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:
Class to obtain a description of. Causes immediate stop of the program.
Class Restriction: implements de.lmu.ifi.dbs.elki.utilities.optionhandling.Parameterizable
Request a help-message, either for the main-routine or for any specified algorithm. Causes immediate stop of the program.
Request a help-message, either for the main-routine or for any specified algorithm. Causes immediate stop of the program.
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.
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.
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).
Request output of performance time.
Enable verbose messages while performing the algorithm.
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
Flag to use random sample (use knn query around centroid, if flag is not set).
Threshold for the size of the random sample to use. Default value is size of the complete dataset.
Request output of performance time.
Enable verbose messages while performing the algorithm.
Request output of performance time.
Enable verbose messages while performing the algorithm.
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
Request output of performance time.
Enable verbose messages while performing the algorithm.
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
Request output of performance time.
Enable verbose messages while performing the algorithm.
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:
Request output of performance time.
Enable verbose messages while performing the algorithm.
Request output of performance time.
Enable verbose messages while performing the algorithm.
Request output of performance time.
Enable verbose messages while performing the algorithm.
Request output of performance time.
Enable verbose messages while performing the algorithm.
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.
Request output of performance time.
Enable verbose messages while performing the algorithm.
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.
Request output of performance time.
Enable verbose messages while performing the algorithm.
The termination criterion for maximization of E(M): E(M) - E(M') < em.delta
Default: 0.0
The number of clusters to find.
Request output of performance time.
Enable verbose messages while performing the algorithm.
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.
Request output of performance time.
Enable verbose messages while performing the algorithm.
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.
Request output of performance time.
Enable verbose messages while performing the algorithm.
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:
Request output of performance time.
Enable verbose messages while performing the algorithm.
The minimum SNN density.
Threshold for minimum number of points in the epsilon-SNN-neighborhood of a point.
Request output of performance time.
Enable verbose messages while performing the algorithm.
Request output of performance time.
Enable verbose messages while performing the algorithm.
Request output of performance time.
Enable verbose messages while performing the algorithm.
Flag to indicate that an adjustment of the applied heuristic for choosing an interval is performed after an interval is selected.
The maximum jitter for distance values.
The maximum level for splitting the hypercube.
The minimum dimensionality of the subspaces to be found.
Default: 1
Threshold for minimum number of points in a cluster.
Request output of performance time.
Enable verbose messages while performing the algorithm.
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:
Preprocessor to derive partition criterion.
Class Restriction: extends de.lmu.ifi.dbs.elki.preprocessing.HiCOPreprocessor
Known implementations:
Request output of performance time.
Enable verbose messages while performing the algorithm.
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:
Preprocessor to derive partition criterion.
Class Restriction: extends de.lmu.ifi.dbs.elki.preprocessing.HiCOPreprocessor
Known implementations:
Request output of performance time.
Enable verbose messages while performing the algorithm.
Request output of performance time.
Enable verbose messages while performing the algorithm.
Distance function to determine the distance between database objects.
Class Restriction: extends de.lmu.ifi.dbs.elki.distance.distancefunction.LocallyWeightedDistanceFunction
The maximum radius of the neighborhood to be considered.
The intrinsic dimensionality of the clusters to find.
Threshold for minimum number of points in the epsilon-neighborhood of a point.
Request output of performance time.
Enable verbose messages while performing the algorithm.
The factor for reducing the number of current clusters in each iteration.
Default: 0.5
The number of clusters to find.
The multiplier for the initial number of seeds.
Default: 30
The dimensionality of the clusters to find.
Request output of performance time.
Enable verbose messages while performing the algorithm.
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.
The density threshold for the selectivity of a unit, where the selectivity isthe fraction of total feature vectors contained in this unit.
The number of intervals (units) in each dimension.
Request output of performance time.
Enable verbose messages while performing the algorithm.
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
Request output of performance time.
Enable verbose messages while performing the algorithm.
The multiplier for the initial number of medoids.
Default: 10
The number of clusters to find.
The multiplier for the initial number of seeds.
Default: 30
The dimensionality of the clusters to find.
Request output of performance time.
Enable verbose messages while performing the algorithm.
Distance function to determine the distance between database objects.
Class Restriction: extends de.lmu.ifi.dbs.elki.distance.distancefunction.LocallyWeightedDistanceFunction
The maximum radius of the neighborhood to be considered.
The intrinsic dimensionality of the clusters to find.
Threshold for minimum number of points in the epsilon-neighborhood of a point.
Request output of performance time.
Enable verbose messages while performing the algorithm.
Flag to indicate that the algorithm should run the fast/approximative version.
Parameter k for kNN queries.
Default: 30
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:
Sample size to use in fast mode.
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:
Request output of performance time.
Enable verbose messages while performing the algorithm.
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:
Scaling factor for averaging neighborhood
Default: 0.5
Minimum neighborhood size to be considered.
Default: 20
The maximum radius of the neighborhood to be considered.
Request output of performance time.
Enable verbose messages while performing the algorithm.
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:
Request output of performance time.
Enable verbose messages while performing the algorithm.
The multiplier for the discriminance value for discerning small from large variances.
Default: 1.1
The number of shared nearest neighbors to be considered for learning the subspace properties.
Default: 1
Request output of performance time.
Enable verbose messages while performing the algorithm.
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 sampling to reduce runtime from O(2*n*n) to O(n*n)+O(n) at the cost of evenutally having more than the configured number of bins.
Number of bins to use in the histogram
Default: 20
Request output of performance time.
Enable verbose messages while performing the algorithm.
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
Request output of performance time.
Enable verbose messages while performing the algorithm.
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:
Request output of performance time.
Enable verbose messages while performing the algorithm.
the file to write the generated data set into, if the file already exists, the generated points will be appended to this file.
The random generator seed.
Factor for scaling the specified cluster sizes.
Default: 1.0
The generator specification file.
Class to obtain a description of. Causes immediate stop of the program.
Class Restriction: implements de.lmu.ifi.dbs.elki.utilities.optionhandling.Parameterizable
Request a help-message, either for the main-routine or for any specified algorithm. Causes immediate stop of the program.
Request a help-message, either for the main-routine or for any specified algorithm. Causes immediate stop of the program.
Enable verbose messages while performing the algorithm.
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:
Class to obtain a description of. Causes immediate stop of the program.
Class Restriction: implements de.lmu.ifi.dbs.elki.utilities.optionhandling.Parameterizable
Request a help-message, either for the main-routine or for any specified algorithm. Causes immediate stop of the program.
Request a help-message, either for the main-routine or for any specified algorithm. Causes immediate stop of the program.
File name of the disk cache to create.
Distance function to cache.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Known implementations:
Enable verbose messages while performing the algorithm.
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:
Class to obtain a description of. Causes immediate stop of the program.
Class Restriction: implements de.lmu.ifi.dbs.elki.utilities.optionhandling.Parameterizable
Request a help-message, either for the main-routine or for any specified algorithm. Causes immediate stop of the program.
Request a help-message, either for the main-routine or for any specified algorithm. Causes immediate stop of the program.
File name of the disk cache to create.
Distance function to cache.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Known implementations:
Enable verbose messages while performing the algorithm.
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:
Class to obtain a description of. Causes immediate stop of the program.
Class Restriction: implements de.lmu.ifi.dbs.elki.utilities.optionhandling.Parameterizable
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:
Request a help-message, either for the main-routine or for any specified algorithm. Causes immediate stop of the program.
Request a help-message, either for the main-routine or for any specified algorithm. Causes immediate stop of the program.
Normalization class in order to normalize values in the database.
Class Restriction: implements de.lmu.ifi.dbs.elki.normalization.Normalization
Known implementations:
Enable verbose messages while performing the algorithm.
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:
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 class label.
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 an external id.
The name of the input file to be parsed.
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:
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 class label.
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 an external id.
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:
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 class label.
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 an external id.
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.
the kernel function which is used to compute the similarity.Default: 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)
A comma separated list of the distance functions to determine the distance between objects within one representation.
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:
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 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:
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.
The maximum distance between two vectors with equal preference vectors before considering them as parallel.
Default: 0.0010
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 max degree of theFooKernelFunction. Default: 2
Default: 2
The degree of the polynomial kernel function. Default: 2.0
Default: 2.0
Algorithm to run.
Class Restriction: implements de.lmu.ifi.dbs.elki.algorithm.Algorithm
Known implementations:
Class label for the 'positive' class.
Request output of performance time.
Enable verbose messages while performing the algorithm.
positive integer specifying the maximal number k of reversek nearest neighbors to be supported.
Flag to indicate that the approximation is done in the ''normal'' space instead of the log-log space (which is default).
positive integer specifying the order of the polynomial approximation.
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 size of the cache in bytes.
Default: 2147483647
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
positive integer specifying the maximal number k of reversek nearest neighbors to be supported.
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 size of the cache in bytes.
Default: 2147483647
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
Specifies 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.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
The size of the cache in bytes.
Default: 2147483647
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
Specifies 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.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
The size of the cache in bytes.
Default: 2147483647
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
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 size of the cache in bytes.
Default: 2147483647
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
flag to specify bulk load (default is no bulk load)
the strategy for bulk load, available strategies are: [MAX_EXTENSION| ZCURVE](default is ZCURVE)
Default: ZCURVE
The size of the cache in bytes.
Default: 2147483647
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
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:
positive integer specifying the maximal number k of reverse k nearest neighbors to be supported.
flag to specify bulk load (default is no bulk load)
the strategy for bulk load, available strategies are: [MAX_EXTENSION| ZCURVE](default is ZCURVE)
Default: ZCURVE
The size of the cache in bytes.
Default: 2147483647
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
flag to specify bulk load (default is no bulk load)
the strategy for bulk load, available strategies are: [MAX_EXTENSION| ZCURVE](default is ZCURVE)
Default: ZCURVE
The size of the cache in bytes.
Default: 2147483647
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
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.
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
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:
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:
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:
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:
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:
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:
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
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:
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:
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:
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 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 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 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.
Index of a class label (may be numeric), counting whitespace separated entries in a line starting with 0 - the corresponding entry will be treated as a label.
Default: -1
Index of a class label (may be numeric), counting whitespace separated entries in a line starting with 0 - the corresponding entry will be treated as a label.
Default: -1
Index of a class label (may be numeric), counting whitespace separated entries in a line starting with 0 - the corresponding entry will be treated as a label.
Default: -1
Distance function.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Known implementations:
Index of a class label (may be numeric), counting whitespace separated entries in a line starting with 0 - the corresponding entry will be treated as a label.
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
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
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]
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.
the strategy for determination of the preference vector, available strategies are: [APRIORI| MAX_INTERSECTION](default is MAX_INTERSECTION)
Default: MAX_INTERSECTION
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]
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.
the strategy for determination of the preference vector, available strategies are: [APRIORI| MAX_INTERSECTION](default is MAX_INTERSECTION)
Default: MAX_INTERSECTION
The maximum radius of the neighborhood to be considered.
Default: the maximum radius of the neighborhood to be considered, must be suitable to de.lmu.ifi.dbs.elki.distance.distancefunction.LocallyWeightedDistanceFunction
Flag to mark delta as an absolute value.
The threshold for strong Eigenvalues. If not otherwise specified, delta is a relative value w.r.t. the (absolute) highest Eigenvalues and has to be a double between 0 and 1. To mark delta as an absolute value, use the option -pca.filter.absolute.
Default: 0.01
Distance function to determine the 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.
a double between 0 and 1 specifying the maximum absolute variance along a coordinate axis.
Default: 0.01
a positive integer specifying 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.
a positive integer specifying 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 object for running PCA.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
The number of nearest neighbors considered in the PCA. If this parameter is not set, k ist set to three times of the dimensionality of the database objects.
The distance function used to select object for running PCA.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
The number of nearest neighbors considered in the PCA. If this parameter is not set, k ist set to three times of the dimensionality of the database objects.
The distance function used to select object for running PCA.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
The number of nearest neighbors considered in the PCA. If this parameter is not set, k ist set to three times of the dimensionality of the database objects.
The distance function used to select object for running PCA.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
The number of nearest neighbors considered in the PCA. If this parameter is not set, k ist set to three times of the dimensionality of the database objects.
The distance function used to select object for running PCA.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
The number of nearest neighbors considered in the PCA. If this parameter is not set, k ist set to three times of the dimensionality of the database objects.
The distance function used to select object for running PCA.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
The number of nearest neighbors considered in the PCA. If this parameter is not set, k ist set to three times of the dimensionality of the database objects.
the distance function to materialize the nearest neighbors
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
The number of nearest neighbors of an object to be materialized.
The maximum radius of the neighborhood to be considered.
Default: the maximum radius of the neighborhood to be considered, must be suitable to de.lmu.ifi.dbs.elki.distance.distancefunction.LocallyWeightedDistanceFunction
a double between 0 and 1 specifying the threshold for small Eigenvalues (default is delta = 0.01).
Default: 0.01
Distance function to determine the distance between database objects.
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
Threshold for minimum number of points in the epsilon-neighborhood of a point.
Flag to omit (a new) preprocessing if for each object the association has already been set.
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
Flag to omit (a new) preprocessing if for each object the association has already been set.
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
Flag to omit (a new) preprocessing if for each object the association has already been set.
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.
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.KnnQueryBasedHiCOPreprocessor
Known implementations:
Flag to omit (a new) preprocessing if for each object the association has already been set.
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.
Preprocessor class to determine the correlation dimension of each object.
Class Restriction: extends de.lmu.ifi.dbs.elki.preprocessing.HiCOPreprocessor
Default: de.lmu.ifi.dbs.elki.preprocessing.KnnQueryBasedHiCOPreprocessor
Known implementations:
Flag to omit (a new) preprocessing if for each object the association has already been set.
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.KnnQueryBasedHiCOPreprocessor
Known implementations:
Flag to omit (a new) preprocessing if for each object the association has already been set.
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.KnnQueryBasedHiCOPreprocessor
Known implementations:
Flag to omit (a new) preprocessing if for each object the association has already been set.
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.KnnQueryBasedHiCOPreprocessor
Known implementations:
Flag to omit (a new) preprocessing if for each object the association has already been set.
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
Flag to omit (a new) preprocessing if for each object the association has already been set.
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
Flag to omit (a new) preprocessing if for each object the association has already been set.
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
Flag to omit (a new) preprocessing if for each object the association has already been set.
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
Flag to omit (a new) preprocessing if for each object the association has already been set.
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
The distance function used to select object 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:
An epsilon value suitable to the specified distance function.
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:
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 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 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 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 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 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 distance function to materialize the nearest neighbors
Class Restriction: implements de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction
Default: de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction
Known implementations:
The number of nearest neighbors of an object to be materialized.
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.
Silently overwrite output files.