel.
Default: 0.001
an integer between 1 and the dimensionality of the feature space 1 specifying the dimension to be considered for distance computation.
The maximum distance between two vectors with equal preference vectors before considering them as parallel.
Default: 0.001
Distance index to use.
Class Restriction: implements de.lmu.ifi.dbs.elki.index.preprocessed.LocalProjectionIndex$Factory
Default: de.lmu.ifi.dbs.elki.index.preprocessed.localpca.KNNQueryFilteredPCAIndex.Factory
Known implementations:
a comma separated array of integer values, where 1 <= 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 1 <= 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 1 <= 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:
The max degree of theFooKernelFunction. Default: 2
Default: 2
The degree of the polynomial kernel function. Default: 2.0
Default: 2.0
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.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.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.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.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.EuclideanDistanceFunction
Known implementations:
The number of partitions to use for approximate kNN.
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.EuclideanDistanceFunction
Known implementations:
The relative amount of objects to consider for kNN computations.
The random number seed.
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.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.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.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.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.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.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
The name of the file storing the index. If this parameter is not set the index is hold in the main memory.
The size of a page in bytes.
Default: 4000
The size of the cache in bytes.
Default: 2147483647
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.EuclideanDistanceFunction
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 name of the file storing the index. If this parameter is not set the index is hold in the main memory.
The size of a page in bytes.
Default: 4000
The size of the cache in bytes.
Default: 2147483647
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.EuclideanDistanceFunction
Known implementations:
positive integer specifying the maximum number k of reverse k nearest neighbors to be supported.
The name of the file storing the index. If this parameter is not set the index is hold in the main memory.
The size of a page in bytes.
Default: 4000
The size of the cache in bytes.
Default: 2147483647
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.EuclideanDistanceFunction
Known implementations:
Specifies the maximal number k of reverse k nearest neighbors to be supported.
The name of the file storing the index. If this parameter is not set the index is hold in the main memory.
The size of a page in bytes.
Default: 4000
The size of the cache in bytes.
Default: 2147483647
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.EuclideanDistanceFunction
Known implementations:
Specifies the maximal number k of reverse k nearest neighbors to be supported.
The name of the file storing the index. If this parameter is not set the index is hold in the main memory.
The size of a page in bytes.
Default: 4000
The size of the cache in bytes.
Default: 2147483647
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.EuclideanDistanceFunction
Known implementations:
The name of the file storing the index. If this parameter is not set the index is hold in the main memory.
The size of a page in bytes.
Default: 4000
The size of the cache in bytes.
Default: 2147483647
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 name of the file storing the index. If this parameter is not set the index is hold in the main memory.
The size of a page in bytes.
Default: 4000
The size of the cache in bytes.
Default: 2147483647
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.
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 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:
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
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.
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
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.
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.EuclideanDistanceFunction
Known implementations:
Number of neighbors to get for stddev based outlier detection.