Uses of Class
de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizable

Packages that use AbstractParameterizable

de.lmu.ifi.dbs.elki	The base-package of the ELKI framework.
de.lmu.ifi.dbs.elki.algorithm	Package to collect algorithms suitable as a task for the KDDTask main routine.
de.lmu.ifi.dbs.elki.algorithm.clustering	Package collects clustering algorithms.
de.lmu.ifi.dbs.elki.algorithm.clustering.biclustering	Package to collect biclustering algorithms suitable as a task for the KDDTask main routine.
de.lmu.ifi.dbs.elki.algorithm.clustering.correlation	Package to collect correlation clustering algorithms suitable as a task for the KDDTask main routine.
de.lmu.ifi.dbs.elki.algorithm.clustering.subspace	Package to collect algorithms for clustering in axis-parallel subspaces, suitable as a task for the KDDTask main routine.
de.lmu.ifi.dbs.elki.database	Package collects variants of databases and related classes.
de.lmu.ifi.dbs.elki.database.connection	Provides database connection classes.
de.lmu.ifi.dbs.elki.distance	Package collects distances and - in its subpackages - distance and similarity functions.
de.lmu.ifi.dbs.elki.distance.distancefunction	Package collects distance functions.
de.lmu.ifi.dbs.elki.distance.similarityfunction	Package collects similarity functions.
de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel	Package collects kernel functions.
de.lmu.ifi.dbs.elki.index.tree	Package collects variants of tree-based index structures.
de.lmu.ifi.dbs.elki.index.tree.metrical	Package collects metrical tree-based index structures.
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants	Package collects variants of the M-Tree.
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mkapp	Package collects classes for the MkAppTree
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mkcop	Package collects classes for the MkCoPTree
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mkmax	Package collects classes for the MkMaxTree
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktab	Package collects classes for the MkTabTree
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mtree	Package collects classes for the MTree
de.lmu.ifi.dbs.elki.index.tree.spatial	Package collects spatial tree-based index structures.
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants	Package collects variants of the R*-Tree.
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.deliclu	Package collects classes for the DeLiCluTree
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rdknn	Package collects classes for the RdKNNTree
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rstar	Package collects classes for the RStarTree
de.lmu.ifi.dbs.elki.normalization	Provides classes and methods for normalizations (and reconstitution) of data sets.
de.lmu.ifi.dbs.elki.parser	Package collects parser for different file formats and data types.
de.lmu.ifi.dbs.elki.preprocessing	Package collects preprocessors used for data preparation in a first step of various algorithms or distance measures.
de.lmu.ifi.dbs.elki.varianceanalysis	Classes for analysis of variance by different methods.

Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki

Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki

class

KDDTask<O extends DatabaseObject> Provides a KDDTask that can be used to perform any algorithm implementing Algorithm using any DatabaseConnection implementing DatabaseConnection.

Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.algorithm

Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.algorithm

class	AbstractAlgorithm<O extends DatabaseObject> AbstractAlgorithm sets the values for flags verbose and time.
class	APRIORI Provides the APRIORI algorithm for Mining Association Rules.
class	DependencyDerivator<V extends RealVector<V,?>,D extends Distance<D>> Dependency derivator computes quantitativly linear dependencies among attributes of a given dataset based on a linear correlation PCA.
class	DistanceBasedAlgorithm<O extends DatabaseObject,D extends Distance<D>> Provides an abstract algorithm already setting the distance function.
class	KNNDistanceOrder<O extends DatabaseObject,D extends Distance<D>> Provides an order of the kNN-distances for all objects within the database.
class	KNNJoin<V extends NumberVector<V,?>,D extends Distance<D>,N extends SpatialNode<N,E>,E extends SpatialEntry> Joins in a given spatial database to each object its k-nearest neighbors.

Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.algorithm.clustering

Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.algorithm.clustering

class	DBSCAN<O extends DatabaseObject,D extends Distance<D>> DBSCAN provides the DBSCAN algorithm, an algorithm to find density-connected sets in a database.
class	DeLiClu<O extends NumberVector<O,?>,D extends Distance<D>> DeLiClu provides the DeLiClu algorithm, a hierachical algorithm to find density-connected sets in a database.
class	EM<V extends RealVector<V,?>> Provides the EM algorithm (clustering by expectation maximization).
class	KMeans<D extends Distance<D>,V extends RealVector<V,?>> Provides the k-means algorithm.
class	OPTICS<O extends DatabaseObject,D extends Distance<D>> OPTICS provides the OPTICS algorithm.
class	ProjectedDBSCAN<V extends RealVector<V,?>> Provides an abstract algorithm requiring a VarianceAnalysisPreprocessor.
class	SLINK<O extends DatabaseObject,D extends Distance<D>> Efficient implementation of the Single-Link Algorithm SLINK of R.
class	SNNClustering<O extends DatabaseObject,D extends Distance<D>> Shared nearest neighbor clustering.

Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.algorithm.clustering.biclustering

Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.algorithm.clustering.biclustering

class

AbstractBiclustering<V extends RealVector<V,Double>> Abstract class as a convenience for different biclustering approaches.

Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation

Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation

class	CASH Subspace clustering algorithm based on the hough transform.
class	COPAA<V extends RealVector<V,?>> Algorithm to partition a database according to the correlation dimension of its objects and to then perform an arbitrary algorithm over the partitions.
class	COPAC<V extends RealVector<V,?>> Algorithm to partition a database according to the correlation dimension of its objects and to then perform an arbitrary clustering algorithm over the partitions.
class	ERiC<V extends RealVector<V,?>> Performs correlation clustering on the data partitioned according to local correlation dimensionality and builds a hierarchy of correlation clusters that allows multiple inheritance from the clustering result.
class	FourC<O extends RealVector<O,?>> 4C identifies local subgroups of data objects sharing a uniform correlation.
class	ORCLUS<V extends RealVector<V,?>> ORCLUS provides the ORCLUS algorithm.

Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace

Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace

class	CLIQUE<V extends RealVector<V,?>> Implementation of the CLIQUE algorithm, a grid-based algorithm to identify dense clusters in subspaces of maximum dimensionality.
class	DiSH<V extends RealVector<V,?>> Algorithm for detecting subspace hierarchies.
class	PreDeCon<V extends RealVector<V,?>> PreDeCon computes clusters of subspace preference weighted connected points.
class	PROCLUS<V extends RealVector<V,?>> PROCLUS provides the PROCLUS algorithm.
class	ProjectedClustering<V extends RealVector<V,?>> Abstract superclass for PROCLUS and ORCLUS.

Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.database

Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.database

class	AbstractDatabase<O extends DatabaseObject> Provides a mapping for associations based on a Hashtable and functions to get the next usable ID for insertion, making IDs reusable after deletion of the entry.
class	IndexDatabase<O extends DatabaseObject> IndexDatabase is a database implementation which is supported by an index structure.
class	InvertedListDatabase<N extends Number,O extends FeatureVector<O,N>> Database implemented by inverted lists that supports range queries on a specific dimension.
class	MetricalIndexDatabase<O extends DatabaseObject,D extends Distance<D>,N extends MetricalNode<N,E>,E extends MTreeEntry<D>> MetricalIndexDatabase is a database implementation which is supported by a metrical index structure.
class	SequentialDatabase<O extends DatabaseObject> SequentialDatabase is a simple implementation of a Database.
class	SpatialIndexDatabase<O extends NumberVector<O,?>,N extends SpatialNode<N,E>,E extends SpatialEntry> SpatialIndexDatabase is a database implementation which is supported by a spatial index structure.

Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.database.connection

Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.database.connection

class	AbstractDatabaseConnection<O extends DatabaseObject> Abstract super class for all database connections.
class	FileBasedDatabaseConnection<O extends DatabaseObject> Provides a file based database connection based on the parser to be set.
class	InputStreamDatabaseConnection<O extends DatabaseObject> Provides a database connection expecting input from standard in.
class	MultipleFileBasedDatabaseConnection<O extends DatabaseObject> Provides a database connection based on multiple files and parsers to be set.

Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.distance

Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.distance

class

AbstractMeasurementFunction<O extends DatabaseObject,D extends Distance<D>> Abstract Measurement Function provides some methods valid for any extending class.

Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.distance.distancefunction

Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.distance.distancefunction

class	AbstractCorrelationDistanceFunction<O extends RealVector<O,?>,P extends Preprocessor<O>,D extends CorrelationDistance<D>> Abstract super class for correlation based distance functions.
class	AbstractDimensionsSelectingDoubleDistanceFunction<V extends NumberVector<V,?>> Provides a distance function that computes the distance (which is a double distance) between feature vectors only in specified dimensions.
class	AbstractDistanceFunction<O extends DatabaseObject,D extends Distance<D>> AbstractDistanceFunction provides some methods valid for any extending class.
class	AbstractDoubleDistanceFunction<O extends DatabaseObject> Provides an abstract superclass for DistanceFunctions that are based on DoubleDistance.
class	AbstractFloatDistanceFunction<O extends DatabaseObject> Provides a DistanceFunction that is based on FloatDistance.
class	AbstractLocallyWeightedDistanceFunction<O extends RealVector<O,?>,P extends Preprocessor<O>> Abstract super class for locally weighted distance functions using a preprocessor to compute the local weight matrix.
class	AbstractPreprocessorBasedDistanceFunction<O extends DatabaseObject,P extends Preprocessor<O>,D extends Distance<D>> Abstract super class for distance functions needing a preprocessor.
class	CosineDistanceFunction<V extends FeatureVector<V,?>> CosineDistanceFunction for FeatureVectors.
class	DimensionSelectingDistanceFunction<N extends Number,O extends FeatureVector<O,N>> Provides a distance function that computes the distance between feature vectors as the absolute difference of their values in a specified dimension.
class	DimensionsSelectingEuklideanDistanceFunction<V extends NumberVector<V,?>> Provides a distance function that computes the Euklidean distance between feature vectors only in specified dimensions.
class	DirectSupportDependentItemsetDistanceFunction Provides a DistanceFunction to compute a Distance between BitVectors based on the number of shared bits.
class	DiSHDistanceFunction<V extends RealVector<V,?>,P extends Preprocessor<V>> Distance function used in the DiSH algorithm.
class	ERiCDistanceFunction<V extends RealVector<V,?>,P extends Preprocessor<V>> Provides a distance function for building the hierarchiy in the ERiC algorithm.
class	EuklideanDistanceFunction<T extends NumberVector<T,?>> Provides the Euklidean distance for FeatureVectors.
class	FileBasedDoubleDistanceFunction Provides a DistanceFunction that is based on double distances given by a distance matrix of an external file.
class	FileBasedFloatDistanceFunction Provides a DistanceFunction that is based on float distances given by a distance matrix of an external file.
class	FractalDimensionBasedDistanceFunction<V extends RealVector<V,?>>
class	FrequencyDependentItemsetDistanceFunction Provides a DistanceFunction to compute a Distance between BitVectors based on the number of shared bits.
class	HiSCDistanceFunction<O extends RealVector<O,?>,P extends Preprocessor<O>> Distance function used in the HiSC algorithm.
class	KernelBasedLocallyWeightedDistanceFunction<O extends RealVector<O,?>,P extends Preprocessor<O>> Provides a kernel based locally weighted distance function.
class	LocallyWeightedDistanceFunction<O extends RealVector<O,?>,P extends Preprocessor<O>> Provides a locally weighted distance function.
class	LPNormDistanceFunction<V extends FeatureVector<V,N>,N extends Number> Provides a LP-Norm for FeatureVectors.
class	ManhattanDistanceFunction<T extends NumberVector<T,?>> Manhattan distance function to compute the Manhattan distance for a pair of NumberVectors.
class	PCABasedCorrelationDistanceFunction<O extends RealVector<O,?>,P extends Preprocessor<O>,D extends CorrelationDistance<D>> Provides the Correlation distance for real valued vectors.
class	PreferenceVectorBasedCorrelationDistanceFunction<O extends RealVector<O,?>,P extends Preprocessor<O>> XXX unify CorrelationDistanceFunction and VarianceDistanceFunction
class	ReciprocalSupportDependentItemsetDistanceFunction Provides a DistanceFunction to compute a Distance between BitVectors based on the number of shared bits.
class	RepresentationSelectingDistanceFunction<O extends DatabaseObject,M extends MultiRepresentedObject<O>,D extends Distance<D>> Distance function for multirepresented objects that selects one representation and computes the distances only within the selected representation.
class	SharedMaximumDistanceFunction Provides a DistanceFunction to compute a Distance between BitVectors based on the number of shared bits.
class	SharedUnitedDistanceFunction Provides a DistanceFunction to compute a Distance between BitVectors based on the number of shared bits.
class	SharingDependentItemsetDistanceFunction Provides a DistanceFunction to compute a Distance between BitVectors based on the number of shared bits.
class	SquareRootSupportLengthDependentItemsetDistanceFunction Provides a DistanceFunction to compute a Distance between BitVectors based on the number of shared bits.
class	SubspaceDistanceFunction<O extends RealVector<O,?>,P extends Preprocessor<O>,D extends SubspaceDistance<D>> Provides a distance function to determine a kind of correlation distance between two points, which is a pair consisting of the distance between the two subspaces spanned by the strong eigenvectors of the two points and the affine distance between the two subspaces.
class	SupportLengthDependentItemsetDistanceFunction Provides a DistanceFunction to compute a Distance between BitVectors based on the number of shared bits.
class	WeightedDistanceFunction<O extends NumberVector<O,?>> Provides the Weighted distance for feature vectors.

Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.distance.similarityfunction

Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.distance.similarityfunction

class	AbstractIntegerSimilarityFunction<O extends DatabaseObject>
class	AbstractPreprocessorBasedSimilarityFunction<O extends DatabaseObject,P extends Preprocessor<O>,D extends Distance<D>> Abstract super class for distance functions needing a preprocessor.
class	AbstractSimilarityFunction<O extends DatabaseObject,D extends Distance<D>>
class	ClusterSimilarity
class	SharedNearestNeighborSimilarityFunction<O extends DatabaseObject,D extends Distance<D>>

Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel

Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel

class	AbstractDoubleKernelFunction<O extends DatabaseObject> Provides an abstract superclass for KernelFunctions that are based on DoubleDistance.
class	AbstractKernelFunction<O extends DatabaseObject,D extends Distance<D>> AbstractKernelFunction provides some methods valid for any extending class.
class	ArbitraryKernelFunctionWrapper<O extends RealVector<O,?>> Provides a wrapper for arbitrary kernel functions whose kernel matrix has been precomputed.
class	FooKernelFunction<O extends FeatureVector> Provides an experimental KernelDistanceFunction for RealVectors.
class	KernelMatrix<O extends RealVector<O,?>> Provides a class for storing the kernel matrix and several extraction methods for convenience.
class	LinearKernelFunction<O extends FeatureVector<O,?>> Provides a linear Kernel function that computes a similarity between the two feature vectors V1 and V2 definded by V1^T*V2.
class	PolynomialKernelFunction<O extends FeatureVector<O,?>> Provides a polynomial Kernel function that computes a similarity between the two feature vectors V1 and V2 definded by (V1^T*V2)^degree.

Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.index.tree

Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.index.tree

class

TreeIndex<O extends DatabaseObject,N extends Node<N,E>,E extends Entry> Abstract super class for all tree based index classes.

Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.index.tree.metrical

Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.index.tree.metrical

class

MetricalIndex<O extends DatabaseObject,D extends Distance<D>,N extends MetricalNode<N,E>,E extends MetricalEntry> Abstract super class for all metrical index classes.

Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants

Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants

class

AbstractMTree<O extends DatabaseObject,D extends Distance<D>,N extends AbstractMTreeNode<O,D,N,E>,E extends MTreeEntry<D>> Abstract super class for all M-Tree variants.

Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mkapp

Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mkapp

class

MkAppTree<O extends DatabaseObject,D extends NumberDistance<D>> MkAppTree is a metrical index structure based on the concepts of the M-Tree supporting efficient processing of reverse k nearest neighbor queries for parameter k < kmax.

Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mkcop

Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mkcop

class

MkCoPTree<O extends DatabaseObject,D extends NumberDistance<D>> MkCopTree is a metrical index structure based on the concepts of the M-Tree supporting efficient processing of reverse k nearest neighbor queries for parameter k < kmax.

Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mkmax

Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mkmax

class

MkMaxTree<O extends DatabaseObject,D extends Distance<D>> MkNNTree is a metrical index structure based on the concepts of the M-Tree supporting efficient processing of reverse k nearest neighbor queries.

Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktab

Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktab

class

MkTabTree<O extends DatabaseObject,D extends Distance<D>> MkMaxTree is a metrical index structure based on the concepts of the M-Tree supporting efficient processing of reverse k nearest neighbor queries for parameter k < kmax.

Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mtree

Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mtree

class

MTree<O extends DatabaseObject,D extends Distance<D>> MTree is a metrical index structure based on the concepts of the M-Tree.

Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.index.tree.spatial

Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.index.tree.spatial

class

SpatialIndex<O extends NumberVector<O,?>,N extends SpatialNode<N,E>,E extends SpatialEntry> Abstract super class for all spatial index classes.

Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants

Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants

class	AbstractRStarTree<O extends NumberVector<O,?>,N extends AbstractRStarTreeNode<N,E>,E extends SpatialEntry> Abstract superclass for index structures based on a R*-Tree.
class	NonFlatRStarTree<O extends NumberVector<O,?>,N extends AbstractRStarTreeNode<N,E>,E extends SpatialEntry> Abstract superclass for all non-flat R*-Tree variants.

Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.deliclu

Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.deliclu

class

DeLiCluTree<O extends NumberVector<O,?>> DeLiCluTree is a spatial index structure based on an R-TRee.

Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rdknn

Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rdknn

class

RdKNNTree<O extends NumberVector<O,?>,D extends NumberDistance<D>> RDkNNTree is a spatial index structure based on the concepts of the R*-Tree supporting efficient processing of reverse k nearest neighbor queries.

Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rstar

Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rstar

class

RStarTree<O extends NumberVector<O,?>> RStarTree is a spatial index structure based on the concepts of the R*-Tree.

Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.normalization

Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.normalization

class	AbstractNormalization<O extends DatabaseObject> Abstract super class for all normalizations.
class	AttributeWiseRealVectorNormalization<V extends RealVector<V,?>> Class to perform and undo a normalization on real vectors with respect to given minimum and maximum in each dimension.
class	DummyNormalization<O extends DatabaseObject> Dummy normalization that does nothing.
class	MultiRepresentedObjectNormalization<O extends DatabaseObject> Class to perform and undo a normalization on multi-represented objects with respect to given normalizations for each representation.

Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.parser

Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.parser

class	AbstractParser<O extends DatabaseObject> Abstract superclass for all parsers providing the option handler for handling options.
class	BitVectorLabelParser Provides a parser for parsing one BitVector per line, bits separated by whitespace.
class	NumberDistanceParser<D extends NumberDistance<D>> Provides a parser for parsing one distance value per line.
class	ParameterizationFunctionLabelParser Provides a parser for parsing one point per line, attributes separated by whitespace.
class	RealVectorLabelParser<V extends RealVector<V,?>> Provides a parser for parsing one point per line, attributes separated by whitespace.
class	RealVectorLabelTransposingParser Parser reads points transposed.
class	SparseBitVectorLabelParser Provides a parser for parsing one sparse BitVector per line, where the indices of the one-bits are separated by whitespace.

Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.preprocessing

Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.preprocessing

class	DiSHPreprocessor<V extends RealVector<V,N>,N extends Number> Preprocessor for DiSH preference vector assignment to objects of a certain database.
class	FourCPreprocessor<D extends Distance<D>,V extends RealVector<V,?>> Preprocessor for 4C local dimensionality and locally weighted matrix assignment to objects of a certain database.
class	FracClusPreprocessor<V extends RealVector<V,?>>
class	HiCOPreprocessor<V extends RealVector<V,?>> Abstract superclass for preprocessors for HiCO correlation dimension assignment to objects of a certain database.
class	HiSCPreprocessor<V extends RealVector<V,?>> Preprocessor for HiSC preference vector assignment to objects of a certain database.
class	KernelFourCPreprocessor<D extends Distance<D>,V extends RealVector<V,?>> Preprocessor for kernel 4C local dimensionality, neighbor objects and strong eigenvector matrix assignment to objects of a certain database.
class	KnnQueryBasedHiCOPreprocessor<V extends RealVector<V,?>> Computes the HiCO correlation dimension of objects of a certain database.
class	PreDeConPreprocessor<D extends Distance<D>,V extends RealVector<V,?>> Preprocessor for PreDeCon local dimensionality and locally weighted matrix assignment to objects of a certain database.
class	ProjectedDBSCANPreprocessor<D extends Distance<D>,V extends RealVector<V,?>> Abstract superclass for preprocessor of algorithms extending the ProjectedDBSCAN alghorithm.
class	RangeQueryBasedHiCOPreprocessor<V extends RealVector<V,?>> Computes the HiCO correlation dimension of objects of a certain database.
class	SharedNearestNeighborsPreprocessor<O extends DatabaseObject,D extends Distance<D>> A preprocessor for annotation of the ids of nearest neighbors to each database object.

Uses of AbstractParameterizable in de.lmu.ifi.dbs.elki.varianceanalysis

Subclasses of AbstractParameterizable in de.lmu.ifi.dbs.elki.varianceanalysis

class	AbstractPCA Abstract super class for pca algorithms.
class	CompositeEigenPairFilter The CompositeEigenPairFilter can be used to build a chain of eigenpair filters.
class	FirstNEigenPairFilter The FirstNEigenPairFilter marks the n highest eigenpairs as strong eigenpairs, where n is a user specified number.
class	GlobalPCA<O extends RealVector<O,?>> Computes the principal components for vector objects of a given database.
class	LimitEigenPairFilter The LimitEigenPairFilter marks all eigenpairs having an (absolute) eigenvalue below the specified threshold (relative or absolute) as weak eigenpairs, the others are marked as strong eigenpairs.
class	LinearLocalPCA<V extends RealVector<V,?>> Performs a linear local PCA based on the covariance matrices of given objects.
class	LocalKernelPCA<V extends RealVector<V,?>> Performs a local kernel PCA based on the kernel matrices of given objects.
class	LocalPCA<V extends RealVector<V,?>> LocalPCA is a super calss for PCA-algorithms considering only a local neighborhood.
class	NormalizingEigenPairFilter The NormalizingEigenPairFilter normalizes all eigenvectors s.t.
class	PercentageEigenPairFilter The PercentageEigenPairFilter sorts the eigenpairs in decending order of their eigenvalues and marks the first eigenpairs, whose sum of eigenvalues is higher than the given percentage of the sum of all eigenvalues as strong eigenpairs.