Kernel function to use.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.similarityfunction.SimilarityFunction
Default: de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel.PolynomialKernelFunction
Known implementations:
Parameter for:
Number of top outliers to compute.
Parameter for:
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:
Parameter for:
Algorithm to run.
Parameter for:
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction
Known implementations:
Parameter for:
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.PrimitiveDistanceFunction
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.PrimitiveDistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.SquaredEuclideanDistanceFunction
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.PrimitiveDistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.SquaredEuclideanDistanceFunction
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.PrimitiveDistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.SquaredEuclideanDistanceFunction
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.PrimitiveDistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.SquaredEuclideanDistanceFunction
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.PrimitiveDistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.SquaredEuclideanDistanceFunction
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.PrimitiveDistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.SquaredEuclideanDistanceFunction
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.PrimitiveDistanceFunction
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.PrimitiveDistanceFunction
Class Restriction: extends de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.LPNormDistanceFunction
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.NumberVectorDistanceFunction
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.PrimitiveDistanceFunction
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.PrimitiveDistanceFunction
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.PrimitiveDoubleDistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.SquaredEuclideanDistanceFunction
Number of stable iterations for convergence.
Default: 15
Parameter for:
Distance function to use.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.SquaredEuclideanDistanceFunction
Known implementations:
Parameter for:
Similarity matrix initialization..
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.clustering.affinitypropagation.AffinityPropagationInitialization
Default: de.lmu.ifi.dbs.elki.algorithm.clustering.affinitypropagation.DistanceBasedInitializationWithMedian
Known implementations:
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 de.lmu.ifi.dbs.elki.distance.similarityfunction.SimilarityFunction
Default: de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel.LinearKernelFunction
Known implementations:
Parameter for:
Threshold for minimum frequency as percentage value (alternatively to parameter apriori.minsupp).
Parameter for:
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).
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:
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.
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: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
Parameter for:
KMeans variant
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeans
Default: de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.BestOfMultipleKMeans
Known implementations:
Parameter for:
Half-transparent filling of bubbles.
Default: false
Parameter for:
Additional scaling function for bubbles.
Class Restriction: implements de.lmu.ifi.dbs.elki.utilities.scaling.outlier.OutlierScalingFunction
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: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
Parameter for:
Factor for scaling the specified cluster sizes.
Default: 1.0
Parameter for:
The generator specification file.
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:
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 de.lmu.ifi.dbs.elki.math.statistics.distribution.Distribution
Default: de.lmu.ifi.dbs.elki.math.statistics.distribution.UniformDistribution
Known implementations:
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 isthe fraction of total feature vectors contained in this unit.
Parameter for:
The number of intervals (units) in each dimension.
Parameter for:
Visualize mean-based clusters using stars.
Default: false
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 de.lmu.ifi.dbs.elki.math.linearalgebra.pca.PCARunner
Default: de.lmu.ifi.dbs.elki.math.linearalgebra.pca.PCARunner
Known implementations:
Parameter for:
The class to compute (filtered) PCA.
Class Restriction: extends de.lmu.ifi.dbs.elki.math.linearalgebra.pca.PCAFilteredRunner
Default: de.lmu.ifi.dbs.elki.math.linearalgebra.pca.PCAFilteredRunner
Known implementations:
Parameter for:
Clustering algorithm to apply to each partition.
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.clustering.ClusteringAlgorithm
Known implementations:
Parameter for:
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:
Parameter for:
Local PCA Preprocessor to derive partition criterion.
Class Restriction: implements de.lmu.ifi.dbs.elki.index.preprocessed.LocalProjectionIndex$Factory
Known implementations:
Parameter for:
Phi parameter, expected rate of outliers. Set to 0 to use raw CDF values.
Parameter for:
Database class.
Class Restriction: implements de.lmu.ifi.dbs.elki.database.Database
Default: de.lmu.ifi.dbs.elki.database.StaticArrayDatabase
Known implementations:
Parameter for:
Database indexes to add.
Parameter for:
Database connection class.
Class Restriction: implements de.lmu.ifi.dbs.elki.datasource.DatabaseConnection
Default: de.lmu.ifi.dbs.elki.datasource.FileBasedDatabaseConnection
Known implementations:
Parameter for:
Dimensionality of the vectors to generate.
Parameter for:
The filters to apply to the input data.
Parameter for:
Seed for randomly generating vectors
Default: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
Parameter for:
The name of the input file to be parsed.
Parameter for:
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:
Parameter for:
Database size to generate.
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.
Parameter for:
Threshold for minimum number of points within a cluster.
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:
The maximum radius of the neighborhood to be considered in each dimension for determination of the preference vector.
Default: 0.001
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:
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:
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:
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 de.lmu.ifi.dbs.elki.distance.distancefunction.external.DistanceParser
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.external.NumberDistanceParser
Known implementations:
Parameter for:
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:
Parameter for:
The maximum distance between two vectors with equal preference vectors before considering them as parallel.
Default: 0.001
Parameter for:
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:
Parameter for:
Class Restriction: implements de.lmu.ifi.dbs.elki.index.preprocessed.snn.SharedNearestNeighborIndex$Factory
Default: de.lmu.ifi.dbs.elki.index.preprocessed.snn.SharedNearestNeighborPreprocessor.Factory
Class Restriction: implements de.lmu.ifi.dbs.elki.index.preprocessed.localpca.FilteredLocalPCAIndex$Factory
Class Restriction: implements de.lmu.ifi.dbs.elki.index.preprocessed.localpca.FilteredLocalPCAIndex$Factory
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:
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
Shift offset parameter.
Parameter for:
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: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
Parameter for:
Distribution scale parameter
Parameter for:
Distribution shape parameter
Parameter for:
Skew of the distribution.
Parameter for:
Degrees of freedom.
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:
Use heuristics as described, thus using the FastDOC algorithm (not yet implemented).
Default: false
Parameter for:
Random seed, for reproducible experiments.
Default: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
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 Edit Distance alignment (positive double value, 0 <= bandSize <= 1)
Default: 0.1
Parameter for:
the delta parameter (similarity threshold) for EDR (positive number)
Default: 1.0
Parameter for:
The termination criterion for maximization of E(M): E(M) - E(M') < em.delta
Default: 0.0
Parameter for:
The number of clusters to find.
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 de.lmu.ifi.dbs.elki.utilities.ensemble.EnsembleVoting
Known implementations:
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 ERP (positive number)
Default: 0.0
Parameter for:
Class to evaluate the results with.
Default: [class de.lmu.ifi.dbs.elki.evaluation.AutomaticEvaluation]
Parameter for:
The inner neighborhood predicate to use.
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood.NeighborSetPredicate$Factory
Known implementations:
Parameter for:
The inner neighborhood predicate to use.
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood.NeighborSetPredicate$Factory
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 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 de.lmu.ifi.dbs.elki.utilities.scaling.ScalingFunction
Default: de.lmu.ifi.dbs.elki.utilities.scaling.IdentityScaling
Known implementations:
Parameter for:
The pattern to match object score prefix
Parameter for:
Number of nearest neighbors to use for ABOD.
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: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
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 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:
Parameter for:
Use a model that keeps track of core points. Needs more memory.
Default: false
Parameter for:
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:
Parameter for:
The number of reference points to be generated.
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 de.lmu.ifi.dbs.elki.math.geodesy.EarthModel
Default: de.lmu.ifi.dbs.elki.math.geodesy.SphericalVincentyEarthModel
Known implementations:
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:
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 de.lmu.ifi.dbs.elki.algorithm.outlier.OutlierAlgorithm
Default: de.lmu.ifi.dbs.elki.algorithm.outlier.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: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
Parameter for:
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:
Parameter for:
Parameter to choose the linkage strategy.
Default: WARD
Parameter for:
Parameter to choose the linkage strategy.
Default: WARD
Parameter for:
Linkage method to use (e.g. Ward, Single-Link)
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.LinkageMethod
Default: de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.WardLinkageMethod
Known implementations:
Parameter for:
The minimum number of clusters to extract (there may be more clusters when tied).
Parameter for:
The output mode: a truncated cluster hierarchy, or a strict (flat) partitioning of the data set.
Parameter for:
Do not avoid singleton clusters. This produces a more complex hierarchy.
Default: false
Parameter for:
The threshold level for which to extract the clusters.
Parameter for:
The thresholding mode to use for extracting clusters: by desired number of clusters, or by distance threshold.
Default: BY_MINCLUSTERS
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:
The dimensionality of the histogram in hue, saturation and brightness.
Parameter for:
Bins per plane for HSV/HSB histogram. This will result in bpp ** 3 bins.
Parameter for:
Bins per plane for HSV/HSB histogram. This will result in bpp ** 3 bins.
Parameter for:
Alpha value for hull drawing (in projected space!).
Default: Infinity
Parameter for:
Number of DBID to generate.
Parameter for:
First integer DBID to generate.
Default: 0
Parameter for:
Partially transparent filling of index pages.
Default: false
Parameter for:
The pagefile factory for storing the index.
Class Restriction: implements de.lmu.ifi.dbs.elki.persistent.PageFileFactory
Default: de.lmu.ifi.dbs.elki.persistent.MemoryPageFileFactory
Known implementations:
Parameter for:
The number of nearest neighbors of an object to be considered for computing its INFLO_SCORE.
Parameter for:
The threshold
Default: 1.0
Parameter for:
The data sources to join.
Parameter for:
The data sources to join.
Parameter for:
The data sources to join.
Parameter for:
Port for the JSON web server to listen on.
Default: 8080
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 de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions.KernelDensityFunction
Default: de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions.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 de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions.KernelDensityFunction
Default: de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions.EpanechnikovKernelDensityFunction
Known implementations:
Parameter for:
KMeans variant to run multiple times.
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeans
Known implementations:
Parameter for:
Number of blocks to use for processing. Means will be recomputed after each block.
Default: 10
Parameter for:
Random source for producing blocks.
Default: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
Parameter for:
Method to choose the initial means.
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansInitialization
Default: de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.RandomlyChosenInitialMeans
Known implementations:
Parameter for:
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMedoidsInitialization
Default: de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.PAMInitialMeans
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMedoidsInitialization
Default: de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.PAMInitialMeans
Default: de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansPlusPlusInitialMeans
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:
Parameter for:
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: -1
Quality measure variant for deciding which run to keep.
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.quality.KMeansQualityMeasure
Known implementations:
Parameter for:
The number of trials to run.
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 de.lmu.ifi.dbs.elki.utilities.scaling.outlier.OutlierScalingFunction
Default: de.lmu.ifi.dbs.elki.utilities.scaling.outlier.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 de.lmu.ifi.dbs.elki.datasource.DatabaseConnection
Known implementations:
Parameter for:
Random generator for sampling.
Default: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
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:
Specifies the distance of the k-distant object to be assessed.
Default: 1
Parameter for:
The average percentage of distances randomly choosen to be provided in the result.
Default: 1.0
Parameter for:
Specifies the k-nearest neighbors to be assigned.
Default: 1
Parameter for:
k nearest neighbor
Parameter for:
k nearest neighbor
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 directionfor 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 de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions.KernelDensityFunction
Default: de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions.GaussianKernelDensityFunction
Known implementations:
Parameter for:
The number of nearest neighbors of an object to be considered for computing its LDOF_SCORE.
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: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
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 de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction
Known implementations:
Parameter for:
The maximum radius of the neighborhood to be considered in the PCA.
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: 0.5
Parameter for:
Scaling factor for averaging neighborhood
Default: 4
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: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
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:
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 de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Known implementations:
Parameter for:
Distance function to determine the reference set of an object.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction
Known implementations:
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 de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Known implementations:
Parameter for:
the degree of the L-P-Norm (positive number)
Parameter for:
Number of hash buckets to use.
Default: 7919
Parameter for:
Hash function family to use for LSH.
Class Restriction: implements de.lmu.ifi.dbs.elki.index.lsh.hashfamilies.LocalitySensitiveHashFunctionFamily
Known implementations:
Parameter for:
Number of hash tables to use.
Parameter for:
the distance function to materialize the nearest neighbors
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction
Known implementations:
Parameter for:
The number of nearest neighbors of an object to be materialized.
Parameter for:
Kernel function to use with mean-shift clustering.
Class Restriction: implements de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions.KernelDensityFunction
Default: de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions.EpanechnikovKernelDensityFunction
Known implementations:
Parameter for:
Range of the kernel to use (aka: radius, bandwidth).
Parameter for:
Class to use as scaling function.
Class Restriction: implements de.lmu.ifi.dbs.elki.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 de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction
Known implementations:
Parameter for:
Insertion strategy to use for constructing the M-tree.
Class Restriction: implements de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.insert.MTreeInsert
Default: de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.insert.MinimumEnlargementInsert
Known implementations:
Parameter for:
Split strategy to use for constructing the M-tree.
Class Restriction: extends de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.split.MTreeSplit
Default: de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.split.MMRadSplit
Known implementations:
Parameter for:
The exponents to use for this distance function
Parameter for:
The neighborhood predicate to use in comparison step.
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood.NeighborSetPredicate$Factory
Known implementations:
Parameter for:
the distance function to use
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Known implementations:
Parameter for:
Parameter for the non-weighted neighborhood to use.
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood.NeighborSetPredicate$Factory
Known implementations:
Parameter for:
the number of neighbors
Parameter for:
Norm (length function) to use for computing the vector length.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DoubleNorm
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction
Known implementations:
Parameter for:
A list of the distribution estimators to try.
Default: [class de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.meta.BestFitEstimator]
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 de.lmu.ifi.dbs.elki.algorithm.clustering.OPTICSTypeAlgorithm
Default: de.lmu.ifi.dbs.elki.algorithm.clustering.OPTICS
Known implementations:
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: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
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:
Subspace clustering algorithm to use.
Class Restriction: implements de.lmu.ifi.dbs.elki.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 a page in bytes.
Default: 1024
Parameter for:
Column separator pattern. The default assumes whitespace separated data.
Default: (\s+|\s*[,;]\s*)
Parameter for:
Default: (\s+|\s*[,;]\s*)
Default: (\s+|\s*[,;]\s*)
Default: (\s+|\s*[,;]\s*)
Default: (\s+|\s*[,;]\s*)
Default: \s+
Default: (\s+|\s*[,;]\s*)
Default: (\s+|\s*[,;]\s*)
Default: (\s+|\s*[,;]\s*)
Default: (\s+|\s*[,;]\s*)
A comma separated list of the indices of labels (may be numeric), counting whitespace separated entries in a line starting with 0. The corresponding entries will be treated as a label.
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 de.lmu.ifi.dbs.elki.data.NumberVector$Factory
Default: de.lmu.ifi.dbs.elki.data.DoubleVector.Factory
Known implementations:
Parameter for:
Class Restriction: implements de.lmu.ifi.dbs.elki.data.SparseNumberVector$Factory
Class Restriction: implements de.lmu.ifi.dbs.elki.data.SparseNumberVector$Factory
Class Restriction: implements de.lmu.ifi.dbs.elki.data.SparseNumberVector$Factory
The number of partitions to use for approximate kNN.
Parameter for:
The random number generator seed.
Default: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
Parameter for:
A constant big value to reset high eigenvalues.
Default: 1.0
Parameter for:
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:
Parameter for:
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:
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:
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.
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: 0.01
Parameter for:
The number of strong eigenvectors: n eigenvectors with the n highesteigenvalues 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:
A constant small value to reset low eigenvalues.
Default: 0.0
Parameter for:
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:
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:
Multiplicator for neighborhood size.
Default: 3.0
Parameter for:
Sparsity of the random projection.
Default: 1.0
Parameter for:
Random generator seed.
Default: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
Parameter for:
Target dimensionality.
Parameter for:
a double between 0 and 1 specifying the threshold for small Eigenvalues (default is delta = 0.01).
Default: 0.01
Parameter for:
The multiplier for the initial number of medoids.
Default: 10
Parameter for:
The random number generator seed.
Default: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
Parameter for:
Distance function to determine the neighbors for variance analysis.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction
Known implementations:
Parameter for:
The maximum radius of the neighborhood to be considered.
Parameter for:
The intrinsic dimensionality of the clusters to find.
Parameter for:
Threshold for minimum number of points in the epsilon-neighborhood of a point.
Parameter for:
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:
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:
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:
Number of bins in the distribution histogram
Default: 50
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 de.lmu.ifi.dbs.elki.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 de.lmu.ifi.dbs.elki.data.projection.Projection
Known implementations:
Parameter for:
The random number seed.
Default: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
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 de.lmu.ifi.dbs.elki.math.linearalgebra.randomprojections.RandomProjectionFamily
Default: de.lmu.ifi.dbs.elki.math.linearalgebra.randomprojections.AchlioptasRandomProjectionFamily
Known implementations:
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 de.lmu.ifi.dbs.elki.datasource.DatabaseConnection
Known implementations:
Parameter for:
Random generator for sampling.
Default: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
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: 100
Parameter for:
Number of bins to use in the histogram
Default: 20
Parameter for:
The number of iterations to perform.
Default: 1000
Parameter for:
Random seed (optional).
Default: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
Parameter for:
Base distance function to use.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction
Known implementations:
Parameter for:
The number of nearest neighbors of an object to be considered for computing its reachability distance.
Parameter for:
The number of nearest neighbors
Parameter for:
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:
Parameter for:
Result handler class.
Parameter for:
Bins per plane for RGB histogram. This will result in bpp ** 3 bins.
Parameter for:
The dimensionality of the histogram in each color
Parameter for:
Strategy for spatial sorting in bulk loading.
Class Restriction: implements de.lmu.ifi.dbs.elki.math.spacefillingcurves.SpatialSorter
Known implementations:
Parameter for:
Insertion strategy for directory nodes.
Class Restriction: implements de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.insert.InsertionStrategy
Known implementations:
Parameter for:
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:
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 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:
Parameter for:
Minimum relative fill required for data pages.
Default: 0.4
Parameter for:
The strategy to use for handling overflows.
Class Restriction: implements de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.overflow.OverflowTreatment
Known implementations:
Parameter for:
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:
Parameter for:
The number of samples to draw.
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:
number of nearest neighbors to consider (at least 1)
Parameter for:
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:
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 de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.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 de.lmu.ifi.dbs.elki.distance.similarityfunction.SimilarityFunction
Default: de.lmu.ifi.dbs.elki.distance.similarityfunction.SharedNearestNeighborSimilarityFunction
Known implementations:
Parameter for:
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:
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:
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*(#|//|;).*$
Default: ^\s*(#|//|;).*$
Default: ^\s*(#|//|;).*$
Default: ^\s*(#|//|;).*$
Default: ^\s*(#|//|;).*$
Default: ^\s*(#|//|;).*$
Default: ^\s*(#|//|;).*$
Default: ^\s*(#|//|;).*$
Default: ^\s*#.*$
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 de.lmu.ifi.dbs.elki.distance.distancefunction.subspace.DimensionSelectingSubspaceDistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.subspace.SubspaceEuclideanDistanceFunction
Known implementations:
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:
Normalize vectors to manhattan length 1 (convert term counts to term frequencies)
Default: false
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:
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 de.lmu.ifi.dbs.elki.datasource.DatabaseConnection
Known implementations:
Parameter for:
Random generator for sampling.
Default: de.lmu.ifi.dbs.elki.utilities.RandomFactory@53c00f5e
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.
Default: ^\Qde.lmu.ifi.dbs.elki.visualization\E\..*
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: