Uses of Interface
de.lmu.ifi.dbs.elki.utilities.optionhandling.Parameterizable

Packages that use Parameterizable

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	Package collects variants of index structures.
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.utilities.optionhandling	Package collects classes required for handling and description of options for any parameterizable class.
de.lmu.ifi.dbs.elki.varianceanalysis	Classes for analysis of variance by different methods.

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

Classes in de.lmu.ifi.dbs.elki that implement Parameterizable

class

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

Fields in de.lmu.ifi.dbs.elki with type parameters of type Parameterizable

private ClassParameter<Parameterizable>

KDDTask.DESCRIPTION_PARAM Optional Parameter to specify a class to obtain a description for, must extend Parameterizable.

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

Subinterfaces of Parameterizable in de.lmu.ifi.dbs.elki.algorithm

interface

Algorithm<O extends DatabaseObject> Specifies the requirements for any algorithm that is to be executable by the main class.

Classes in de.lmu.ifi.dbs.elki.algorithm that implement Parameterizable

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 Parameterizable in de.lmu.ifi.dbs.elki.algorithm.clustering

Subinterfaces of Parameterizable in de.lmu.ifi.dbs.elki.algorithm.clustering

interface

Clustering<O extends DatabaseObject> Interface for Algorithms that are capable to provide a ClusteringResult.

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering that implement Parameterizable

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 Parameterizable in de.lmu.ifi.dbs.elki.algorithm.clustering.biclustering

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.biclustering that implement Parameterizable

class

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

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

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation that implement Parameterizable

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 Parameterizable in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace that implement Parameterizable

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 Parameterizable in de.lmu.ifi.dbs.elki.database

Subinterfaces of Parameterizable in de.lmu.ifi.dbs.elki.database

interface

Database<O extends DatabaseObject> Database specifies the requirements for any database implementation.

Classes in de.lmu.ifi.dbs.elki.database that implement Parameterizable

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 Parameterizable in de.lmu.ifi.dbs.elki.database.connection

Subinterfaces of Parameterizable in de.lmu.ifi.dbs.elki.database.connection

interface

DatabaseConnection<O extends DatabaseObject> DatabaseConnection is to provide a database.

Classes in de.lmu.ifi.dbs.elki.database.connection that implement Parameterizable

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 Parameterizable in de.lmu.ifi.dbs.elki.distance

Subinterfaces of Parameterizable in de.lmu.ifi.dbs.elki.distance

interface

MeasurementFunction<O extends DatabaseObject,D extends Distance<D>> Interface Measurement describes the requirements of any measurement function (e.g. distance function or similarity function), that provides a measurement for comparing database objects.

Classes in de.lmu.ifi.dbs.elki.distance that implement Parameterizable

class

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

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

Subinterfaces of Parameterizable in de.lmu.ifi.dbs.elki.distance.distancefunction

interface

DistanceFunction<O extends DatabaseObject,D extends Distance<D>> Interface DistanceFunction describes the requirements of any distance function.

Classes in de.lmu.ifi.dbs.elki.distance.distancefunction that implement Parameterizable

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 Parameterizable in de.lmu.ifi.dbs.elki.distance.similarityfunction

Subinterfaces of Parameterizable in de.lmu.ifi.dbs.elki.distance.similarityfunction

interface

SimilarityFunction<O extends DatabaseObject,D extends Distance<D>> Interface SimilarityFunction describes the requirements of any similarity function.

Classes in de.lmu.ifi.dbs.elki.distance.similarityfunction that implement Parameterizable

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 Parameterizable in de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel

Subinterfaces of Parameterizable in de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel

interface

KernelFunction<O extends DatabaseObject,D extends Distance<D>> Interface Kernel describes the requirements of any kernel function.

Classes in de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel that implement Parameterizable

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 Parameterizable in de.lmu.ifi.dbs.elki.index

Subinterfaces of Parameterizable in de.lmu.ifi.dbs.elki.index

interface

Index<O extends DatabaseObject> Interface defining the minimum requirements for all index classes.

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

Classes in de.lmu.ifi.dbs.elki.index.tree that implement Parameterizable

class

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

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

Classes in de.lmu.ifi.dbs.elki.index.tree.metrical that implement Parameterizable

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 Parameterizable in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants

Classes in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants that implement Parameterizable

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 Parameterizable in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mkapp

Classes in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mkapp that implement Parameterizable

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 Parameterizable in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mkcop

Classes in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mkcop that implement Parameterizable

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 Parameterizable in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mkmax

Classes in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mkmax that implement Parameterizable

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 Parameterizable in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktab

Classes in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktab that implement Parameterizable

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 Parameterizable in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mtree

Classes in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mtree that implement Parameterizable

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 Parameterizable in de.lmu.ifi.dbs.elki.index.tree.spatial

Subinterfaces of Parameterizable in de.lmu.ifi.dbs.elki.index.tree.spatial

interface

SpatialDistanceFunction<O extends FeatureVector<O,?>,D extends Distance<D>> Defines the requirements for a distance function that can used in spatial index to measure the dissimilarity between spatial data objects.

Classes in de.lmu.ifi.dbs.elki.index.tree.spatial that implement Parameterizable

class

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

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

Classes in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants that implement Parameterizable

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 Parameterizable in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.deliclu

Classes in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.deliclu that implement Parameterizable

class

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

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

Classes in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rdknn that implement Parameterizable

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 Parameterizable in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rstar

Classes in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rstar that implement Parameterizable

class

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

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

Subinterfaces of Parameterizable in de.lmu.ifi.dbs.elki.normalization

interface

Normalization<O extends DatabaseObject> Normalization performs a normalization on a set of feature vectors and is capable to transform a set of feature vectors to the original attribute ranges.

Classes in de.lmu.ifi.dbs.elki.normalization that implement Parameterizable

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 Parameterizable in de.lmu.ifi.dbs.elki.parser

Subinterfaces of Parameterizable in de.lmu.ifi.dbs.elki.parser

interface	DistanceParser<O extends DatabaseObject,D extends Distance<D>> A DistanceParser shall provide a DistanceParsingResult by parsing an InputStream.
interface	Parser<O extends DatabaseObject> A Parser shall provide a ParsingResult by parsing an InputStream.

Classes in de.lmu.ifi.dbs.elki.parser that implement Parameterizable

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 Parameterizable in de.lmu.ifi.dbs.elki.preprocessing

Subinterfaces of Parameterizable in de.lmu.ifi.dbs.elki.preprocessing

interface	PreferenceVectorPreprocessor<O extends DatabaseObject> Marker interface for preprocessors computing preference vectors.
interface	Preprocessor<O extends DatabaseObject> Defines the requirements for classes that do some preprocessing steps for objects of a certain database.

Classes in de.lmu.ifi.dbs.elki.preprocessing that implement Parameterizable

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 Parameterizable in de.lmu.ifi.dbs.elki.utilities.optionhandling

Classes in de.lmu.ifi.dbs.elki.utilities.optionhandling that implement Parameterizable

class

AbstractParameterizable Abstract superclass for classes parameterizable.

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

Subinterfaces of Parameterizable in de.lmu.ifi.dbs.elki.varianceanalysis

interface	EigenPairFilter The eigenpair filter is used to filter eigenpairs (i.e. eigenvectors and their corresponding eigenvalues) which are a result of a Variance Analysis Algorithm, e.g.
interface	PCA A PCA is a principal component analysis that belongs to an object stored in a database.

Classes in de.lmu.ifi.dbs.elki.varianceanalysis that implement Parameterizable

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.