Kernel function to use.
Class Restriction: implements distance.similarityfunction.SimilarityFunction
Default: kernel.PolynomialKernelFunction
Known implementations:
Parameter for:
Number of top outliers to compute.
Parameter for:
Frequency of zeros in the projection matrix.
Default: 3.0
Parameter for:
Similarity function to derive the distance between database objects from.
Class Restriction: implements distance.similarityfunction.NormalizedSimilarityFunction
Known implementations:
Parameter for:
Algorithm to run.
Class Restriction: implements algorithm.Algorithm
Known implementations:
Parameter for:
Class Restriction: implements algorithm.clustering.hierarchical.HierarchicalClusteringAlgorithm
Class Restriction: implements algorithm.clustering.hierarchical.HierarchicalClusteringAlgorithm
Class Restriction: implements algorithm.clustering.hierarchical.HierarchicalClusteringAlgorithm
Class Restriction: implements algorithm.clustering.hierarchical.HierarchicalClusteringAlgorithm
Class Restriction: implements algorithm.clustering.hierarchical.HierarchicalClusteringAlgorithm
Class Restriction: implements algorithm.clustering.kmeans.KMeans
Default: KMeansHamerly
Class Restriction: implements algorithm.clustering.ClusteringAlgorithm
Class Restriction: implements algorithm.outlier.OutlierAlgorithm
Class Restriction: implements algorithm.outlier.OutlierAlgorithm
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Parameter for:
Class Restriction: implements distance.distancefunction.PrimitiveDistanceFunction
Class Restriction: implements distance.distancefunction.PrimitiveDistanceFunction
Class Restriction: extends distance.distancefunction.minkowski.LPNormDistanceFunction
Class Restriction: implements distance.distancefunction.PrimitiveDistanceFunction
Class Restriction: implements distance.distancefunction.PrimitiveDistanceFunction
Class Restriction: implements distance.distancefunction.PrimitiveDistanceFunction
Class Restriction: implements distance.distancefunction.PrimitiveDistanceFunction
Class Restriction: implements distance.distancefunction.PrimitiveDistanceFunction
Class Restriction: implements distance.distancefunction.PrimitiveDistanceFunction
Class Restriction: implements distance.distancefunction.PrimitiveDistanceFunction
Class Restriction: implements distance.distancefunction.PrimitiveDistanceFunction
Class Restriction: implements distance.distancefunction.PrimitiveDistanceFunction
Class Restriction: implements distance.distancefunction.PrimitiveDistanceFunction
Class Restriction: implements distance.distancefunction.PrimitiveDistanceFunction
Class Restriction: implements distance.distancefunction.PrimitiveDistanceFunction
Class Restriction: implements distance.distancefunction.NumberVectorDistanceFunction
Class Restriction: extends distance.distancefunction.minkowski.LPNormDistanceFunction
Class Restriction: implements distance.distancefunction.NumberVectorDistanceFunction
Class Restriction: implements distance.distancefunction.NumberVectorDistanceFunction
Class Restriction: implements distance.distancefunction.PrimitiveDistanceFunction
Class Restriction: implements distance.distancefunction.PrimitiveDistanceFunction
Class Restriction: implements distance.distancefunction.NumberVectorDistanceFunction
Class Restriction: implements distance.distancefunction.NumberVectorDistanceFunction
Class Restriction: implements distance.distancefunction.NumberVectorDistanceFunction
Number of stable iterations for convergence.
Default: 15
Parameter for:
Distance function to use.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.SquaredEuclideanDistanceFunction
Known implementations:
Parameter for:
Similarity matrix initialization..
Class Restriction: implements algorithm.clustering.affinitypropagation.AffinityPropagationInitialization
Default: DistanceBasedInitializationWithMedian
Known implementations:
Parameter for:
Dampening factor lambda. Usually 0.5 to 1.
Default: 0.5
Parameter for:
Maximum number of iterations.
Default: 1000
Parameter for:
Quantile to use for diagonal entries.
Default: 0.5
Parameter for:
Similarity function to use.
Class Restriction: implements distance.similarityfunction.SimilarityFunction
Default: kernel.LinearKernelFunction
Known implementations:
Parameter for:
Pattern to recognize class label attributes.
Default: (Class|Class-?Label)
Parameter for:
Pattern to recognize external ID attributes.
Default: (External-?ID)
Parameter for:
Algorithm to be used for frequent itemset mining.
Class Restriction: extends algorithm.itemsetmining.AbstractFrequentItemsetAlgorithm
Default: FPGrowth
Known implementations:
Parameter for:
Interestingness measure to be used
Class Restriction: implements algorithm.itemsetmining.associationrules.interest.InterestingnessMeasure
Default: Confidence
Known implementations:
Parameter for:
Maximum threshold for specified interstingness measure
Parameter for:
Minimum threshold for specified interstingness measure
Parameter for:
Filter for selecting eigenvectors during autotuning PCA.
Class Restriction: implements math.linearalgebra.pca.filter.EigenPairFilter
Default: SignificantEigenPairFilter
Known implementations:
Parameter for:
Include the query object in the evaluation.
Default: false
Parameter for:
K to compute the average precision at.
Parameter for:
Relative amount of object to sample.
Parameter for:
Random seed for deterministic sampling.
Default: use global random seed
Parameter for:
Scale the data space extension by the given factor.
Default: 1.0
Parameter for:
Subspace dimensionality to search for.
Parameter for:
Population size for evolutionary algorithm.
Parameter for:
The number of equi-depth grid ranges to use in each dimension.
Parameter for:
The random number generator seed.
Default: use global random seed
Parameter for:
KMeans variant
Class Restriction: implements algorithm.clustering.kmeans.KMeans
Default: BestOfMultipleKMeans
Known implementations:
Parameter for:
Half-transparent filling of bubbles.
Default: false
Parameter for:
Additional scaling function for bubbles.
Class Restriction: implements utilities.scaling.ScalingFunction
Default: outlier.OutlierLinearScaling
Known implementations:
Parameter for:
Bundle file to load the data from.
Parameter for:
Flag to indicate that only subspaces with large coverage (i.e. the fraction of the database that is covered by the dense units) are selected, the rest will be pruned.
Default: false
Parameter for:
Pattern to recognize noise classes by their label.
Parameter for:
Pattern to recognize noise models by their label.
Parameter for:
The random generator seed.
Default: use global random seed
Parameter for:
Pattern to specify clusters to reassign.
Parameter for:
Factor for scaling the specified cluster sizes.
Default: 1.0
Parameter for:
The generator specification file.
Parameter for:
Distance function to use for computing the c-index.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Parameter for:
Control how noise should be treated.
Default: TREAT_NOISE_AS_SINGLETONS
Parameter for:
Inclusion threshold for canopy clustering. t1 >= t2!
Parameter for:
Removal threshold for canopy clustering. t1 >= t2!
Parameter for:
Flag to indicate that an adjustment of the applied heuristic for choosing an interval is performed after an interval is selected.
Default: false
Parameter for:
The maximum jitter for distance values.
Parameter for:
The maximum level for splitting the hypercube.
Parameter for:
The minimum dimensionality of the subspaces to be found.
Default: 1
Parameter for:
Threshold for minimum number of points in a cluster.
Parameter for:
Clustering algorithm to use for detecting outliers.
Class Restriction: implements algorithm.clustering.ClusteringAlgorithm
Default: kmeans.KMeansSort
Known implementations:
Parameter for:
The ratio of the data that should be included in the large clusters
Parameter for:
The ratio of the data that should be included in the large clusters
Parameter for:
Absorption criterion to use.
Class Restriction: implements algorithm.clustering.hierarchical.birch.BIRCHAbsorptionCriterion
Default: DiameterCriterion
Known implementations:
Parameter for:
Maximum branching factor of the CF-Tree
Default: 64
Parameter for:
Distance function to use for node assignment.
Class Restriction: implements algorithm.clustering.hierarchical.birch.BIRCHDistance
Default: VarianceIncreaseDistance
Known implementations:
Parameter for:
Maximum number of leaves (if less than 1, the values is assumed to be relative)
Default: 0.05
Parameter for:
Threshold for adding points to existing nodes in the CF-Tree.
Parameter for:
Confidence level to use with bootstrap sampling.
Default: 0.9975
Parameter for:
Number of samples to draw for bootstrapping the confidence estimate.
Default: 1000
Parameter for:
Random generator seed for bootstrap sampling.
Default: use global random seed
Parameter for:
Parameter for multiple node deletion to accelerate the algorithm.
Default: 1.0
Parameter for:
Threshold value to determine the maximal acceptable score (mean squared residue) of a bicluster.
Parameter for:
The number of biclusters to be found.
Default: 1
Parameter for:
Distribution of replacement values when masking found clusters.
Class Restriction: implements math.statistics.distribution.Distribution
Default: UniformDistribution
Known implementations:
Parameter for:
Draw independent samples (default is to keep the previous best medoids in the sample).
Default: false
Parameter for:
Number of samples (restarts) to run.
Default: 2
Parameter for:
Number of tries to find a neighbor.
Default: 0.0125
Parameter for:
Random generator seed.
Default: use global random seed
Parameter for:
Number of samples (iterations) to run.
Default: 5
Parameter for:
The size of the sample.
Default: 40.0
Parameter for:
Random generator seed.
Default: use global random seed
Parameter for:
Class label to use for negative instances.
Default: negative
Parameter for:
Regular expression to identify positive objects.
Parameter for:
Class label to use for positive instances.
Default: positive
Parameter for:
Maximum value to allow.
Parameter for:
Minimum value to allow.
Parameter for:
Flag to indicate that only subspaces with large coverage (i.e. the fraction of the database that is covered by the dense units) are selected, the rest will be pruned.
Default: false
Parameter for:
The density threshold for the selectivity of a unit, where the selectivity is the fraction of total feature vectors contained in this unit.
Parameter for:
The number of intervals (units) in each dimension.
Parameter for:
Parameter to override the clustering label, mostly to give a more descriptive label.
Parameter for:
Output file name. When not given, the result will be written to stdout.
Parameter for:
Always append to the output file.
Default: false
Parameter for:
The number of neighbors (not including the query object) to use for computing the COF score.
Parameter for:
number of bins
Default: 50
Parameter for:
Class label for the 'positive' class.
Parameter for:
Class label for the 'positive' class.
Parameter for:
Class to use as scaling function.
Class Restriction: implements utilities.scaling.ScalingFunction
Default: IdentityScaling
Known implementations:
Parameter for:
Class to use as scaling function.
Class Restriction: implements utilities.scaling.ScalingFunction
Default: IdentityScaling
Known implementations:
Parameter for:
Control how noise should be treated.
Default: TREAT_NOISE_AS_SINGLETONS
Parameter for:
Distance function to use for measuring concordant and discordant pairs.
Class Restriction: implements distance.distancefunction.PrimitiveDistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Parameter for:
The assumed distribution of squared distances. ChiSquared is faster, Gamma expected to be more accurate but could also overfit.
Default: GAMMA
Parameter for:
Expected share of outliers. Only affect score normalization.
Default: 0.001
Parameter for:
The number of nearest neighbors of an object to be considered for computing its COP_SCORE.
Parameter for:
The number of nearest neighbors of an object to be considered for computing its COP_SCORE.
Parameter for:
Include COP models (error vectors) in output. This needs more memory.
Default: false
Parameter for:
The class to compute (filtered) PCA.
Class Restriction: extends math.linearalgebra.pca.PCARunner
Default: PCARunner
Known implementations:
Parameter for:
The class to compute (filtered) PCA.
Class Restriction: extends math.linearalgebra.pca.PCARunner
Default: PCARunner
Known implementations:
Parameter for:
Number of neighbors to use for PCA.
Parameter for:
Phi parameter, expected rate of outliers. Set to 0 to use raw CDF values.
Parameter for:
Distance function to determine the distance between objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Known implementations:
Parameter for:
Expansion rate of the tree (Default: 1.3).
Default: 1.3
Parameter for:
Truncate tree when branches have less than this number of instances.
Default: 10
Parameter for:
Distance function to use for computing the davies-bouldin index.
Class Restriction: implements distance.distancefunction.NumberVectorDistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Parameter for:
Control how noise should be treated.
Default: TREAT_NOISE_AS_SINGLETONS
Parameter for:
Database class.
Class Restriction: implements database.Database
Default: StaticArrayDatabase
Known implementations:
Parameter for:
Database indexes to add.
Class Restriction: implements index.IndexFactory
Known implementations:
Parameter for:
Database connection class.
Class Restriction: implements datasource.DatabaseConnection
Default: FileBasedDatabaseConnection
Known implementations:
Parameter for:
Class label class to use.
Class Restriction: extends data.ClassLabel
Default: SimpleClassLabel
Known implementations:
Parameter for:
The index of the label to be used as class label. The first label is 0, negative indexes are relative to the end.
Parameter for:
Dimensionality of the vectors to generate.
Parameter for:
The index of the label to be used as external Id. The first label is 0; negative indexes are relative to the end.
Parameter for:
The filters to apply to the input data.
Class Restriction: implements datasource.filter.ObjectFilter
Known implementations:
Parameter for:
Seed for randomly generating vectors
Default: use global random seed
Parameter for:
The name of the input file to be parsed.
Parameter for:
Input stream to read. Defaults to standard input.
Class Restriction: extends java.io.InputStream
Parameter for:
Parser to provide the database.
Class Restriction: implements datasource.parser.Parser
Default: NumberVectorLabelParser
Known implementations:
Parameter for:
Database size to generate.
Parameter for:
Object ID to start counting with
Default: 0
Parameter for:
Distance function to use for computing the dbcv.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Parameter for:
size of the D-neighborhood
Parameter for:
minimum fraction of objects that must be outside the D-neighborhood of an outlier
Parameter for:
The maximum radius of the neighborhood to be considered.
Parameter for:
Threshold for minimum number of points in the epsilon-neighborhood of a point. The suggested value is '2 * dim - 1'.
Parameter for:
Threshold for minimum number of points within a cluster.
Parameter for:
Positioning logic for dendrograms.
Default: HALF_POS
Parameter for:
Drawing style for dendrograms.
Default: RECTANGULAR
Parameter for:
Threshold for output accuracy fraction digits.
Default: 4
Parameter for:
Flag to use random sample (use knn query around centroid, if flag is not set).
Default: false
Parameter for:
Threshold for the size of the random sample to use. Default value is size of the complete dataset.
Parameter for:
an integer between 1 and the dimensionality of the feature space 1 specifying the dimension to be considered for distance computation.
Parameter for:
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
Parameter for:
The maximum radius of the neighborhood to be considered in each dimension for determination of the preference vector.
Default: 0.001
Parameter for:
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.
Parameter for:
The minimum number of points as a smoothing factor to avoid the single-link-effekt.
Default: 1
Parameter for:
The strategy for determination of the preference vector, available strategies are: [APRIORI| MAX_INTERSECTION](default is MAX_INTERSECTION)
Default: MAX_INTERSECTION
Parameter for:
Default distance to use for undefined values.
Default: Infinity
Parameter for:
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.
Parameter for:
The dimension containing the latitude.
Parameter for:
The dimension containing the longitude.
Parameter for:
The name of the file containing the distance matrix.
Parameter for:
The name of the file containing the distance matrix.
Parameter for:
Parser used to load the distance matrix.
Class Restriction: implements distance.distancefunction.external.DistanceParser
Default: AsciiDistanceParser
Known implementations:
Parameter for:
Weights to use for the distance function.
Parameter for:
Distance index to use.
Class Restriction: implements index.preprocessed.snn.SharedNearestNeighborIndex
Default: SharedNearestNeighborPreprocessor
Known implementations:
Parameter for:
Beta distribution alpha parameter
Parameter for:
Beta distribution beta parameter
Parameter for:
Cauchy distribution gamma/shape parameter.
Parameter for:
Chi distribution degrees of freedom parameter.
Parameter for:
Constant value.
Parameter for:
Shift offset parameter.
Default: 0.0
Parameter for:
Exponential distribution rate (lambda) parameter (inverse of scale).
Parameter for:
Gamma distribution k = alpha parameter.
Parameter for:
Gamma distribution theta = 1/beta parameter.
Parameter for:
First shape parameter of kappa distribution.
Parameter for:
Second shape parameter of kappa distribution.
Parameter for:
Laplace distribution rate (lambda) parameter (inverse of scale).
Parameter for:
Distribution location parameter
Parameter for:
Default: 0.0
Default: 0.0
Default: 0.0
Default: 0.0
Default: 0.0
Shift offset parameter.
Parameter for:
Mean of the distribution before logscaling.
Parameter for:
Standard deviation of the distribution before logscaling.
Parameter for:
Shifting offset, so the distribution does not begin at 0.
Default: 0.0
Parameter for:
Maximum value of distribution.
Parameter for:
Minimum value of distribution.
Parameter for:
Number of trials.
Parameter for:
Success probability.
Parameter for:
Random generation data source.
Default: use global random seed
Parameter for:
Distribution scale parameter
Parameter for:
Default: 1.0
Distribution shape parameter
Parameter for:
Skew of the distribution.
Parameter for:
Degrees of freedom.
Parameter for:
Ignore zero distances, beneficial for data sets with many duplicates.
Default: false
Parameter for:
Quantile to compute.
Default: 0.1
Parameter for:
Number of distances to compute, either relative (values less than 1), or absolute.
Parameter for:
Random generator seed.
Default: use global random seed
Parameter for:
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
Parameter for:
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
Parameter for:
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
Parameter for:
Minimum relative density for a set of points to be considered a cluster (|C|>=doc.alpha*|S|).
Default: 0.2
Parameter for:
Preference of cluster size versus number of relevant dimensions (higher value means higher priority on larger clusters).
Default: 0.8
Parameter for:
Random seed, for reproducible experiments.
Default: use global random seed
Parameter for:
Maximum extent of scattering of points along a single attribute for the attribute to be considered relevant.
Default: 0.05
Parameter for:
Radius increase factor.
Default: 1.1
Parameter for:
Number of neighbors to get for DWOF score outlier detection.
Parameter for:
The band size for time series alignment. By default, no constraint is used. If the value is larger than 0, it will be considered absolute, otherwise relative to the longer sequence. Note that 0 does not make sense: use Euclidean distance then instead.
Parameter for:
the delta parameter (similarity threshold) for EDR (positive number)
Default: 1.0
Parameter for:
Method to choose the initial cluster centers.
Class Restriction: implements algorithm.clustering.kmeans.initialization.KMeansInitialization
Default: RandomlyChosenInitialMeans
Known implementations:
Parameter for:
The termination criterion for maximization of E(M): E(M) - E(M') < em.delta
Default: 1.0E-7
Parameter for:
The number of clusters to find.
Parameter for:
Regularization factor for MAP estimation.
Parameter for:
Model factory.
Class Restriction: implements algorithm.clustering.em.EMClusterModelFactory
Default: MultivariateGaussianModelFactory
Known implementations:
Parameter for:
Parameter to enable debugging for particular packages.
Parameter for:
Quantile to use in median voting.
Default: 0.5
Parameter for:
Voting strategy to use in the ensemble.
Class Restriction: implements utilities.ensemble.EnsembleVoting
Known implementations:
Parameter for:
Number of neighbors to use for PCA.
Parameter for:
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
Parameter for:
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
Parameter for:
The g parameter of ERP - comparison value to use in gaps.
Default: 0.0
Parameter for:
Class to evaluate the results with.
Class Restriction: implements evaluation.Evaluator
Default: AutomaticEvaluation
Known implementations:
Parameter for:
The inner neighborhood predicate to use.
Class Restriction: implements algorithm.outlier.spatial.neighborhood.NeighborSetPredicate
Known implementations:
Parameter for:
The inner neighborhood predicate to use.
Class Restriction: implements algorithm.outlier.spatial.neighborhood.NeighborSetPredicate
Known implementations:
Parameter for:
The number of steps allowed in the neighborhood graph.
Parameter for:
The number of steps allowed in the neighborhood graph.
Parameter for:
Filename with the precomputed k nearest neighbors.
Parameter for:
The file name containing the (external) cluster vector.
Parameter for:
The file listing the neighbors.
Parameter for:
The file name containing the (external) outlier scores.
Parameter for:
The pattern to match object ID prefix
Default: ^ID=
Parameter for:
Flag to signal an inverted outlier score.
Default: false
Parameter for:
Class to use as scaling function.
Class Restriction: implements utilities.scaling.ScalingFunction
Default: IdentityScaling
Known implementations:
Parameter for:
The pattern to match object score prefix
Parameter for:
The number of clusters to extract.
Parameter for:
The minimum cluster size.
Parameter for:
Keep the first object chosen (which is chosen randomly) for the farthest points heuristic.
Default: false
Parameter for:
Number of nearest neighbors to use for ABOD.
Parameter for:
Parameter for FastDOC, setting the number of relevant attributes which, when found for a cluster, are deemed enough to stop iterating.
Default: 5
Parameter for:
Random seed for generating projections.
Default: use global random seed
Parameter for:
Use the breadth first combinations instead of the cumulative sum approach
Default: false
Parameter for:
The number of instances to use in the ensemble.
Parameter for:
Specify a particular random seed.
Default: use global random seed
Parameter for:
The number of samples to draw from each uncertain object to determine the epsilon-neighborhood.
Parameter for:
Random generator used to draw samples.
Default: use global random seed
Parameter for:
The amount of samples that have to be epsilon-close for two objects to be neighbors.
Default: 0.5
Parameter for:
Dimensionality of vectors to retain.
Parameter for:
Number of objects to keep.
Parameter for:
Regularize scores before using Gamma scaling.
Default: false
Parameter for:
Invert the value range to [0:1], with 1 being outliers instead of 0.
Default: false
Parameter for:
Core point predicate for Generalized DBSCAN
Class Restriction: implements algorithm.clustering.gdbscan.CorePredicate
Default: MinPtsCorePredicate
Known implementations:
Parameter for:
Core point predicate for Generalized DBSCAN
Class Restriction: implements algorithm.clustering.gdbscan.CorePredicate
Default: MinPtsCorePredicate
Known implementations:
Parameter for:
Use a model that keeps track of core points. Needs more memory.
Default: false
Parameter for:
Use a model that keeps track of core points. Needs more memory.
Default: false
Parameter for:
Minimum similarity of points to cluster.
Parameter for:
Neighborhood predicate for Generalized DBSCAN
Class Restriction: implements algorithm.clustering.gdbscan.NeighborPredicate
Default: EpsilonNeighborPredicate
Known implementations:
Parameter for:
Neighborhood predicate for Generalized DBSCAN
Class Restriction: implements algorithm.clustering.gdbscan.NeighborPredicate
Default: EpsilonNeighborPredicate
Known implementations:
Parameter for:
Similarity function to use.
Class Restriction: implements distance.similarityfunction.SimilarityFunction
Known implementations:
Parameter for:
The number of reference points to be generated.
Parameter for:
Random generator seed.
Default: use global random seed
Parameter for:
Scale the grid by the given factor. This can be used to obtain reference points outside the used data space.
Default: 1.0
Parameter for:
Earth model to use for projection. Default: spherical model.
Class Restriction: implements math.geodesy.EarthModel
Default: SphericalVincentyEarthModel
Known implementations:
Parameter for:
Filter to use for dimensionality reduction.
Class Restriction: implements math.linearalgebra.pca.filter.EigenPairFilter
Known implementations:
Parameter for:
Operation mode: full, or rotate only.
Default: FULL
Parameter for:
Significance niveau
Parameter for:
k nearest neighbors to use
Parameter for:
Scale the grid by the given factor. This can be used to obtain reference points outside the used data space.
Default: 1.0
Parameter for:
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
Parameter for:
Width of the grid used, must be at least two times epsilon.
Parameter for:
Produce a hierarchical output.
Default: false
Parameter for:
The minimum cluster size.
Default: 1
Parameter for:
The minimum cluster size.
Default: 1
Parameter for:
Threshold for minimum number of points in the epsilon-neighborhood of a point (including this point).
Parameter for:
The threshold for 'strong' eigenvectors: the 'strong' eigenvectors explain a portion of at least alpha of the total variance.
Default: 0.85
Parameter for:
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
Parameter for:
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.
Parameter for:
Specifies the smoothing factor. The mu-nearest neighbor is used to compute the correlation reachability of an object.
Parameter for:
The Algorithm that performs the actual outlier detection on the resulting set of subspace
Class Restriction: implements algorithm.outlier.OutlierAlgorithm
Default: lof.LOF
Known implementations:
Parameter for:
The discriminance value that determines the size of the test statistic .
Default: 0.1
Parameter for:
The threshold that determines how many d-dimensional subspace candidates to retain in each step of the generation
Default: 100
Parameter for:
The number of iterations in the Monte-Carlo processing.
Default: 50
Parameter for:
The random seed.
Default: use global random seed
Parameter for:
The statistical test that is used to calculate the deviation of two data samples
Class Restriction: implements math.statistics.tests.GoodnessOfFitTest
Default: KolmogorovSmirnovTest
Known implementations:
Parameter for:
Generate a truncated hierarchical clustering result (or strict partitions).
Default: false
Parameter for:
Linkage method to use (e.g. Ward, Single-Link)
Class Restriction: implements algorithm.clustering.hierarchical.linkage.Linkage
Default: WardLinkage
Known implementations:
Parameter for:
Parameter to choose the linkage strategy.
Default: WARD
Parameter for:
Parameter to choose the linkage strategy.
Default: WARD
Parameter for:
The minimum number of clusters to extract (there may be more clusters when tied, and singletons may be merged into a noise cluster).
Parameter for:
The threshold level for which to extract the clusters.
Parameter for:
Max. Hilbert-Level
Default: 32
Parameter for:
Compute up to k next neighbors
Default: 5
Parameter for:
Compute n outliers
Default: 10
Parameter for:
output of Top n or all elements
Default: TopN
Parameter for:
The maximum absolute variance along a coordinate axis.
Default: 0.01
Parameter for:
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.
Parameter for:
Use separate frequencies for outliers and non-outliers.
Default: false
Parameter for:
Random generator seed for holdout evaluation.
Default: use global random seed
Parameter for:
Nearest neighbor to use for the statistic
Default: 1
Parameter for:
Maximum values in each dimension. If no value is specified, the maximum value in each dimension will be used. If only one value is specified, this value will be used for all dimensions.
Parameter for:
Minimum values in each dimension. If no value is specified, the minimum value in each dimension will be used. If only one value is specified, this value will be used for all dimensions.
Parameter for:
The number of times to repeat the experiment (default: 1)
Default: 1
Parameter for:
Number of object / random samples to analyze.
Parameter for:
The random number generator.
Default: use global random seed
Parameter for:
The dimensionality of the histogram in hue, saturation and brightness.
Parameter for:
Alpha value for hull drawing (in projected space!).
Default: Infinity
Parameter for:
Class to estimate ID from distance distribution.
Class Restriction: implements math.statistics.intrinsicdimensionality.IntrinsicDimensionalityEstimator
Default: MOMEstimator
Known implementations:
Parameter for:
Number of nearest neighbors to use for ID estimation (usually 20-100).
Parameter for:
Number of DBID to generate.
Parameter for:
First integer DBID to generate.
Default: 0
Parameter for:
Estimation method for intrinsic dimensionality.
Class Restriction: implements math.statistics.intrinsicdimensionality.IntrinsicDimensionalityEstimator
Default: GEDEstimator
Known implementations:
Parameter for:
Number of kNN (absolute or relative)
Default: 50.0
Parameter for:
Sample size (absolute or relative)
Default: 0.1
Parameter for:
Distance function to build the index for.
Class Restriction: implements distance.distancefunction.DistanceFunction
Known implementations:
Parameter for:
Number of reference points to use.
Parameter for:
Method to choose the reference points.
Class Restriction: implements algorithm.clustering.kmeans.initialization.KMedoidsInitialization
Known implementations:
Parameter for:
Estimator of intrinsic dimensionality.
Class Restriction: implements math.statistics.intrinsicdimensionality.IntrinsicDimensionalityEstimator
Default: ALIDEstimator
Known implementations:
Parameter for:
Context set size (ID estimation).
Parameter for:
Reference set size.
Parameter for:
Partially transparent filling of index pages.
Default: false
Parameter for:
The pagefile factory for storing the index.
Class Restriction: implements persistent.PageFileFactory
Default: MemoryPageFileFactory
Known implementations:
Parameter for:
The number of nearest neighbors of an object to be considered for computing its INFLO score.
Parameter for:
The pruning threshold
Default: 1.0
Parameter for:
Estimator for intrinsic dimensionality.
Class Restriction: implements math.statistics.intrinsicdimensionality.IntrinsicDimensionalityEstimator
Default: AggregatedHillEstimator
Known implementations:
Parameter for:
Number of neighbors to use. Should be about 3x the desired perplexity.
Default: 100
Parameter for:
Maximum length of frequent itemsets to report. This can help to reduce the output size to only the most interesting patterns.
Parameter for:
Minimum length of frequent itemsets to report. This can help to reduce the output size to only the most interesting patterns.
Parameter for:
Threshold for minimum support as minimally required number of transactions (if > 1) or the minimum frequency (if <= 1).
Parameter for:
Estimator for intrinsic dimensionality.
Class Restriction: implements math.statistics.intrinsicdimensionality.IntrinsicDimensionalityEstimator
Default: AggregatedHillEstimator
Known implementations:
Parameter for:
Jitter amount relative to data.
Parameter for:
Jitter random seed.
Default: use global random seed
Parameter for:
The data sources to join.
Class Restriction: implements datasource.DatabaseConnection
Known implementations:
Parameter for:
The data sources to join.
Class Restriction: implements datasource.DatabaseConnection
Known implementations:
Parameter for:
The data sources to join.
Class Restriction: implements datasource.DatabaseConnection
Known implementations:
Parameter for:
Maximum leaf size for the k-d-tree. Nodes will be split until their size is smaller than this threshold.
Default: 1
Parameter for:
Intrinsic dimensionality of this data set. Use -1 for using the true data dimensionality, but values such as 0-2 often offer better performance.
Default: 1
Parameter for:
Maximum value of k to analyze.
Parameter for:
Minimum value of k to analyze.
Parameter for:
Kernel density function to use.
Class Restriction: implements math.statistics.kernelfunctions.KernelDensityFunction
Default: GaussianKernelDensityFunction
Known implementations:
Parameter for:
Minimum bandwidth for kernel density estimation.
Parameter for:
Scaling factor for the kernel function.
Default: 0.25
Parameter for:
Standard deviation of the laplace RBF kernel.
Default: 1.0
Parameter for:
The bias of the polynomial kernel, a constant that is added to the scalar product.
Parameter for:
The degree of the polynomial kernel function. Default: 2
Default: 2
Parameter for:
Constant term in the rational quadratic kernel.
Default: 1.0
Parameter for:
Standard deviation of the Gaussian RBF kernel.
Default: 1.0
Parameter for:
Sigmoid c parameter (scaling).
Default: 1.0
Parameter for:
Sigmoid theta parameter (bias).
Default: 0.0
Parameter for:
Dimension to use for clustering. For one-dimensional data, use 0.
Default: 0
Parameter for:
Kernel function for density estimation.
Class Restriction: implements math.statistics.kernelfunctions.KernelDensityFunction
Default: EpanechnikovKernelDensityFunction
Known implementations:
Parameter for:
Number of nearest neighbors to use for bandwidth estimation.
Parameter for:
Kernel density estimation mode (baloon estimator vs. sample point estimator).
Default: BALLOON
Parameter for:
Half width of sliding window to find local minima.
Parameter for:
Kernel to use for kernel density LOF.
Class Restriction: implements math.statistics.kernelfunctions.KernelDensityFunction
Default: EpanechnikovKernelDensityFunction
Known implementations:
Parameter for:
KMeans variant to run multiple times.
Class Restriction: implements algorithm.clustering.kmeans.KMeans
Known implementations:
Parameter for:
Clustering algorithm to use for detecting outliers.
Class Restriction: implements algorithm.clustering.kmeans.KMeans
Default: KMeansLloyd
Known implementations:
Parameter for:
KMeans variant to run multiple times.
Class Restriction: implements algorithm.clustering.kmeans.KMeans
Known implementations:
Parameter for:
Method to choose the initial means.
Class Restriction: implements algorithm.clustering.kmeans.initialization.KMedoidsInitialization
Default: PAMInitialMeans
Known implementations:
Parameter for:
Class Restriction: implements algorithm.clustering.kmeans.initialization.KMeansInitialization
Default: RandomlyChosenInitialMeans
Class Restriction: implements algorithm.clustering.kmeans.initialization.KMeansInitialization
Default: RandomlyChosenInitialMeans
Default: LABInitialMeans
Default: LABInitialMeans
Class Restriction: implements algorithm.clustering.kmeans.initialization.KMeansInitialization
Default: RandomlyChosenInitialMeans
Class Restriction: implements algorithm.clustering.kmeans.initialization.KMeansInitialization
Default: RandomlyChosenInitialMeans
Class Restriction: implements algorithm.clustering.kmeans.initialization.KMeansInitialization
Default: RandomlyChosenInitialMeans
Class Restriction: implements algorithm.clustering.kmeans.initialization.KMeansInitialization
Default: RandomlyChosenInitialMeans
Class Restriction: implements algorithm.clustering.kmeans.initialization.KMeansInitialization
Default: RandomlyChosenInitialMeans
Class Restriction: implements algorithm.clustering.kmeans.initialization.KMeansInitialization
Default: RandomlyChosenInitialMeans
Class Restriction: implements algorithm.clustering.kmeans.initialization.KMeansInitialization
Default: RandomlyChosenInitialMeans
Class Restriction: implements algorithm.clustering.kmeans.initialization.KMeansInitialization
Default: RandomlyChosenInitialMeans
Class Restriction: implements algorithm.clustering.kmeans.initialization.KMeansInitialization
Default: RandomlyChosenInitialMeans
Default: ParkInitialMeans
Class Restriction: implements algorithm.clustering.kmeans.initialization.KMeansInitialization
Default: RandomlyChosenInitialMeans
Class Restriction: implements algorithm.clustering.kmeans.initialization.KMeansInitialization
Default: RandomlyChosenInitialMeans
Class Restriction: implements algorithm.clustering.kmeans.initialization.KMeansInitialization
Default: RandomlyChosenInitialMeans
Class Restriction: implements algorithm.clustering.kmeans.initialization.KMeansInitialization
Default: KMeansPlusPlusInitialMeans
The number of clusters to find.
Parameter for:
The maximum number of iterations to do. 0 means no limit.
Parameter for:
Default: 0
Default: 0
Default: 0
Default: 0
Default: 0
Default: 0
Default: 0
Default: 0
Default: 0
Default: 0
Default: 0
Default: 0
Default: 0
Default: 0
Default: 0
Default: 0
Default: 0
Default: 0
Default: 0
Default: 0
Default: -1
Default: 0
Initial means for k-means.
Parameter for:
Quality measure variant for deciding which run to keep.
Class Restriction: implements algorithm.clustering.kmeans.quality.KMeansQualityMeasure
Known implementations:
Parameter for:
Sample set size (if > 1) or sampling rante (if < 1).
Parameter for:
The random number generator seed.
Default: use global random seed
Parameter for:
The number of trials to run.
Parameter for:
Compute the final clustering variance statistic. Needs an additional full pass over the data set.
Default: false
Parameter for:
Number of outliers to ignore, or (if less than 1) a relative rate.
Default: 0.05
Parameter for:
Create a noise cluster, instead of assigning the noise objects.
Default: false
Parameter for:
Automatically open the result file.
Default: false
Parameter for:
Use simpler KML objects, compatibility mode.
Default: false
Parameter for:
Additional scaling function for KML colorization.
Class Restriction: implements utilities.scaling.outlier.OutlierScaling
Default: OutlierLinearScaling
Known implementations:
Parameter for:
Number of neighbors to retreive for kNN benchmarking.
Parameter for:
Data source for the queries. If not set, the queries are taken from the database.
Class Restriction: implements datasource.DatabaseConnection
Known implementations:
Parameter for:
Random generator for sampling.
Default: use global random seed
Parameter for:
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.
Parameter for:
The number of neighbors to take into account for classification.
Default: 1
Parameter for:
The k nearest neighbor, excluding the query point (i.e. query point is the 0-nearest-neighbor)
Default: 1
Parameter for:
Specifies the distance of the k-distant object to be assessed, ignoring the query object.
Parameter for:
The percentage of objects to use for sampling, or the absolute number of samples.
Default: 1.0
Parameter for:
Random generator seed for sampling.
Default: use global random seed
Parameter for:
The early termination parameter.
Default: 0.001
Parameter for:
maximum number of iterations
Default: 100
Parameter for:
Do not use initial neighbors.
Default: false
Parameter for:
The sample rate parameter
Default: 1.0
Parameter for:
The random number seed.
Default: use global random seed
Parameter for:
Specifies the k-nearest neighbors to be assigned.
Default: 1
Parameter for:
The k nearest neighbor, excluding the query point (i.e. query point is the 0-nearest-neighbor)
Parameter for:
The k nearest neighbor, excluding the query point (i.e. query point is the 0-nearest-neighbor)
Parameter for:
Beta for the Lance-Williams flexible beta approach.
Default: -0.25
Parameter for:
the allowed deviation in x direction for LCSS alignment (positive double value, 0 <= pDelta <= 1)
Default: 0.1
Parameter for:
the allowed deviation in y direction for LCSS alignment (positive double value, 0 <= pEpsilon <= 1)
Default: 0.05
Parameter for:
Score scaling parameter for LDF.
Default: 0.1
Parameter for:
Kernel bandwidth multiplier for LDF.
Parameter for:
Number of neighbors to use for LDF.
Parameter for:
Kernel to use for LDF.
Class Restriction: implements math.statistics.kernelfunctions.KernelDensityFunction
Default: GaussianKernelDensityFunction
Known implementations:
Parameter for:
The number of nearest neighbors of an object to be considered for computing its LDOF_SCORE.
Parameter for:
Maximum distance from leading object.
Parameter for:
The k nearest neighbor, excluding the query point (i.e. query point is the 0-nearest-neighbor)
Parameter for:
Ignore zero entries when computing the minimum and maximum.
Default: false
Parameter for:
Fixed maximum to use in linear scaling.
Parameter for:
Fixed minimum to use in linear scaling.
Parameter for:
Use the mean as minimum for scaling.
Default: false
Parameter for:
Maximum linear manifold dimension to search.
Parameter for:
Minimum cluster size to allow.
Parameter for:
A number used to determine how many samples are taken in each search.
Default: 100
Parameter for:
Random generator seed.
Default: use global random seed
Parameter for:
Threshold to determine if a cluster was found.
Parameter for:
The distance function used to select objects for running PCA.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Parameter for:
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.
Parameter for:
Scaling factor for averaging neighborhood
Default: 4
Parameter for:
Scaling factor for averaging neighborhood
Default: 0.5
Parameter for:
The number of Grids to use.
Default: 1
Parameter for:
Minimum neighborhood size to be considered.
Default: 20
Parameter for:
Minimum neighborhood size to be considered.
Default: 20
Parameter for:
The maximum radius of the neighborhood to be considered.
Parameter for:
The seed to use for initializing Random.
Default: use global random seed
Parameter for:
The number of nearest neighbors (not including the query point) of an object to be considered for computing its LOF score.
Parameter for:
The number of nearest neighbors of an object to be considered for computing its LOF score.
Parameter for:
The number of nearest neighbors of an object to be considered for computing its LOF score.
Parameter for:
Distance function to determine the reachability distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Known implementations:
Parameter for:
Boosting factor. Larger values will yield a steeper curve.
Default: 1.0
Parameter for:
Distance function to determine the reference set of an object.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Parameter for:
The number of nearest neighbors of an object to be considered for computing its LOOP_SCORE.
Parameter for:
The number of nearest neighbors of an object to be used for the PRD value.
Parameter for:
The number of standard deviations to consider for density computation.
Default: 2.0
Parameter for:
Distance function to determine the density of an object.
Class Restriction: implements distance.distancefunction.DistanceFunction
Known implementations:
Parameter for:
Degree p of the L_p-Norm (positive number)
Parameter for:
Density difference factor
Parameter for:
Neighborhood size (k)
Parameter for:
Number of hash buckets to use.
Default: 7919
Parameter for:
Hash function family to use for LSH.
Class Restriction: implements index.lsh.hashfamilies.LocalitySensitiveHashFunctionFamily
Known implementations:
Parameter for:
Number of projections to use for each hash function.
Parameter for:
Number of projections to use for each hash function.
Parameter for:
Random seed for generating the projections.
Default: use global random seed
Parameter for:
Random seed for generating the projections.
Default: use global random seed
Parameter for:
Bin width for random projections.
Parameter for:
Number of hash tables to use.
Parameter for:
Include the query object in the evaluation.
Default: false
Parameter for:
Maximum value of k for kNN evaluation.
Parameter for:
Relative amount of object to sample.
Parameter for:
Random seed for deterministic sampling.
Default: use global random seed
Parameter for:
the distance function to materialize the nearest neighbors
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Parameter for:
The number of nearest neighbors of an object to be materialized.
Parameter for:
Distance function for the precomputed distance matrix.
Class Restriction: implements distance.distancefunction.DistanceFunction
Known implementations:
Parameter for:
Similarity function for the precomputed similarity matrix.
Class Restriction: implements distance.similarityfunction.SimilarityFunction
Known implementations:
Parameter for:
Output dimensionality.
Parameter for:
Distance function to use.
Class Restriction: implements distance.distancefunction.PrimitiveDistanceFunction
Default: minkowski.SquaredEuclideanDistanceFunction
Known implementations:
Parameter for:
Random seed for fast MDS.
Default: use global random seed
Parameter for:
The type of vectors to create.
Class Restriction: implements data.NumberVector
Default: DoubleVector
Known implementations:
Parameter for:
Kernel function to use with mean-shift clustering.
Class Restriction: implements math.statistics.kernelfunctions.KernelDensityFunction
Default: EpanechnikovKernelDensityFunction
Known implementations:
Parameter for:
Range of the kernel to use (aka: radius, bandwidth).
Parameter for:
Class to use as scaling function.
Class Restriction: implements utilities.scaling.ScalingFunction
Known implementations:
Parameter for:
positive integer specifying the maximum number k of reverse k nearest neighbors to be supported.
Parameter for:
Flag to indicate that the approximation is done in the ''normal'' space instead of the log-log space (which is default).
Default: false
Parameter for:
positive integer specifying the order of the polynomial approximation.
Parameter for:
positive integer specifying the maximum number k of reverse k nearest neighbors to be supported.
Parameter for:
Specifies the maximal number k of reverse k nearest neighbors to be supported.
Parameter for:
cutoff
Default: 1.0E-7
Parameter for:
Expected amount of outliers, for making the scores more intuitive. When the value is 1, the CDF will be given instead.
Default: 0.01
Parameter for:
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Parameter for:
Insertion strategy to use for constructing the M-tree.
Class Restriction: implements index.tree.metrical.mtreevariants.strategies.insert.MTreeInsert
Default: MinimumEnlargementInsert
Known implementations:
Parameter for:
Random generator / seed for the randomized split.
Default: use global random seed
Parameter for:
Split strategy to use for constructing the M-tree.
Class Restriction: implements index.tree.metrical.mtreevariants.strategies.split.MTreeSplit
Default: MLBDistSplit
Known implementations:
Parameter for:
Distribution strategy for mtree entries during splitting.
Class Restriction: implements index.tree.metrical.mtreevariants.strategies.split.distribution.DistributionStrategy
Default: GeneralizedHyperplaneDistribution
Known implementations:
Parameter for:
The exponents to use for this distance function
Parameter for:
Distribution to sample replacement values from.
Class Restriction: implements math.statistics.distribution.Distribution
Known implementations:
Parameter for:
The neighborhood predicate to use in comparison step.
Class Restriction: implements algorithm.outlier.spatial.neighborhood.NeighborSetPredicate
Known implementations:
Parameter for:
the distance function to use
Class Restriction: implements distance.distancefunction.DistanceFunction
Known implementations:
Parameter for:
Parameter for the non-weighted neighborhood to use.
Class Restriction: implements algorithm.outlier.spatial.neighborhood.NeighborSetPredicate
Known implementations:
Parameter for:
the number of neighbors
Parameter for:
positive number of folds for cross-validation
Default: 10
Parameter for:
Number of folds for cross-validation.
Default: 10
Parameter for:
Number of folds for cross-validation
Default: 10
Parameter for:
Maximum value to assign to objects.
Default: 1.0
Parameter for:
Minimum value to assign to objects.
Default: 0.0
Parameter for:
Norm (length function) to use for computing the vector length.
Class Restriction: implements distance.distancefunction.Norm
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Parameter for:
Alpha parameter to control the shape of the output distribution.
Default: 0.1
Parameter for:
A list of the distribution estimators to try.
Class Restriction: implements math.statistics.distribution.estimator.DistributionEstimator
Default: meta.BestFitEstimator
Known implementations:
Parameter for:
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.
Parameter for:
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.
Parameter for:
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.
Parameter for:
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.
Parameter for:
Number of neighbors to use for kNN graph.
Parameter for:
Number of neighbors to use for kNN graph.
Parameter for:
The maximum radius of the neighborhood to be considered.
Parameter for:
Threshold for minimum number of points in the epsilon-neighborhood of a point.
Parameter for:
The actual OPTICS-type algorithm to use.
Class Restriction: implements algorithm.clustering.optics.OPTICSTypeAlgorithm
Default: OPTICSHeap
Known implementations:
Parameter for:
Keep the steep up/down areas of the plot.
Default: false
Parameter for:
Disable the predecessor correction.
Default: false
Parameter for:
Threshold for the steepness requirement.
Parameter for:
The factor for reducing the number of current clusters in each iteration.
Default: 0.5
Parameter for:
The random number generator seed.
Default: use global random seed
Parameter for:
Filename the KMZ file (compressed KML) is written to.
Parameter for:
Filter pattern for output selection. Only output streams that match the given pattern will be written.
Parameter for:
Enable gzip compression of output files.
Default: false
Parameter for:
Silently overwrite output files.
Default: false
Parameter for:
Label pattern to match outliers.
Default: .*(Outlier|Noise).*
Parameter for:
Class label for the 'positive' class.
Parameter for:
Subspace clustering algorithm to use.
Class Restriction: implements algorithm.clustering.subspace.SubspaceClusteringAlgorithm
Known implementations:
Parameter for:
Alpha parameter for S1 score.
Default: 0.25
Parameter for:
Range value for OUTRES in 2 dimensions.
Parameter for:
The significance level for uniform testing in the initial binning step.
Default: 0.001
Parameter for:
The change delta for the EM step below which to stop.
Default: 1.0E-5
Parameter for:
The maximum number of iterations for the EM step. Use -1 to run until delta convergence.
Default: 20
Parameter for:
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
Parameter for:
The threshold value for the poisson test used when merging signatures.
Default: 1.0E-4
Parameter for:
The size of the cache in bytes.
Parameter for:
The name of the file storing the page file.
Parameter for:
The name of the file storing the page file.
Parameter for:
The backing pagefile for the cache.
Class Restriction: implements persistent.PageFileFactory
Default: PersistentPageFileFactory
Known implementations:
Parameter for:
The size of a page in bytes.
Default: 1024
Parameter for:
Default: 4000
Default: 4000
Default: 4000
Use special handling for noise clusters.
Default: false
Parameter for:
Reference clustering to compare with. Defaults to a by-label clustering.
Class Restriction: implements algorithm.clustering.ClusteringAlgorithm
Default: trivial.ByLabelOrAllInOneClustering
Known implementations:
Parameter for:
Enable self-pairing for cluster comparison.
Default: false
Parameter for:
Tolerance for optimistically performing additional swaps, where 1 executes all fast swaps, 0 only those that are unaffected by the primary swaps.
Default: 1.0
Parameter for:
Draw straight lines
Default: false
Parameter for:
Layouting method for 3DPC.
Class Restriction: implements visualization.parallel3d.layout.Layouter3DPC
Default: SimpleCircularMSTLayout3DPC
Known implementations:
Parameter for:
Similarity measure for spanning tree.
Class Restriction: implements math.statistics.dependence.DependenceMeasure
Default: CorrelationDependenceMeasure
Known implementations:
Parameter for:
Column separator pattern. The default assumes whitespace separated data.
Default: \s*[,;\s]\s*
Parameter for:
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.
Parameter for:
Quotation characters. By default, both double and single ASCII quotes are accepted.
Default: "'
Parameter for:
The type of vectors to create for numerical attributes.
Class Restriction: implements data.NumberVector
Default: DoubleVector
Known implementations:
Parameter for:
Class Restriction: implements data.SparseNumberVector
Default: SparseFloatVector
Class Restriction: implements data.SparseNumberVector
Default: SparseFloatVector
Class Restriction: implements data.SparseNumberVector
Default: SparseFloatVector
The number of partitions to use for approximate kNN.
Parameter for:
The random number generator seed.
Default: use global random seed
Parameter for:
Flag to invert pattern.
Default: false
Parameter for:
The filter pattern to use.
Parameter for:
Distance function to use for computing PBM.
Class Restriction: implements distance.distancefunction.NumberVectorDistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Parameter for:
Control how noise should be treated.
Default: TREAT_NOISE_AS_SINGLETONS
Parameter for:
Class used to compute the covariance matrix.
Class Restriction: implements math.linearalgebra.pca.CovarianceMatrixBuilder
Default: StandardCovarianceMatrixBuilder
Known implementations:
Parameter for:
Filter class to determine the strong and weak eigenvectors.
Class Restriction: implements math.linearalgebra.pca.filter.EigenPairFilter
Default: PercentageEigenPairFilter
Known implementations:
Parameter for:
Flag to mark delta as an absolute value.
Default: false
Parameter for:
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
Parameter for:
The threshold for strong Eigenvalues. If not otherwise specified, delta is a relative value w.r.t. the (absolute) highest Eigenvalues and has to be a double between 0 and 1. To mark delta as an absolute value, use the option -pca.filter.absolute.
Default: 0.1
Parameter for:
The number of strong eigenvectors: n eigenvectors with the n highest eigenvalues are marked as strong eigenvectors.
Parameter for:
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
Parameter for:
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
Parameter for:
The minimum strength of the statistically expected variance (1/n) share an eigenvector needs to have to be considered 'strong'.
Default: 0.0
Parameter for:
The class to compute (filtered) PCA.
Class Restriction: extends math.linearalgebra.pca.PCARunner
Default: PCARunner
Known implementations:
Parameter for:
Weight function to use in weighted PCA.
Class Restriction: implements math.linearalgebra.pca.weightfunctions.WeightFunction
Default: ConstantWeight
Known implementations:
Parameter for:
Only used, if MINMAX is set as scaling reference: a comma separated concatenation of the maximum values in each dimension assumed as a reference. If no value is specified, the maximum value of the attribute range in this dimension will be taken.
Parameter for:
Only used, if MINMAX is set as scaling reference: a comma separated concatenation of the minimum values in each dimension assumed as a reference. If no value is specified, the minimum value of the attribute range in this dimension will be taken.
Parameter for:
The nature of the noise distribution, default is UNIFORM
Default: UNIFORM
Parameter for:
Percentage of the standard deviation of the random Gaussian noise generation per attribute, given the standard deviation of the corresponding attribute in the original data distribution (assuming a Gaussian distribution there).
Default: 0.01
Parameter for:
The reference for scaling the Gaussian noise. Default is UNITCUBE, parameter perturbationfilter.percentage will then directly define the standard deviation of all noise Gaussians. For options STDDEV and MINMAX, the percentage of the attributewise standard deviation or extension, repectively, will define the attributewise standard deviation of the noise Gaussians.
Default: UNITCUBE
Parameter for:
Seed for random noise generation.
Parameter for:
Multiplicator for neighborhood size.
Default: 3.0
Parameter for:
Sparsity of the random projection.
Default: 1.0
Parameter for:
Random generator seed.
Default: use global random seed
Parameter for:
Target dimensionality.
Parameter for:
k value for precision@k. Can be set to 0, to get R-precision, or the precision-recall-break-even-point.
Default: 0
Parameter for:
Maximum value of 'k' to compute the curve up to.
Parameter for:
Class label for the 'positive' class.
Parameter for:
Class label for the 'positive' class.
Parameter for:
A double specifying the variance threshold for small Eigenvalues.
Parameter for:
Penalty factor for deviations in preferred (low-variance) dimensions.
Default: 20.0
Parameter for:
Penalty factor for deviations in preferred (low-variance) dimensions.
Default: 20.0
Parameter for:
Maximum dimensionality to consider for core points.
Parameter for:
Maximum dimensionality to consider for core points.
Parameter for:
The multiplier for the initial number of medoids.
Default: 10
Parameter for:
The random number generator seed.
Default: use global random seed
Parameter for:
The number of clusters to find.
Parameter for:
The multiplier for the initial number of seeds.
Default: 30
Parameter for:
The dimensionality of the clusters to find.
Parameter for:
Projection to use.
Class Restriction: implements data.projection.Projection
Known implementations:
Parameter for:
Projection dimensionality
Default: 2
Parameter for:
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
Parameter for:
Random projection family to use. The default is to use the original axes.
Class Restriction: implements data.projection.random.RandomProjectionFamily
Known implementations:
Parameter for:
Number of projections to use.
Parameter for:
Random generator.
Default: use global random seed
Parameter for:
Window size multiplicator.
Default: 10.0
Parameter for:
Number of bins in the distribution histogram
Default: 80
Parameter for:
Use curves instead of the stacked histogram style.
Default: false
Parameter for:
Flag to disable refinement of distances.
Default: false
Parameter for:
Index to use on the projected data.
Class Restriction: implements index.IndexFactory
Known implementations:
Parameter for:
Multiplier for k.
Default: 1.0
Parameter for:
Flag to materialize the projected data.
Default: false
Parameter for:
Projection to use for the projected index.
Class Restriction: implements data.projection.Projection
Known implementations:
Parameter for:
Alpha threshold for estimating the confidence probability.
Default: 0.95
Parameter for:
Clustering algorithm used on the samples.
Class Restriction: implements algorithm.clustering.ClusteringAlgorithm
Known implementations:
Parameter for:
Distance measure of clusterings.
Class Restriction: implements distance.similarityfunction.cluster.ClusteringDistanceSimilarityFunction
Default: ClusteringAdjustedRandIndexSimilarityFunction
Known implementations:
Parameter for:
Algorithm used to aggregate clustering results. Must be a distance-based clustering algorithm.
Class Restriction: implements algorithm.clustering.ClusteringAlgorithm
Default: kmeans.KMedoidsPAM
Known implementations:
Parameter for:
Random generator used for sampling.
Default: use global random seed
Parameter for:
Number of clusterings to produce on samples.
Default: 10
Parameter for:
Retain all sampled relations, not only the representative results.
Default: false
Parameter for:
The random number seed.
Default: use global random seed
Parameter for:
The relative amount of objects to consider for kNN computations.
Parameter for:
Amount of dimensions to project to.
Parameter for:
Projection family to use.
Class Restriction: implements data.projection.random.RandomProjectionFamily
Default: AchlioptasRandomProjectionFamily
Known implementations:
Parameter for:
Random generator seed.
Default: use global random seed
Parameter for:
number of selected attributes
Default: 1
Parameter for:
Seed for random selection of projection attributes.
Default: use global random seed
Parameter for:
Scaling exponent for value differences.
Default: 0.5
Parameter for:
The damping parameter c.
Parameter for:
Number of nearest neighbors to use.
Parameter for:
Data source for the queries. If not set, the queries are taken from the database.
Class Restriction: implements datasource.DatabaseConnection
Known implementations:
Parameter for:
Random generator for sampling.
Default: use global random seed
Parameter for:
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.
Parameter for:
Number of bins to use in the histogram
Default: 20
Parameter for:
Number of bins to use in the histogram
Default: 100
Parameter for:
The number of iterations to perform.
Default: 1000
Parameter for:
Random seed (optional).
Default: use global random seed
Parameter for:
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.SpatialPrimitiveDistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Parameter for:
positive integer specifying the maximal number k of reverse k nearest neighbors to be supported.
Parameter for:
The number of nearest neighbors
Parameter for:
The heuristic for finding reference points.
Class Restriction: implements utilities.referencepoints.ReferencePointsHeuristic
Default: GridBasedReferencePoints
Known implementations:
Parameter for:
Result handler class.
Class Restriction: implements result.ResultHandler
Known implementations:
Parameter for:
The dimensionality of the histogram in each color
Parameter for:
Class label for the 'positive' class.
Parameter for:
Strategy for spatial sorting in bulk loading.
Class Restriction: implements math.spacefillingcurves.SpatialSorter
Known implementations:
Parameter for:
Insertion strategy for directory nodes.
Class Restriction: implements index.tree.spatial.rstarvariants.strategies.insert.InsertionStrategy
Default: LeastEnlargementWithAreaInsertionStrategy
Known implementations:
Parameter for:
Insertion strategy for leaf nodes.
Class Restriction: implements index.tree.spatial.rstarvariants.strategies.insert.InsertionStrategy
Default: LeastOverlapInsertionStrategy
Known implementations:
Parameter for:
defines how many children are tested for finding the child generating the least overlap when inserting an object.
Default: 32
Parameter for:
The strategy to use for object insertion.
Class Restriction: implements index.tree.spatial.rstarvariants.strategies.insert.InsertionStrategy
Default: CombinedInsertionStrategy
Known implementations:
Parameter for:
Minimum relative fill required for data pages.
Default: 0.4
Parameter for:
The strategy to use for handling overflows.
Class Restriction: implements index.tree.spatial.rstarvariants.strategies.overflow.OverflowTreatment
Default: LimitedReinsertOverflowTreatment
Known implementations:
Parameter for:
The amount of entries to reinsert.
Default: 0.3
Parameter for:
The distance function to compute reinsertion candidates by.
Class Restriction: implements distance.distancefunction.SpatialPrimitiveDistanceFunction
Default: minkowski.SquaredEuclideanDistanceFunction
Known implementations:
Parameter for:
The strategy to select candidates for reinsertion.
Class Restriction: implements index.tree.spatial.rstarvariants.strategies.reinsert.ReinsertStrategy
Default: CloseReinsert
Known implementations:
Parameter for:
The strategy to use for node splitting.
Class Restriction: implements index.tree.spatial.rstarvariants.strategies.split.SplitStrategy
Default: TopologicalSplitter
Known implementations:
Parameter for:
The number of samples to draw.
Parameter for:
Random generator seed.
Default: use global random seed
Parameter for:
Sampling probability. Each object has a chance of being sampled with this probability.
Parameter for:
Random generator seed for sampling.
Default: use global random seed
Parameter for:
Forcibly set the scales to the given range.
Parameter for:
Gamma value for scaling.
Parameter for:
Use wireframe style for selection ranges.
Default: false
Parameter for:
Radius to use for selectivity estimation.
Parameter for:
Relative amount of object to sample.
Parameter for:
Random seed for deterministic sampling.
Default: use global random seed
Parameter for:
Number of variates this time series has.
Parameter for:
Space filling curve generators to use for kNN approximation.
Class Restriction: implements math.spacefillingcurves.SpatialSorter
Known implementations:
Parameter for:
Space filling curve generators to use for kNN approximation.
Class Restriction: implements math.spacefillingcurves.SpatialSorter
Known implementations:
Parameter for:
Number of dimensions to use for each curve.
Parameter for:
Random projection to use.
Class Restriction: implements data.projection.random.RandomProjectionFamily
Known implementations:
Parameter for:
Random generator.
Default: use global random seed
Parameter for:
Random generator.
Default: use global random seed
Parameter for:
Number of curve variants to generate.
Default: 1
Parameter for:
Number of curve variants to generate.
Default: 1
Parameter for:
Window size multiplicator.
Default: 10.0
Parameter for:
Window size multiplicator.
Default: 10.0
Parameter for:
number of nearest neighbors to consider (at least 1)
Parameter for:
Seed for randomly shuffling the rows for the database. If the parameter is not set, a random seed will be used.
Default: use global random seed
Parameter for:
Adjustment for chance: a small constant corresponding to background noise levels.
Parameter for:
Half-life time: number of time steps until data has lost half its weight.
Parameter for:
Significance threshold for reporting
Parameter for:
Clustering algorithm to use for the silhouette coefficients.
Class Restriction: implements algorithm.clustering.ClusteringAlgorithm
Known implementations:
Parameter for:
Distance function to use for computing the silhouette.
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Parameter for:
Control how noise should be treated.
Default: TREAT_NOISE_AS_SINGLETONS
Parameter for:
Preprocessor to use.
Class Restriction: implements index.preprocessed.snn.SharedNearestNeighborIndex
Default: SharedNearestNeighborPreprocessor
Known implementations:
Parameter for:
Class to use as scaling function.
Class Restriction: implements utilities.scaling.ScalingFunction
Known implementations:
Parameter for:
Skip zero values when computing the colors to increase contrast.
Default: false
Parameter for:
Desired perplexity (approximately the number of neighbors to preserve)
Default: 40.0
Parameter for:
Gaussian kernel standard deviation.
Parameter for:
The minimum SNN density.
Parameter for:
Threshold for minimum number of points in the epsilon-SNN-neighborhood of a point.
Parameter for:
the distance function to asses the nearest neighbors
Class Restriction: implements distance.distancefunction.DistanceFunction
Default: minkowski.EuclideanDistanceFunction
Known implementations:
Parameter for:
The multiplier for the discriminance value for discerning small from large variances.
Default: 1.1
Parameter for:
The number of most snn-similar objects to use as reference set for learning the subspace properties.
Parameter for:
Report the models computed by SOD (default: report only scores).
Default: false
Parameter for:
The similarity function used for the neighborhood set.
Class Restriction: implements distance.similarityfunction.SimilarityFunction
Default: SharedNearestNeighborSimilarityFunction
Known implementations:
Parameter for:
Number of neighbors to use. Should be about 3x the desired perplexity.
Default: 15
Parameter for:
Perplexity to use.
Default: 4.5
Parameter for:
The class to perform the bulk split with.
Class Restriction: implements index.tree.spatial.rstarvariants.strategies.bulk.BulkSplit
Known implementations:
Parameter for:
Dimensions to split into the first relation.
Parameter for:
Fixed maximum to use in sqrt scaling.
Parameter for:
Fixed minimum to use in sqrt scaling.
Parameter for:
Significance level to use for error function.
Default: 3.0
Parameter for:
Fixed mean to use in standard deviation scaling.
Parameter for:
Fixed minimum to use in sqrt scaling.
Parameter for:
Distance function to use for computing the SSQ.
Class Restriction: implements distance.distancefunction.NumberVectorDistanceFunction
Default: minkowski.SquaredEuclideanDistanceFunction
Known implementations:
Parameter for:
Control how noise should be treated.
Default: TREAT_NOISE_AS_SINGLETONS
Parameter for:
Do not use the center as extra reference point.
Default: false
Parameter for:
Scale the reference points by the given factor. This can be used to obtain reference points outside the used data space.
Default: 1.0
Parameter for:
Number of neighbors to get for stddev based outlier detection.
Parameter for:
Significance level to use for error function.
Default: 3.0
Parameter for:
Fixed mean to use in standard deviation scaling.
Parameter for:
Ignore lines in the input file that satisfy this pattern.
Default: ^\s*(#|//|;).*$
Parameter for:
Default: ^\s*#.*$
Remove leading and trailing whitespace from each line.
Default: false
Parameter for:
Distance function to determine the distance between database objects.
Class Restriction: implements distance.distancefunction.subspace.DimensionSelectingSubspaceDistanceFunction
Default: SubspaceEuclideanDistanceFunction
Known implementations:
Parameter for:
The maximum radius of the neighborhood to be considered.
Parameter for:
Minimum dimensionality to generate clusters for.
Parameter for:
Threshold for minimum number of points in the epsilon-neighborhood of a point.
Parameter for:
Kernel to use with SVM.
Default: RBF
Parameter for:
SVM nu parameter.
Default: 0.05
Parameter for:
Normalize vectors to manhattan length 1 (convert term counts to term frequencies)
Default: false
Parameter for:
Class to use as scaling function.
Class Restriction: implements utilities.scaling.ScalingFunction
Default: IdentityScaling
Known implementations:
Parameter for:
Threshold(s) to apply.
Parameter for:
Enable logging of runtime data. Do not combine with more verbose logging, since verbose logging can significantly impact performance.
Default: false
Parameter for:
the percentile parameter
Parameter for:
Number of digits to show (e.g. when visualizing outlier scores)
Default: 4
Parameter for:
Make the top k a binary scaling.
Default: false
Parameter for:
Number of outliers to keep.
Parameter for:
Estimator to use on the trimmed data.
Class Restriction: implements math.statistics.distribution.estimator.DistributionEstimator
Known implementations:
Parameter for:
Relative amount of data to trim on each end, must be 0 < trim < 0.5
Parameter for:
Affinity matrix builder.
Class Restriction: implements algorithm.projection.AffinityMatrixBuilder
Default: NearestNeighborAffinityMatrixBuilder
Known implementations:
Parameter for:
Affinity matrix builder.
Class Restriction: implements algorithm.projection.AffinityMatrixBuilder
Default: PerplexityAffinityMatrixBuilder
Known implementations:
Parameter for:
Output dimensionality.
Default: 2
Parameter for:
Output dimensionality.
Default: 2
Parameter for:
Number of iterations to perform.
Default: 1000
Parameter for:
Number of iterations to perform.
Default: 1000
Parameter for:
Learning rate of the method.
Default: 200.0
Parameter for:
Learning rate of the method.
Default: 200.0
Parameter for:
The final momentum to use.
Default: 0.8
Parameter for:
The final momentum to use.
Default: 0.8
Parameter for:
Retain the original data.
Default: false
Parameter for:
Random generator seed
Default: use global random seed
Parameter for:
Random generator seed
Default: use global random seed
Parameter for:
Approximation quality parameter
Default: 0.5
Parameter for:
Dimensionality of the data set (used for splitting).
Parameter for:
Dimensionality of the data set (used for splitting).
Parameter for:
Column in which the probability is stored, starting at 0. -1 is the last column.
Parameter for:
Class to generate the point distribution.
Class Restriction: implements data.uncertain.uncertainifier.Uncertainifier
Known implementations:
Parameter for:
Maximum points per uncertain object.
Default: 10
Parameter for:
Minimum points per uncertain object (defaults to maximum.
Parameter for:
Generate a symetric uncertain region, centered around the exact data.
Default: false
Parameter for:
Maximum deviation of uncertain bounding box.
Parameter for:
Maximum 3-sigma deviation of uncertain region.
Parameter for:
Minimum deviation of uncertain bounding box.
Default: 0.0
Parameter for:
Minimum 3-sigma deviation of uncertain region.
Default: 0.0
Parameter for:
Generator to derive uncertain objects from certain vectors.
Class Restriction: implements data.uncertain.uncertainifier.Uncertainifier
Known implementations:
Parameter for:
Keep the original data as well.
Default: false
Parameter for:
Random seed for uncertainification.
Default: use global random seed
Parameter for:
Number of partitions to use in each dimension.
Parameter for:
Number of partitions to use in each dimension.
Parameter for:
Force the use of linear scanning as reference.
Default: false
Parameter for:
Number of neighbors to retreive for kNN benchmarking.
Parameter for:
Pattern to select query points.
Parameter for:
Data source for the queries. If not set, the queries are taken from the database.
Class Restriction: implements datasource.DatabaseConnection
Known implementations:
Parameter for:
Random generator for sampling.
Default: use global random seed
Parameter for:
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.
Parameter for:
Enable verbose messages.
Default: false
Parameter for:
Visualizers to enable by default.
Parameter for:
File format. Note that some formats requrie additional libraries, only SVG and PNG are default.
Default: SVG
Parameter for:
Maximum number of dimensions to display.
Default: 10
Parameter for:
The output folder.
Parameter for:
The width/heigh ratio of the output.
Default: 1.33
Parameter for:
Maximum number of objects to visualize by default (for performance reasons).
Default: 10000
Parameter for:
Embed visualizers in a single window, not using thumbnails and detail views.
Default: false
Parameter for:
Title to use for visualization window.
Parameter for:
Style properties file to use, included properties: classic, default, greyscale, neon, presentation, print
Default: default
Parameter for:
Mode for drawing the voronoi cells (and/or delaunay triangulation)
Default: VORONOI
Parameter for:
The number of nearest neighbors (not including the query point) of an object to be considered for computing its VOV score.
Parameter for:
Control how noise should be treated.
Default: TREAT_NOISE_AS_SINGLETONS
Parameter for:
Estimator to use on the winsorized data.
Class Restriction: implements math.statistics.distribution.estimator.DistributionEstimator
Known implementations:
Parameter for:
Relative amount of data to winsorize on each end, must be 0 < winsorize < 0.5
Parameter for:
The minimum number of clusters to find.
Default: 2
Parameter for:
kMeans algorithm to use.
Class Restriction: implements algorithm.clustering.kmeans.KMeans
Default: KMeansLloyd
Known implementations:
Parameter for:
The quality measure to evaluate splits (e.g. AIC, BIC)
Class Restriction: implements algorithm.clustering.kmeans.quality.KMeansQualityMeasure
Known implementations:
Parameter for:
Random seed for splitting clusters.
Default: use global random seed
Parameter for: