Algorithm to run.
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.Algorithm
Known implementations:
Database connection class.
Class Restriction: implements de.lmu.ifi.dbs.elki.database.connection.DatabaseConnection
Default: de.lmu.ifi.dbs.elki.database.connection.FileBasedDatabaseConnection
Known implementations:
Normalization class in order to normalize values in the database.
Class Restriction: implements de.lmu.ifi.dbs.elki.normalization.Normalization
Known implementations:
Revert normalization result to original values - invalid option if no normalization has been performed.
Default: false
Result handler class.
Class Restriction: implements de.lmu.ifi.dbs.elki.result.ResultHandler
Default: de.lmu.ifi.dbs.elki.result.ResultWriter
Known implementations:
Enable verbose messages while performing the algorithm.
Default: false
Request output of performance time.
Default: false
Threshold for minimum frequency as percentage value (alternatively to parameter apriori.minsupp).
Threshold for minimum support as minimally required number of transactions (alternatively to parameter apriori.minfreq - setting apriori.minsupp is slightly preferable over setting apriori.minfreq in terms of efficiency).
Enable verbose messages while performing the algorithm.
Default: false
Request output of performance time.
Default: false
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
Threshold for output accuracy fraction digits.
Default: 4
Threshold for the size of the random sample to use. Default value is size of the complete dataset.
Flag to use random sample (use knn query around centroid, if flag is not set).
Default: false
Class used to compute the covariance matrix.
Class Restriction: extends de.lmu.ifi.dbs.elki.math.linearalgebra.pca.CovarianceMatrixBuilder
Default: de.lmu.ifi.dbs.elki.math.linearalgebra.pca.StandardCovarianceMatrixBuilder
Known implementations:
Filter class to determine the strong and weak eigenvectors.
Class Restriction: implements de.lmu.ifi.dbs.elki.math.linearalgebra.pca.EigenPairFilter
Default: de.lmu.ifi.dbs.elki.math.linearalgebra.pca.PercentageEigenPairFilter
Known implementations:
A constant big value to reset high eigenvalues.
Default: 1.0
A constant small value to reset low eigenvalues.
Default: 0.0
Enable verbose messages while performing the algorithm.
Default: false
Request output of performance time.
Default: false
Enable verbose messages while performing the algorithm.
Default: false
Request output of performance time.
Default: false
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
Specifies the distance of the k-distant object to be assessed.
Default: 1
The average percentage of distances randomly choosen to be provided in the result.
Default: 1.0
Enable verbose messages while performing the algorithm.
Default: false
Request output of performance time.
Default: false
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
Specifies the k-nearest neighbors to be assigned.
Default: 1
Enable verbose messages while performing the algorithm.
Default: false
Request output of performance time.
Default: false
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
Enable verbose messages while performing the algorithm.
Default: false
Request output of performance time.
Default: false
Algorithms to run
Enable verbose messages while performing the algorithm.
Default: false
Request output of performance time.
Default: false
Enable verbose messages while performing the algorithm.
Default: false
Request output of performance time.
Default: false
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
Enable verbose messages while performing the algorithm.
Default: false
Request output of performance time.
Default: false
Enable verbose messages while performing the algorithm.
Default: false
Request output of performance time.
Default: false
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
The maximum radius of the neighborhood to be considered.
Threshold for minimum number of points in the epsilon-neighborhood of a point.
Enable verbose messages while performing the algorithm.
Default: false
Request output of performance time.
Default: false
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
Threshold for minimum number of points within a cluster.
Enable verbose messages while performing the algorithm.
Default: false
Request output of performance time.
Default: false
The number of clusters to find.
The termination criterion for maximization of E(M): E(M) - E(M') < em.delta
Default: 0.0
Enable verbose messages while performing the algorithm.
Default: false
Request output of performance time.
Default: false
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
The number of clusters to find.
The maximum number of iterations to do. 0 means no limit.
Default: 0
Enable verbose messages while performing the algorithm.
Default: false
Request output of performance time.
Default: false
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
The maximum radius of the neighborhood to be considered.
Threshold for minimum number of points in the epsilon-neighborhood of a point.
Enable verbose messages while performing the algorithm.
Default: false
Request output of performance time.
Default: false
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
Enable verbose messages while performing the algorithm.
Default: false
Request output of performance time.
Default: false
The minimum SNN density.
Threshold for minimum number of points in the epsilon-SNN-neighborhood of a point.
The Classname of the preprocessor to determine the neighbors of the objects.
Class Restriction: extends de.lmu.ifi.dbs.elki.preprocessing.SharedNearestNeighborsPreprocessor
Default: de.lmu.ifi.dbs.elki.preprocessing.SharedNearestNeighborsPreprocessor
Known implementations:
Flag to omit (a new) preprocessing if for each object the association has already been set.
Default: false
Enable verbose messages while performing the algorithm.
Default: false
Request output of performance time.
Default: false
Enable verbose messages while performing the algorithm.
Default: false
Request output of performance time.
Default: false
Enable verbose messages while performing the algorithm.
Default: false
Request output of performance time.
Default: false
Threshold for minimum number of points in a cluster.
The maximum level for splitting the hypercube.
The minimum dimensionality of the subspaces to be found.
Default: 1
The maximum jitter for distance values.
Flag to indicate that an adjustment of the applied heuristic for choosing an interval is performed after an interval is selected.
Default: false
Enable verbose messages while performing the algorithm.
Default: false
Request output of performance time.
Default: false
Local PCA Preprocessor to derive partition criterion.
Class Restriction: extends de.lmu.ifi.dbs.elki.preprocessing.LocalPCAPreprocessor
Known implementations:
Distance to use for the inner algorithms.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.LocalPCAPreprocessorBasedDistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.LocallyWeightedDistanceFunction
Known implementations:
Clustering algorithm to apply to each partition.
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.clustering.ClusteringAlgorithm
Known implementations:
Database class for each partition. If this parameter is not set, the databases of the partitions have the same class as the original database.
Class Restriction: implements de.lmu.ifi.dbs.elki.database.Database
Known implementations:
Enable verbose messages while performing the algorithm.
Default: false
Request output of performance time.
Default: false
Local PCA Preprocessor to derive partition criterion.
Class Restriction: extends de.lmu.ifi.dbs.elki.preprocessing.LocalPCAPreprocessor
Known implementations:
Distance to use for the inner algorithms.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.LocalPCAPreprocessorBasedDistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.LocallyWeightedDistanceFunction
Known implementations:
Clustering algorithm to apply to each partition.
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.clustering.ClusteringAlgorithm
Known implementations:
Database class for each partition. If this parameter is not set, the databases of the partitions have the same class as the original database.
Class Restriction: implements de.lmu.ifi.dbs.elki.database.Database
Known implementations:
Enable verbose messages while performing the algorithm.
Default: false
Request output of performance time.
Default: false
Distance function to determine the neighbors for variance analysis.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
The maximum radius of the neighborhood to be considered.
Threshold for minimum number of points in the epsilon-neighborhood of a point.
Distance function to determine the distance between database objects.
Class Restriction: extends de.lmu.ifi.dbs.elki.distance.distancefunction.AbstractLocallyWeightedDistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.LocallyWeightedDistanceFunction
Known implementations:
The intrinsic dimensionality of the clusters to find.
Enable verbose messages while performing the algorithm.
Default: false
Request output of performance time.
Default: false
Specifies the smoothing factor. The mu-nearest neighbor is used to compute the correlation reachability of an object.
Optional parameter to specify the number of nearest neighbors considered in the PCA. If this parameter is not set, k is set to the value of parameter mu.
Threshold of a distance between a vector q and a given space that indicates that q adds a new dimension to the space.
Default: 0.25
The threshold for 'strong' eigenvectors: the 'strong' eigenvectors explain a portion of at least alpha of the total variance.
Default: 0.85
Enable verbose messages while performing the algorithm.
Default: false
Request output of performance time.
Default: false
The number of clusters to find.
The multiplier for the initial number of seeds.
Default: 30
The dimensionality of the clusters to find.
The factor for reducing the number of current clusters in each iteration.
Default: 0.5
Class used to compute the covariance matrix.
Class Restriction: extends de.lmu.ifi.dbs.elki.math.linearalgebra.pca.CovarianceMatrixBuilder
Default: de.lmu.ifi.dbs.elki.math.linearalgebra.pca.StandardCovarianceMatrixBuilder
Known implementations:
Enable verbose messages while performing the algorithm.
Default: false
Request output of performance time.
Default: false
The number of intervals (units) in each dimension.
The density threshold for the selectivity of a unit, where the selectivity isthe fraction of total feature vectors contained in this unit.
Flag to indicate that only subspaces with large coverage (i.e. the fraction of the database that is covered by the dense units) are selected, the rest will be pruned.
Default: false
Enable verbose messages while performing the algorithm.
Default: false
Request output of performance time.
Default: false
The maximum radius of the neighborhood to be considered in each dimension for determination of the preference vector.
Default: 0.0010
The minimum number of points as a smoothing factor to avoid the single-link-effekt.
Default: 1
Enable verbose messages while performing the algorithm.
Default: false
Request output of performance time.
Default: false
The number of nearest neighbors considered to determine the preference vector. If this value is not defined, k ist set to three times of the dimensionality of the database objects.
The maximum absolute variance along a coordinate axis.
Default: 0.01
Enable verbose messages while performing the algorithm.
Default: false
Request output of performance time.
Default: false
The number of clusters to find.
The multiplier for the initial number of seeds.
Default: 30
The dimensionality of the clusters to find.
The multiplier for the initial number of medoids.
Default: 10
Enable verbose messages while performing the algorithm.
Default: false
Request output of performance time.
Default: false
Distance function to determine the neighbors for variance analysis.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
The maximum radius of the neighborhood to be considered.
Threshold for minimum number of points in the epsilon-neighborhood of a point.
Distance function to determine the distance between database objects.
Class Restriction: extends de.lmu.ifi.dbs.elki.distance.distancefunction.AbstractLocallyWeightedDistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.LocallyWeightedDistanceFunction
Known implementations:
The intrinsic dimensionality of the clusters to find.
Enable verbose messages while performing the algorithm.
Default: false
Request output of performance time.
Default: false
Distance function to determine the distance between database objects.
Class Restriction: extends de.lmu.ifi.dbs.elki.distance.distancefunction.subspace.AbstractDimensionsSelectingDoubleDistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.subspace.DimensionsSelectingEuclideanDistanceFunction
Known implementations:
The maximum radius of the neighborhood to be considered.
Threshold for minimum number of points in the epsilon-neighborhood of a point.
Enable verbose messages while performing the algorithm.
Default: false
Request output of performance time.
Default: false
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
Parameter k for kNN queries.
Default: 30
Flag to indicate that the algorithm should run the fast/approximative version.
Default: false
Sample size to use in fast mode.
Kernel function to use.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel.KernelFunction
Default: de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel.PolynomialKernelFunction
Known implementations:
Enable verbose messages while performing the algorithm.
Default: false
Request output of performance time.
Default: false
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
size of the D-neighborhood
minimum fraction of objects that must be outside the D-neigborhood of an outlier
Enable verbose messages while performing the algorithm.
Default: false
Request output of performance time.
Default: false
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
size of the D-neighborhood
Enable verbose messages while performing the algorithm.
Default: false
Request output of performance time.
Default: false
The number of clusters to find.
The termination criterion for maximization of E(M): E(M) - E(M') < em.delta
Default: 0.0
Enable verbose messages while performing the algorithm.
Default: false
Request output of performance time.
Default: false
Invert the value range to [0:1], with 1 being outliers instead of 0.
Default: false
Enable verbose messages while performing the algorithm.
Default: false
Request output of performance time.
Default: false
expected fraction of outliers
cutoff
Default: 1.0E-7
Enable verbose messages while performing the algorithm.
Default: false
Request output of performance time.
Default: false
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
The number of nearest neighbors of an object to be considered for computing its INFLO_SCORE.
The threshold
Default: 1.0
Enable verbose messages while performing the algorithm.
Default: false
Request output of performance time.
Default: false
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
k nearest neighbor
Enable verbose messages while performing the algorithm.
Default: false
Request output of performance time.
Default: false
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
k nearest neighbor
Enable verbose messages while performing the algorithm.
Default: false
Request output of performance time.
Default: false
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
The number of nearest neighbors of an object to be considered for computing its LDOF_SCORE.
Enable verbose messages while performing the algorithm.
Default: false
Request output of performance time.
Default: false
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
The maximum radius of the neighborhood to be considered.
Minimum neighborhood size to be considered.
Scaling factor for averaging neighborhood
Default: 0.5
Enable verbose messages while performing the algorithm.
Default: false
Request output of performance time.
Default: false
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
The number of nearest neighbors of an object to be considered for computing its LOF_SCORE.
Distance function to determine the reachability distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Known implementations:
Enable verbose messages while performing the algorithm.
Default: false
Request output of performance time.
Default: false
The number of standard deviations to consider for density computation.
Default: 2.0
The number of nearest neighbors of an object to be considered for computing its LOOP_SCORE.
The number of nearest neighbors of an object to be used for the PRD value.
Distance function to determine the reference set of an object.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
Distance function to determine the reference set of an object.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Known implementations:
Preprocessor used to materialize the kNN neighborhoods.
Class Restriction: extends de.lmu.ifi.dbs.elki.preprocessing.MaterializeKNNPreprocessor
Default: de.lmu.ifi.dbs.elki.preprocessing.MaterializeKNNPreprocessor
Known implementations:
Enable verbose messages while performing the algorithm.
Default: false
Request output of performance time.
Default: false
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
Threshold for minimum number of points in the epsilon-neighborhood of a point.
Enable verbose messages while performing the algorithm.
Default: false
Request output of performance time.
Default: false
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
The heuristic for finding reference points.
Class Restriction: implements de.lmu.ifi.dbs.elki.utilities.referencepoints.ReferencePointsHeuristic
Default: de.lmu.ifi.dbs.elki.utilities.referencepoints.GridBasedReferencePoints
Known implementations:
The number of nearest neighbors
Enable verbose messages while performing the algorithm.
Default: false
Request output of performance time.
Default: false
The number of shared nearest neighbors to be considered for learning the subspace properties.
Default: 1
The multiplier for the discriminance value for discerning small from large variances.
Default: 1.1
The Classname of the preprocessor to determine the neighbors of the objects.
Class Restriction: extends de.lmu.ifi.dbs.elki.preprocessing.SharedNearestNeighborsPreprocessor
Default: de.lmu.ifi.dbs.elki.preprocessing.SharedNearestNeighborsPreprocessor
Known implementations:
Flag to omit (a new) preprocessing if for each object the association has already been set.
Default: false
Enable verbose messages while performing the algorithm.
Default: false
Request output of performance time.
Default: false
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
Number of bins to use in the histogram. By default, it is only guaranteed to be within 1*n and 2*n of the given number.
Default: 20
In a first pass, compute the exact minimum and maximum, at the cost of O(2*n*n) instead of O(n*n). The number of resulting bins is guaranteed to be as requested.
Default: false
Enable sampling of O(n) size to determine the minimum and maximum distances approximately. The resulting number of bins can be larger than the given n.
Default: false
Enable verbose messages while performing the algorithm.
Default: false
Request output of performance time.
Default: false
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
Number of bins to use in the histogram
Default: 20
Enable verbose messages while performing the algorithm.
Default: false
Request output of performance time.
Default: false
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
Enable verbose messages while performing the algorithm.
Default: false
Class that is used to generate a color histogram.
Class Restriction: implements de.lmu.ifi.dbs.elki.data.images.ComputeColorHistogram
Default: de.lmu.ifi.dbs.elki.data.images.ComputeNaiveRGBColorHistogram
Known implementations:
Input image for color histograms.
Enable verbose messages while performing the algorithm.
Default: false
the file to write the generated data set into, if the file already exists, the generated points will be appended to this file.
The generator specification file.
Factor for scaling the specified cluster sizes.
Default: 1.0
The random generator seed.
Enable verbose messages while performing the algorithm.
Default: false
Algorithm to run.
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.Algorithm
Known implementations:
Database connection class.
Class Restriction: implements de.lmu.ifi.dbs.elki.database.connection.DatabaseConnection
Default: de.lmu.ifi.dbs.elki.database.connection.FileBasedDatabaseConnection
Known implementations:
Normalization class in order to normalize values in the database.
Class Restriction: implements de.lmu.ifi.dbs.elki.normalization.Normalization
Known implementations:
Revert normalization result to original values - invalid option if no normalization has been performed.
Default: false
Result handler class.
Class Restriction: implements de.lmu.ifi.dbs.elki.result.ResultHandler
Default: de.lmu.ifi.dbs.elki.result.ResultWriter
Known implementations:
Enable verbose messages while performing the algorithm.
Default: false
Database connection class.
Class Restriction: implements de.lmu.ifi.dbs.elki.database.connection.DatabaseConnection
Default: de.lmu.ifi.dbs.elki.database.connection.FileBasedDatabaseConnection
Known implementations:
Distance function to cache.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Known implementations:
File name of the disk cache to create.
Enable verbose messages while performing the algorithm.
Default: false
Database connection class.
Class Restriction: implements de.lmu.ifi.dbs.elki.database.connection.DatabaseConnection
Default: de.lmu.ifi.dbs.elki.database.connection.FileBasedDatabaseConnection
Known implementations:
Distance function to cache.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Known implementations:
File name of the disk cache to create.
Enable verbose messages while performing the algorithm.
Default: false
Database connection class.
Class Restriction: implements de.lmu.ifi.dbs.elki.database.connection.DatabaseConnection
Default: de.lmu.ifi.dbs.elki.database.connection.FileBasedDatabaseConnection
Known implementations:
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
Normalization class in order to normalize values in the database.
Class Restriction: implements de.lmu.ifi.dbs.elki.normalization.Normalization
Known implementations:
Bins per plane for HSV/HSB histogram. This will result in bpp ** 3 bins.
Bins per plane for HSV/HSB histogram. This will result in bpp ** 3 bins.
Bins per plane for RGB histogram. This will result in bpp ** 3 bins.
Metrical index class to use.
Class Restriction: extends de.lmu.ifi.dbs.elki.index.tree.metrical.MetricalIndex
Known implementations:
Spatial index class to use.
Class Restriction: extends de.lmu.ifi.dbs.elki.index.tree.spatial.SpatialIndex
Known implementations:
Database class to be provided by the parse method.
Class Restriction: implements de.lmu.ifi.dbs.elki.database.Database
Default: de.lmu.ifi.dbs.elki.database.SequentialDatabase
Known implementations:
The index of the label to be used as class label.
Class label class to use.
Class Restriction: extends de.lmu.ifi.dbs.elki.data.ClassLabel
Default: de.lmu.ifi.dbs.elki.data.SimpleClassLabel
Known implementations:
The index of the label to be used as an external id. If the external id is an integer value the external id is also used as internal id, otherwise an association with de.lmu.ifi.dbs.elki.database.AssociationID@9aafa626 is set.
Database class to be provided by the parse method.
Class Restriction: implements de.lmu.ifi.dbs.elki.database.Database
Default: de.lmu.ifi.dbs.elki.database.SequentialDatabase
Known implementations:
The index of the label to be used as class label.
Class label class to use.
Class Restriction: extends de.lmu.ifi.dbs.elki.data.ClassLabel
Default: de.lmu.ifi.dbs.elki.data.SimpleClassLabel
Known implementations:
The index of the label to be used as an external id. If the external id is an integer value the external id is also used as internal id, otherwise an association with de.lmu.ifi.dbs.elki.database.AssociationID@9aafa626 is set.
Parser to provide the database.
Class Restriction: implements de.lmu.ifi.dbs.elki.parser.Parser
Default: de.lmu.ifi.dbs.elki.parser.DoubleVectorLabelParser
Known implementations:
Seed for randomly shuffling the rows for the database. If the parameter is not set, no shuffling will be performed.
The name of the input file to be parsed.
Database class to be provided by the parse method.
Class Restriction: implements de.lmu.ifi.dbs.elki.database.Database
Default: de.lmu.ifi.dbs.elki.database.SequentialDatabase
Known implementations:
The index of the label to be used as class label.
Class label class to use.
Class Restriction: extends de.lmu.ifi.dbs.elki.data.ClassLabel
Default: de.lmu.ifi.dbs.elki.data.SimpleClassLabel
Known implementations:
The index of the label to be used as an external id. If the external id is an integer value the external id is also used as internal id, otherwise an association with de.lmu.ifi.dbs.elki.database.AssociationID@9aafa626 is set.
Parser to provide the database.
Class Restriction: implements de.lmu.ifi.dbs.elki.parser.Parser
Default: de.lmu.ifi.dbs.elki.parser.DoubleVectorLabelParser
Known implementations:
Seed for randomly shuffling the rows for the database. If the parameter is not set, no shuffling will be performed.
Database class to be provided by the parse method.
Class Restriction: implements de.lmu.ifi.dbs.elki.database.Database
Default: de.lmu.ifi.dbs.elki.database.SequentialDatabase
Known implementations:
The index of the label to be used as class label.
Class label class to use.
Class Restriction: extends de.lmu.ifi.dbs.elki.data.ClassLabel
Default: de.lmu.ifi.dbs.elki.data.SimpleClassLabel
Known implementations:
The index of the label to be used as an external id. If the external id is an integer value the external id is also used as internal id, otherwise an association with de.lmu.ifi.dbs.elki.database.AssociationID@9aafa626 is set.
A comma separated list of the names of the input files to be parsed.
Comma separated list of classnames specifying the parsers to provide a database. If this parameter is not set, de.lmu.ifi.dbs.elki.parser.DoubleVectorLabelParser is used as parser for all input files.
Preprocessor class to determine the correlation dimension of each object.
Class Restriction: implements de.lmu.ifi.dbs.elki.preprocessing.Preprocessor
Default: de.lmu.ifi.dbs.elki.preprocessing.KnnQueryBasedLocalPCAPreprocessor
Known implementations:
Flag to omit (a new) preprocessing if for each object the association has already been set.
Default: false
the kernel function which is used to compute the similarity.Default: class de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel.LinearKernelFunction
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel.KernelFunction
Default: de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel.LinearKernelFunction
Known implementations:
the degree of the L-P-Norm (positive number)
Preprocessor class to determine the correlation dimension of each object.
Class Restriction: implements de.lmu.ifi.dbs.elki.preprocessing.Preprocessor
Default: de.lmu.ifi.dbs.elki.preprocessing.KnnQueryBasedLocalPCAPreprocessor
Known implementations:
Flag to omit (a new) preprocessing if for each object the association has already been set.
Default: false
Similarity function to derive the distance between database objects from.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.similarityfunction.NormalizedSimilarityFunction
Default: de.lmu.ifi.dbs.elki.distance.similarityfunction.FractionalSharedNearestNeighborSimilarityFunction
Known implementations:
Similarity function to derive the distance between database objects from.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.similarityfunction.NormalizedSimilarityFunction
Default: de.lmu.ifi.dbs.elki.distance.similarityfunction.FractionalSharedNearestNeighborSimilarityFunction
Known implementations:
Similarity function to derive the distance between database objects from.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.similarityfunction.NormalizedSimilarityFunction
Default: de.lmu.ifi.dbs.elki.distance.similarityfunction.FractionalSharedNearestNeighborSimilarityFunction
Known implementations:
The dimensionality of the histogram in hue, saturation and brightness.
The dimensionality of the histogram in each color
Preprocessor class to determine the correlation dimension of each object.
Class Restriction: extends de.lmu.ifi.dbs.elki.preprocessing.LocalPCAPreprocessor
Default: de.lmu.ifi.dbs.elki.preprocessing.KnnQueryBasedLocalPCAPreprocessor
Known implementations:
Flag to omit (a new) preprocessing if for each object the association has already been set.
Default: false
Threshold for approximate linear dependency: the strong eigenvectors of q are approximately linear dependent from the strong eigenvectors p if the following condition holds for all stroneg eigenvectors q_i of q (lambda_q < lambda_p): q_i' * M^check_p * q_i <= delta^2.
Default: 0.1
Threshold for the maximum distance between two approximately linear dependent subspaces of two objects p and q (lambda_q < lambda_p) before considering them as parallel.
Default: 0.1
Preprocessor class to determine the correlation dimension of each object.
Class Restriction: extends de.lmu.ifi.dbs.elki.preprocessing.LocalPCAPreprocessor
Default: de.lmu.ifi.dbs.elki.preprocessing.KnnQueryBasedLocalPCAPreprocessor
Known implementations:
Flag to omit (a new) preprocessing if for each object the association has already been set.
Default: false
Threshold of a distance between a vector q and a given space that indicates that q adds a new dimension to the space.
Default: 0.25
The name of the file containing the distance matrix.
The name of the file containing the distance matrix.
The name of the file containing the distance matrix.
Parser used to load the distance matrix.
Class Restriction: implements de.lmu.ifi.dbs.elki.parser.DistanceParser
Default: de.lmu.ifi.dbs.elki.parser.NumberDistanceParser
Known implementations:
The name of the file containing the distance matrix.
Parser used to load the distance matrix.
Class Restriction: implements de.lmu.ifi.dbs.elki.parser.DistanceParser
Default: de.lmu.ifi.dbs.elki.parser.NumberDistanceParser
Known implementations:
Preprocessor class to determine the preference vector of each object.
Class Restriction: implements de.lmu.ifi.dbs.elki.preprocessing.PreferenceVectorPreprocessor
Default: de.lmu.ifi.dbs.elki.preprocessing.DiSHPreprocessor
Known implementations:
Flag to omit (a new) preprocessing if for each object the association has already been set.
Default: false
The maximum distance between two vectors with equal preference vectors before considering them as parallel.
Default: 0.0010
an integer between 1 and the dimensionality of the feature space 1 specifying the dimension to be considered for distance computation.
a comma separated array of integer values, where 1 <= d_i <= the dimensionality of the feature space specifying the dimensions to be considered for distance computation. If this parameter is not set, no dimensions will be considered, i.e. the distance between two objects is always 0.
Preprocessor class to determine the preference vector of each object.
Class Restriction: implements de.lmu.ifi.dbs.elki.preprocessing.PreferenceVectorPreprocessor
Default: de.lmu.ifi.dbs.elki.preprocessing.HiSCPreprocessor
Known implementations:
Flag to omit (a new) preprocessing if for each object the association has already been set.
Default: false
The maximum distance between two vectors with equal preference vectors before considering them as parallel.
Default: 0.0010
Preprocessor class to determine the correlation dimension of each object.
Class Restriction: extends de.lmu.ifi.dbs.elki.preprocessing.LocalPCAPreprocessor
Default: de.lmu.ifi.dbs.elki.preprocessing.KnnQueryBasedLocalPCAPreprocessor
Known implementations:
Flag to omit (a new) preprocessing if for each object the association has already been set.
Default: false
the band size for Edit Distance alignment (positive double value, 0 <= bandSize <= 1)
Default: 0.1
the band size for Edit Distance alignment (positive double value, 0 <= bandSize <= 1)
Default: 0.1
the delta parameter (similarity threshold) for EDR (positive number)
Default: 1.0
the band size for Edit Distance alignment (positive double value, 0 <= bandSize <= 1)
Default: 0.1
the g parameter ERP (positive number)
Default: 0.0
the allowed deviation in x direction for LCSS alignment (positive double value, 0 <= pDelta <= 1)
Default: 0.1
the allowed deviation in y directionfor LCSS alignment (positive double value, 0 <= pEpsilon <= 1)
Default: 0.05
The Classname of the preprocessor to determine the neighbors of the objects.
Class Restriction: extends de.lmu.ifi.dbs.elki.preprocessing.SharedNearestNeighborsPreprocessor
Default: de.lmu.ifi.dbs.elki.preprocessing.SharedNearestNeighborsPreprocessor
Known implementations:
Flag to omit (a new) preprocessing if for each object the association has already been set.
Default: false
The Classname of the preprocessor to determine the neighbors of the objects.
Class Restriction: extends de.lmu.ifi.dbs.elki.preprocessing.SharedNearestNeighborsPreprocessor
Default: de.lmu.ifi.dbs.elki.preprocessing.SharedNearestNeighborsPreprocessor
Known implementations:
Flag to omit (a new) preprocessing if for each object the association has already been set.
Default: false
The max degree of theFooKernelFunction. Default: 2
The degree of the polynomial kernel function. Default: 2.0
Default: 2.0
Enable verbose messages while performing the algorithm.
Default: false
Request output of performance time.
Default: false
Class label for the 'positive' class.
Algorithm to run.
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.Algorithm
Known implementations:
number of bins
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:
Use separate frequencies for outliers and non-outliers.
Default: false
Enable verbose messages while performing the algorithm.
Default: false
Request output of performance time.
Default: false
Class label for the 'positive' class.
Algorithm to run.
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.Algorithm
Known implementations:
The name of the file storing the index. If this parameter is not set the index is hold in the main memory.
The size of a page in bytes.
Default: 4000
The size of the cache in bytes.
Default: 2147483647
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
positive integer specifying the maximal number k of reversek nearest neighbors to be supported.
positive integer specifying the order of the polynomial approximation.
Flag to indicate that the approximation is done in the ''normal'' space instead of the log-log space (which is default).
Default: false
The name of the file storing the index. If this parameter is not set the index is hold in the main memory.
The size of a page in bytes.
Default: 4000
The size of the cache in bytes.
Default: 2147483647
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
positive integer specifying the maximal number k of reversek nearest neighbors to be supported.
The name of the file storing the index. If this parameter is not set the index is hold in the main memory.
The size of a page in bytes.
Default: 4000
The size of the cache in bytes.
Default: 2147483647
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
Specifies the maximal number k of reverse k nearest neighbors to be supported.
The name of the file storing the index. If this parameter is not set the index is hold in the main memory.
The size of a page in bytes.
Default: 4000
The size of the cache in bytes.
Default: 2147483647
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
Specifies the maximal number k of reverse k nearest neighbors to be supported.
The name of the file storing the index. If this parameter is not set the index is hold in the main memory.
The size of a page in bytes.
Default: 4000
The size of the cache in bytes.
Default: 2147483647
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
The name of the file storing the index. If this parameter is not set the index is hold in the main memory.
The size of a page in bytes.
Default: 4000
The size of the cache in bytes.
Default: 2147483647
flag to specify bulk load (default is no bulk load)
Default: false
the strategy for bulk load, available strategies are: [MAX_EXTENSION| ZCURVE](default is ZCURVE)
Default: ZCURVE
The name of the file storing the index. If this parameter is not set the index is hold in the main memory.
The size of a page in bytes.
Default: 4000
The size of the cache in bytes.
Default: 2147483647
flag to specify bulk load (default is no bulk load)
Default: false
the strategy for bulk load, available strategies are: [MAX_EXTENSION| ZCURVE](default is ZCURVE)
Default: ZCURVE
positive integer specifying the maximal number k of reverse k nearest neighbors to be supported.
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.index.tree.spatial.SpatialDistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
The name of the file storing the index. If this parameter is not set the index is hold in the main memory.
The size of a page in bytes.
Default: 4000
The size of the cache in bytes.
Default: 2147483647
flag to specify bulk load (default is no bulk load)
Default: false
the strategy for bulk load, available strategies are: [MAX_EXTENSION| ZCURVE](default is ZCURVE)
Default: ZCURVE
A comma separated list of the class names of the filters to be used. The specified filters will be applied sequentially in the given order.
The number of strong eigenvectors: n eigenvectors with the n highesteigenvalues are marked as strong eigenvectors.
Flag to mark delta as an absolute value.
Default: false
The threshold for strong Eigenvalues. If not otherwise specified, delta is a relative value w.r.t. the (absolute) highest Eigenvalues and has to be a double between 0 and 1. To mark delta as an absolute value, use the option -pca.filter.absolute.
Default: 0.01
Class used to compute the covariance matrix.
Class Restriction: extends de.lmu.ifi.dbs.elki.math.linearalgebra.pca.CovarianceMatrixBuilder
Default: de.lmu.ifi.dbs.elki.math.linearalgebra.pca.StandardCovarianceMatrixBuilder
Known implementations:
Filter class to determine the strong and weak eigenvectors.
Class Restriction: implements de.lmu.ifi.dbs.elki.math.linearalgebra.pca.EigenPairFilter
Default: de.lmu.ifi.dbs.elki.math.linearalgebra.pca.PercentageEigenPairFilter
Known implementations:
A constant big value to reset high eigenvalues.
Default: 1.0
A constant small value to reset low eigenvalues.
Default: 0.0
Class used to compute the covariance matrix.
Class Restriction: extends de.lmu.ifi.dbs.elki.math.linearalgebra.pca.CovarianceMatrixBuilder
Default: de.lmu.ifi.dbs.elki.math.linearalgebra.pca.StandardCovarianceMatrixBuilder
Known implementations:
The share (0.0 to 1.0) of variance that needs to be explained by the 'strong' eigenvectors.The filter class will choose the number of strong eigenvectors by this share.
Default: 0.85
The share (0.0 to 1.0) of variance that needs to be explained by the 'strong' eigenvectors.The filter class will choose the number of strong eigenvectors by this share.
Default: 0.5
The minimum strength of the statistically expected variance (1/n) share an eigenvector needs to have to be considered 'strong'.
Default: 0.95
The sensitivity niveau for weak eigenvectors: An eigenvector which is at less than the given share of the statistical average variance is considered weak.
Default: 1.1
The minimum strength of the statistically expected variance (1/n) share an eigenvector needs to have to be considered 'strong'.
Default: 0.0
The minimum strength of the statistically expected variance (1/n) share an eigenvector needs to have to be considered 'strong'.
Default: 0.95
Weight function to use in weighted PCA.
Class Restriction: implements de.lmu.ifi.dbs.elki.math.linearalgebra.pca.weightfunctions.WeightFunction
Default: de.lmu.ifi.dbs.elki.math.linearalgebra.pca.weightfunctions.ConstantWeight
Known implementations:
a comma separated concatenation of the minimum values in each dimension that are mapped to 0. If no value is specified, the minimum value of the attribute range in this dimension will be taken.
a comma separated concatenation of the maximum values in each dimension that are mapped to 1. If no value is specified, the maximum value of the attribute range in this dimension will be taken.
a comma separated concatenation of the mean values in each dimension that are mapped to 0. If no value is specified, the mean value of the attribute range in this dimension will be taken.
a comma separated concatenation of the standard deviations in each dimension that are scaled to 1. If no value is specified, the standard deviation of the attribute range in this dimension will be taken.
A comma separated list of normalizations for each representation. If in one representation no normalization is desired, please use the class 'de.lmu.ifi.dbs.elki.normalization.DummyNormalization' in the list.
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.
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.
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.
Distance function.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Known implementations:
A comma separated list of the indices of labels (may be numeric), counting whitespace separated entries in a line starting with 0. The corresponding entries will be treated as a label.
Parser to use as base parser
Class Restriction: implements de.lmu.ifi.dbs.elki.parser.Parser
Default: de.lmu.ifi.dbs.elki.parser.DoubleVectorLabelParser
Known implementations:
a comma separated array of integer values d_i, where 1 <= 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
Parser to use as base parser
Class Restriction: implements de.lmu.ifi.dbs.elki.parser.Parser
Default: de.lmu.ifi.dbs.elki.parser.DoubleVectorLabelParser
Known implementations:
number of selected attributes
Default: 1
Parser to use as base parser
Class Restriction: implements de.lmu.ifi.dbs.elki.parser.Parser
Default: de.lmu.ifi.dbs.elki.parser.DoubleVectorLabelParser
Known implementations:
a comma separated array of integer values d_i, where 1 <= 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
Parser to use as base parser
Class Restriction: implements de.lmu.ifi.dbs.elki.parser.Parser
Default: de.lmu.ifi.dbs.elki.parser.DoubleVectorLabelParser
Known implementations:
number of selected attributes
Default: 1
Positive threshold for minumum numbers of points in the epsilon-neighborhood of a point. The value of the preference vector in dimension d_i is set to 1 if the epsilon neighborhood contains more than dish.minpts points and the following condition holds: for all dimensions d_j: |neighbors(d_i) intersection neighbors(d_j)| >= dish.minpts.
A comma separated list of positive doubles specifying the maximum radius of the neighborhood to be considered in each dimension for determination of the preference vector (default is 0.0010 in each dimension). If only one value is specified, this value will be used for each dimension.
Default: [0.0010]
The strategy for determination of the preference vector, available strategies are: [APRIORI| MAX_INTERSECTION](default is MAX_INTERSECTION)
Default: MAX_INTERSECTION
Distance function to determine the neighbors for variance analysis.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
The maximum radius of the neighborhood to be considered.
Threshold for minimum number of points in the epsilon-neighborhood of a point.
Flag to mark delta as an absolute value.
Default: false
The threshold for strong Eigenvalues. If not otherwise specified, delta is a relative value w.r.t. the (absolute) highest Eigenvalues and has to be a double between 0 and 1. To mark delta as an absolute value, use the option -pca.filter.absolute.
Default: 0.01
The maximum absolute variance along a coordinate axis.
Default: 0.01
The number of nearest neighbors considered to determine the preference vector. If this value is not defined, k ist set to three times of the dimensionality of the database objects.
The distance function used to select objects for running PCA.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
Class used to compute the covariance matrix.
Class Restriction: extends de.lmu.ifi.dbs.elki.math.linearalgebra.pca.CovarianceMatrixBuilder
Default: de.lmu.ifi.dbs.elki.math.linearalgebra.pca.StandardCovarianceMatrixBuilder
Known implementations:
Filter class to determine the strong and weak eigenvectors.
Class Restriction: implements de.lmu.ifi.dbs.elki.math.linearalgebra.pca.EigenPairFilter
Default: de.lmu.ifi.dbs.elki.math.linearalgebra.pca.PercentageEigenPairFilter
Known implementations:
A constant big value to reset high eigenvalues.
Default: 1.0
A constant small value to reset low eigenvalues.
Default: 0.0
The number of nearest neighbors considered in the PCA. If this parameter is not set, k ist set to three times of the dimensionality of the database objects.
The number of nearest neighbors of an object to be materialized.
the distance function to materialize the nearest neighbors
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
Distance function to determine the neighbors for variance analysis.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
The maximum radius of the neighborhood to be considered.
Threshold for minimum number of points in the epsilon-neighborhood of a point.
a double between 0 and 1 specifying the threshold for small Eigenvalues (default is delta = 0.01).
Default: 0.01
The distance function used to select objects for running PCA.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
Class used to compute the covariance matrix.
Class Restriction: extends de.lmu.ifi.dbs.elki.math.linearalgebra.pca.CovarianceMatrixBuilder
Default: de.lmu.ifi.dbs.elki.math.linearalgebra.pca.StandardCovarianceMatrixBuilder
Known implementations:
Filter class to determine the strong and weak eigenvectors.
Class Restriction: implements de.lmu.ifi.dbs.elki.math.linearalgebra.pca.EigenPairFilter
Default: de.lmu.ifi.dbs.elki.math.linearalgebra.pca.PercentageEigenPairFilter
Known implementations:
A constant big value to reset high eigenvalues.
Default: 1.0
A constant small value to reset low eigenvalues.
Default: 0.0
The maximum radius of the neighborhood to be considered in the PCA.
number of nearest neighbors to consider (at least 1)
Default: 1
the distance function to asses the nearest neighbors
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
The number of nearest neighbors of an object to be materialized.
the distance function to materialize the nearest neighbors
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
Directory name (or name of an existing file) to write the obtained results in. If this parameter is omitted, per default the output will sequentially be given to STDOUT.
Enable gzip compression of output files.
Default: false
Silently overwrite output files.
Default: false
Scale the data space extension by the given factor.
Default: 1.0
The number of partitions in each dimension. Points will be placed on the edges of the grid, except for a grid size of 0, where only the mean is generated as reference point.
Default: 1
Scale the grid by the given factor. This can be used to obtain reference points outside the used data space.
Default: 1.0
The number of reference points to be generated.
Scale the grid by the given factor. This can be used to obtain reference points outside the used data space.
Default: 1.0
The number of samples to draw.
Do not use the center as extra reference point.
Default: false
Scale the reference points by the given factor. This can be used to obtain reference points outside the used data space.
Default: 1.0
Minimum value to allow.
Maximum value to allow.
Fixed mean to use in standard deviation scaling.
Significance level to use for error function.
Default: 3.0
Regularize scores before using Gamma scaling.
Default: false
Fixed minimum to use in lienar scaling.
Fixed maximum to use in linear scaling.
Use the mean as minimum for scaling.
Default: false
Fixed minimum to use in sqrt scaling.
Fixed maximum to use in sqrt scaling.
Fixed minimum to use in sqrt scaling.
Fixed mean to use in standard deviation scaling.
Significance level to use for error function.
Default: 3.0
Fixed mean to use in standard deviation scaling.
Significance level to use for error function.
Default: 3.0
Number of outliers to keep.
Make the top k a binary scaling.
Default: false
Title to use for visualization window.
Maximum number of dimensions to display.
Default: 10
Style properties file to use
Default: default
Use curves instead of the stacked histogram style.
Default: false
Number of bins in the distribution histogram
Default: 20
Number of digits to show (e.g. when visualizing outlier scores)
Default: 4
Half-transparent filling of bubbles.
Default: false
Additional scaling function for bubbles.
Class Restriction: implements de.lmu.ifi.dbs.elki.utilities.scaling.outlier.OutlierScalingFunction
Known implementations:
A gamma-correction.
Default: 1.0
Style properties file to use
Default: default
Use curves instead of the stacked histogram style.
Default: false
Number of bins in the distribution histogram
Default: 20
Number of digits to show (e.g. when visualizing outlier scores)
Default: 4
Half-transparent filling of bubbles.
Default: false
Additional scaling function for bubbles.
Class Restriction: implements de.lmu.ifi.dbs.elki.utilities.scaling.outlier.OutlierScalingFunction
Known implementations:
A gamma-correction.
Default: 1.0
Use curves instead of the stacked histogram style.
Default: false
Number of bins in the distribution histogram
Default: 20
Number of digits to show (e.g. when visualizing outlier scores)
Default: 4
Half-transparent filling of bubbles.
Default: false
Additional scaling function for bubbles.
Class Restriction: implements de.lmu.ifi.dbs.elki.utilities.scaling.outlier.OutlierScalingFunction
Known implementations:
A gamma-correction.
Default: 1.0
Use curves instead of the stacked histogram style.
Default: false
Number of bins in the distribution histogram
Default: 20
Half-transparent filling of bubbles.
Default: false
Additional scaling function for bubbles.
Class Restriction: implements de.lmu.ifi.dbs.elki.utilities.scaling.outlier.OutlierScalingFunction
Known implementations:
A gamma-correction.
Default: 1.0
Number of digits to show (e.g. when visualizing outlier scores)
Default: 4