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

Packages that use Parameterizable

de.lmu.ifi.dbs.elki	ELKI framework "Environment for Developing KDD-Applications Supported by Index-Structures" KDDTask is the main class of the ELKI-Framework for command-line interaction.
de.lmu.ifi.dbs.elki.algorithm	Algorithms suitable as a task for the KDDTask main routine.
de.lmu.ifi.dbs.elki.algorithm.clustering	Clustering algorithms Clustering algorithms are supposed to implement the Algorithm-Interface.
de.lmu.ifi.dbs.elki.algorithm.clustering.correlation	Correlation clustering algorithms
de.lmu.ifi.dbs.elki.algorithm.clustering.subspace	Axis-parallel subspace clustering algorithms The clustering algorithms in this package are instances of both, projected clustering algorithms or subspace clustering algorithms according to the classical but somewhat obsolete classification schema of clustering algorithms for axis-parallel subspaces.
de.lmu.ifi.dbs.elki.algorithm.outlier	Outlier detection algorithms
de.lmu.ifi.dbs.elki.algorithm.statistics	Statistical analysis algorithms The algorithms in this package perform statistical analysis of the data (e.g. compute distributions, distance distributions etc.)
de.lmu.ifi.dbs.elki.application	Base classes for stand alone applications.
de.lmu.ifi.dbs.elki.application.cache	Utility applications for the persistence layer such as distance cache builders.
de.lmu.ifi.dbs.elki.application.visualization	Visualization applications in ELKI.
de.lmu.ifi.dbs.elki.database	ELKI database layer - loading, storing, indexing and accessing data
de.lmu.ifi.dbs.elki.database.connection	Database connections are classes implementing data sources.
de.lmu.ifi.dbs.elki.distance	Distances and (in subpackages) distance functions and similarity functions.
de.lmu.ifi.dbs.elki.distance.distancefunction	Distance functions for use within ELKI.
de.lmu.ifi.dbs.elki.distance.distancefunction.adapter	Distance functions deriving distances from e.g. similarity measures
de.lmu.ifi.dbs.elki.distance.distancefunction.correlation	Distance functions using correlations.
de.lmu.ifi.dbs.elki.distance.distancefunction.external	Distance functions using external data sources.
de.lmu.ifi.dbs.elki.distance.distancefunction.subspace	Distance functions based on subspaces.
de.lmu.ifi.dbs.elki.distance.distancefunction.timeseries	Distance functions designed for time series.
de.lmu.ifi.dbs.elki.distance.similarityfunction	Similarity functions.
de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel	Kernel functions.
de.lmu.ifi.dbs.elki.evaluation	Functionality for the evaluation of algorithms.
de.lmu.ifi.dbs.elki.index	Index structure implementations
de.lmu.ifi.dbs.elki.index.tree	Tree-based index structures
de.lmu.ifi.dbs.elki.index.tree.metrical	Tree-based index structures for metrical vector spaces.
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants	M-Tree and variants.
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees	Metrical index structures based on the concepts of the M-Tree supporting processing of reverse k nearest neighbor queries by using the k-nn distances of the entries.
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkapp	MkAppTree
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkcop	MkCoPTree
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkmax	MkMaxTree
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mktab	MkTabTree
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mtree	MTree
de.lmu.ifi.dbs.elki.index.tree.spatial	Tree-based index structures for spatial indexing.
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants	R*-Tree and variants.
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.deliclu	DeLiCluTree
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rdknn	RdKNNTree
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rstar	RStarTree
de.lmu.ifi.dbs.elki.math.linearalgebra.pca	Principal Component Analysis (PCA) and Eigenvector processing.
de.lmu.ifi.dbs.elki.normalization	Data normalization (and reconstitution) of data sets.
de.lmu.ifi.dbs.elki.parser	Parsers for different file formats and data types.
de.lmu.ifi.dbs.elki.parser.meta	MetaParsers for different file formats and data types.
de.lmu.ifi.dbs.elki.preprocessing	Preprocessors used for data preparation in a first step of various algorithms or distance and similarity measures.
de.lmu.ifi.dbs.elki.result	Result types, representation and handling
de.lmu.ifi.dbs.elki.utilities.optionhandling	Parameter handling and option descriptions.
de.lmu.ifi.dbs.elki.visualization	Visualization package of ELKI.

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.

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,R extends Result> 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,R extends Result> 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 quantitatively linear dependencies among attributes of a given dataset based on a linear correlation PCA.
class	DistanceBasedAlgorithm<O extends DatabaseObject,D extends Distance<D>,R extends Result> Provides an abstract algorithm already setting the distance function.
class	DummyAlgorithm<V extends NumberVector<V,?>> Dummy Algorithm, which just iterates over all points once, doing a 10NN query each.
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.
class	MaterializeDistances<V extends RealVector<V,?>,D extends NumberDistance<D,N>,N extends Number> Algorithm to materialize all the distances in a data set.
class	NullAlgorithm<V extends NumberVector<V,?>> Null Algorithm, which does nothing.

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

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

interface

ClusteringAlgorithm<C extends Clustering<? extends Model>,O extends DatabaseObject> Interface for Algorithms that are capable to provide a Clustering as Result.

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

class	ByLabelClustering<O extends DatabaseObject> Pseudo clustering using labels.
class	ByLabelHierarchicalClustering<O extends DatabaseObject> Pseudo clustering using labels.
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 hierarchical 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.
class	TrivialAllInOne<O extends DatabaseObject> Trivial pseudo-clustering that just considers all points to be one big cluster.
class	TrivialAllNoise<O extends DatabaseObject> Trivial pseudo-clustering that just considers all points to be noise.

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 Provides the CASH algorithm, an subspace clustering algorithm based on the hough transform.
class	COPAC<V extends RealVector<V,?>> Provides the COPAC algorithm, an 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, an algorithm to find clusters in high dimensional spaces.

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,?>> Provides the PROCLUS algorithm, an algorithm to find subspace clusters in high dimensional spaces.
class	ProjectedClustering<V extends RealVector<V,?>> Abstract superclass for projected clustering algorithms, like PROCLUS and ORCLUS.

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

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

class	ABOD<V extends RealVector<V,?>> Angle-Based Outlier Detection Outlier detection using variance analysis on angles, especially for high dimensional data sets.
class	LOCI<O extends DatabaseObject,D extends NumberDistance<D,?>> Fast Outlier Detection Using the "Local Correlation Integral".
class	LOF<O extends DatabaseObject,D extends NumberDistance<D,?>> Algorithm to compute density-based local outlier factors in a database based on a specified parameter LOF.K_ID (-lof.k).
class	SOD<V extends RealVector<V,Double>,D extends Distance<D>>

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

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

class	DistanceStatisticsWithClasses<V extends RealVector<V,?>,D extends NumberDistance<D,?>> Algorithm to gather statistics over the distance distribution in the data set.
class	EvaluateRankingQuality<V extends RealVector<V,?>,D extends NumberDistance<D,?>> Evaluate a distance function with respect to kNN queries.
class	RankingQualityHistogram<V extends RealVector<V,?>,D extends NumberDistance<D,?>> Evaluate a distance function with respect to kNN queries.

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

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

class	AbstractApplication AbstractApplication sets the values for flags verbose and help.
class	GeneratorXMLSpec Generate a data set based on a specified model (using an XML specification)
class	StandAloneApplication StandAloneApplication sets additionally to the flags set by AbstractApplication the output parameter out.
class	StandAloneInputApplication StandAloneInputApplication extends StandAloneApplication and sets additionally the parameter in.

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

private ClassParameter<Parameterizable>

AbstractApplication.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.application.cache

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

class	CacheDoubleDistanceInOnDiskMatrix<O extends DatabaseObject,N extends NumberDistance<N,D>,D extends Number> Wrapper to convert a traditional text-serialized result into a on-disk matrix for random access.
class	CacheFloatDistanceInOnDiskMatrix<O extends DatabaseObject,N extends NumberDistance<N,D>,D extends Number> Wrapper to convert a traditional text-serialized result into a on-disk matrix for random access.

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

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

class

KNNExplorer<O extends NumberVector<O,?>,N extends NumberDistance<N,D>,D extends Number> User application to explore the k Nearest Neighbors for a given data set and distance function.

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	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 an input stream such as stdin.
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.
interface	PreprocessorBasedMeasurementFunction<O extends DatabaseObject,P extends Preprocessor<O>,D extends Distance<D>> Describes the requirements of any measurement function (e.g. distance function or similarity function) needing a preprocessor running on a database.

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

class

AbstractMeasurementFunction<O extends DatabaseObject,D extends Distance<D>> Abstract implementation of interface MeasurementFunction that 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	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	ArcCosineDistanceFunction<V extends FeatureVector<V,?>> Cosine distance function for feature vectors.
class	CosineDistanceFunction<V extends FeatureVector<V,?>> Cosine distance function for feature vectors.
class	EuclideanDistanceFunction<V extends NumberVector<V,?>> Provides the Euclidean distance for NumberVectors.
class	KernelBasedLocallyWeightedDistanceFunction<V extends RealVector<V,?>,P extends Preprocessor<V>> Provides a kernel based locally weighted distance function.
class	LocallyWeightedDistanceFunction<V extends RealVector<V,?>,P extends Preprocessor<V>> Provides a locally weighted distance function.
class	LPNormDistanceFunction<V extends FeatureVector<V,N>,N extends Number> Provides a LP-Norm for FeatureVectors.
class	ManhattanDistanceFunction<V extends NumberVector<V,?>> Manhattan distance function to compute the Manhattan distance for a pair of NumberVectors.
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	WeightedDistanceFunction<V extends NumberVector<V,?>> Provides the Weighted distance for feature vectors.

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

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

class	SimilarityAdapterAbstract<V extends FeatureVector<V,?>> Adapter from a normalized similarity function to a distance function.
class	SimilarityAdapterArccos<V extends FeatureVector<V,?>> Adapter from a normalized similarity function to a distance function using arccos(sim).
class	SimilarityAdapterLinear<V extends FeatureVector<V,?>> Adapter from a normalized similarity function to a distance function using 1 - sim.
class	SimilarityAdapterLn<V extends FeatureVector<V,?>> Adapter from a normalized similarity function to a distance function using -log(sim).

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

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

class	AbstractCorrelationDistanceFunction<V extends RealVector<V,?>,P extends Preprocessor<V>,D extends CorrelationDistance<D>> Abstract super class for correlation based distance functions.
class	AbstractPreferenceVectorBasedCorrelationDistanceFunction<V extends RealVector<V,?>,P extends PreferenceVectorPreprocessor<V>> Abstract super class for all preference vector based correlation distance functions.
class	ERiCDistanceFunction<V extends RealVector<V,?>,P extends Preprocessor<V>> Provides a distance function for building the hierarchiy in the ERiC algorithm.
class	PCABasedCorrelationDistanceFunction<V extends RealVector<V,?>,P extends HiCOPreprocessor<V>,D extends CorrelationDistance<D>> Provides the correlation distance for real valued vectors.

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

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

class	DiskCacheBasedDoubleDistanceFunction<V extends DatabaseObject> Provides a DistanceFunction that is based on double distances given by a distance matrix of an external file.
class	DiskCacheBasedFloatDistanceFunction<V extends DatabaseObject> Provides a DistanceFunction that is based on float distances given by a distance matrix of an external file.
class	FileBasedDoubleDistanceFunction<V extends DatabaseObject> Provides a DistanceFunction that is based on double distances given by a distance matrix of an external file.
class	FileBasedFloatDistanceFunction<V extends DatabaseObject> Provides a DistanceFunction that is based on float distances given by a distance matrix of an external file.

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

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

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	DimensionSelectingDistanceFunction<N extends Number,V extends FeatureVector<V,N>> Provides a distance function that computes the distance between feature vectors as the absolute difference of their values in a specified dimension.
class	DimensionsSelectingEuclideanDistanceFunction<V extends NumberVector<V,?>> Provides a distance function that computes the Euclidean distance between feature vectors only in specified dimensions.
class	DiSHDistanceFunction<V extends RealVector<V,?>,P extends PreferenceVectorPreprocessor<V>> Distance function used in the DiSH algorithm.
class	HiSCDistanceFunction<V extends RealVector<V,?>,P extends PreferenceVectorPreprocessor<V>> Distance function used in the HiSC algorithm.
class	SubspaceDistanceFunction<V extends RealVector<V,?>,P extends Preprocessor<V>> 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.

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

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

class	AbstractEditDistanceFunction<V extends NumberVector<V,?>> Provides the Edit Distance for NumberVectors.
class	DTWDistanceFunction<V extends NumberVector<V,?>> Provides the Dynamic Time Warping distance for NumberVectors.
class	EDRDistanceFunction<V extends NumberVector<V,?>> Provides the Edit Distance on Real Sequence distance for NumberVectors.
class	ERPDistanceFunction<V extends NumberVector<V,?>> Provides the Edit Distance With Real Penalty distance for NumberVectors.
class	LCSSDistanceFunction<V extends NumberVector<V,?>> Provides the Longest Common Subsequence distance for NumberVectors.

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

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

interface	NormalizedSimilarityFunction<O extends DatabaseObject,D extends Distance<D>> Marker interface to signal that the similarity function is normalized to produce values in the range of [0:1].
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	FractionalSharedNearestNeighborSimilarityFunction<O extends DatabaseObject,D extends Distance<D>>
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 defined 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 defined by (V1^T*V2)^degree.

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

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

class

ComputeROCCurve<O extends DatabaseObject> Compute a ROC curve to evaluate a ranking algorithm and compute the corresponding ROCAUC value.

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.mktrees

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

class

AbstractMkTree<O extends DatabaseObject,D extends Distance<D>,N extends AbstractMTreeNode<O,D,N,E>,E extends MTreeEntry<D>> Abstract class for all M-Tree variants supporting processing of reverse k-nearest neighbor queries by using the k-nn distances of the entries, where k is less than or equal to the specified parameter AbstractMkTree.K_MAX_PARAM.

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

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

class

MkAppTree<O extends DatabaseObject,D extends NumberDistance<D,N>,N extends Number> 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.mktrees.mkcop

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

class

MkCoPTree<O extends DatabaseObject,D extends NumberDistance<D,N>,N extends Number> 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.mktrees.mkmax

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

class

MkMaxTree<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 <= k_max.

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

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

class

MkTabTree<O extends DatabaseObject,D extends Distance<D>> MkTabTree 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<V extends FeatureVector<V,?>,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,N>,N extends Number> 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.math.linearalgebra.pca

Subinterfaces of Parameterizable in de.lmu.ifi.dbs.elki.math.linearalgebra.pca

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.

Classes in de.lmu.ifi.dbs.elki.math.linearalgebra.pca that implement Parameterizable

class	CompositeEigenPairFilter The CompositeEigenPairFilter can be used to build a chain of eigenpair filters.
class	CovarianceMatrixBuilder<V extends RealVector<V,?>,D extends NumberDistance<D,?>> Abstract class with the task of computing a Covariance matrix to be used in PCA.
class	FirstNEigenPairFilter The FirstNEigenPairFilter marks the n highest eigenpairs as strong eigenpairs, where n is a user specified number.
class	KernelCovarianceMatrixBuilder<V extends RealVector<V,?>,D extends NumberDistance<D,?>> Kernel Covariance Matrix Builder.
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	NormalizingEigenPairFilter The NormalizingEigenPairFilter normalizes all eigenvectors s.t.
class	PCAFilteredRunner<V extends RealVector<V,?>,D extends NumberDistance<D,?>> PCA runner that will do dimensionality reduction.
class	PCARunner<V extends RealVector<V,?>,D extends NumberDistance<D,?>> Class to run PCA on given data.
class	PercentageEigenPairFilter The PercentageEigenPairFilter sorts the eigenpairs in descending 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.
class	ProgressiveEigenPairFilter The ProgressiveEigenPairFilter sorts the eigenpairs in descending 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.
class	RelativeEigenPairFilter The RelativeEigenPairFilter sorts the eigenpairs in descending order of their eigenvalues and marks the first eigenpairs who are a certain factor above the average of the remaining eigenvalues.
class	SignificantEigenPairFilter The SignificantEigenPairFilter sorts the eigenpairs in descending order of their eigenvalues and chooses the contrast of an Eigenvalue to the remaining Eigenvalues is maximal.
class	StandardCovarianceMatrixBuilder<V extends RealVector<V,?>,D extends NumberDistance<D,?>> Class for building a "traditional" covariance matrix.
class	WeakEigenPairFilter The WeakEigenPairFilter sorts the eigenpairs in descending order of their eigenvalues and returns the first eigenpairs who are above the average mark as "strong", the others as "weak".
class	WeightedCovarianceMatrixBuilder<V extends RealVector<V,?>,D extends NumberDistance<D,?>> CovarianceMatrixBuilder with weights.

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	AttributeWiseMinMaxNormalization<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	AttributeWiseVarianceNormalization<V extends RealVector<V,?>> Class to perform and undo a normalization on real vectors with respect to given mean and standard deviation 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	LinebasedParser<O extends DatabaseObject> A parser that can parse single line.
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	DoubleVectorLabelParser Provides a parser for parsing one point per line, attributes separated by whitespace.
class	DoubleVectorLabelTransposingParser Parser reads points transposed.
class	FloatVectorLabelParser Provides a parser for parsing one point per line, attributes separated by whitespace.
class	NumberDistanceParser<D extends NumberDistance<D,N>,N extends Number> 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<?,?>> Provides a parser for parsing one point per line, attributes separated by whitespace.
class	SparseBitVectorLabelParser Provides a parser for parsing one sparse BitVector per line, where the indices of the one-bits are separated by whitespace.
class	SparseFloatVectorLabelParser Provides a parser for parsing one point per line, attributes separated by whitespace.

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

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

class	DoubleVectorProjectionParser Parser to project the ParsingResult obtained by a suitable base parser onto a selected subset of attributes.
class	DoubleVectorRandomProjectionParser Parser to project the ParsingResult obtained by a suitable base parser onto a randomly selected subset of attributes.
class	MetaParser<O extends DatabaseObject> A MetaParser uses any Parser as specified by the user via parameter setting as base parser and may perform certain transformations on the retrieved ParsingResult.
class	ProjectionParser<V extends RealVector<V,?>> A ProjectionParser projects the ParsingResult of its base parser onto a subspace specified by a BitSet.
class	RandomProjectionParser<V extends RealVector<V,?>> A RandomProjectionParser selects a subset of attributes randomly for projection of a ParsingResult.
class	SparseFloatVectorProjectionParser Parser to project the ParsingResult obtained by a suitable base parser onto a selected subset of attributes.
class	SparseFloatVectorRandomProjectionParser Parser to project the ParsingResult obtained by a suitable base parser onto a randomly selected subset of attributes.

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.
interface	PreprocessorClient<P extends Preprocessor<O>,O extends DatabaseObject> Interface defining the requirements for classes using a PreprocessorHandler to run a preprocessor on 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	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	KnnQueryBasedHiCOPreprocessor<V extends RealVector<V,?>> Computes the HiCO correlation dimension of objects of a certain database.
class	MaterializeKNNPreprocessor<O extends DatabaseObject,D extends Distance<D>> A preprocessor for annotation of the k nearest neighbors (and their distances) to each database object.
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	PreprocessorHandler<O extends DatabaseObject,P extends Preprocessor<O>> Handler class for all objects (e.g. distance functions) using a preprocessor running on a certain database.
class	ProjectedDBSCANPreprocessor<D extends Distance<D>,V extends RealVector<V,?>> Abstract superclass for preprocessor of algorithms extending the ProjectedDBSCAN algorithm.
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.
class	SpatialApproximationMaterializeKNNPreprocessor<O extends NumberVector<O,?>,D extends Distance<D>,N extends SpatialNode<N,E>,E extends SpatialEntry> A preprocessor for annotation of the k nearest neighbors (and their distances) to each database object.

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

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

interface

ResultHandler<O extends DatabaseObject,R extends Result> Interface for any class that can handle results

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

class	DiscardResultHandler<O extends DatabaseObject,R extends Result> A dummy result handler that discards the actual result, for use in benchmarks.
class	ResultWriter<O extends DatabaseObject> Result handler that feeds the data into a TextWriter

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.

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

private List<Pair<Parameterizable,List<OptionID>>>

AbstractParameterizable.parameterizables Hold parameterizables contained

Methods in de.lmu.ifi.dbs.elki.utilities.optionhandling with parameters of type Parameterizable

protected void	AbstractParameterizable.addParameterizable(Parameterizable p) Add a new parameterizable to the list.
protected void	AbstractParameterizable.addParameterizable(Parameterizable p, List<OptionID> override) Add a new parameterizable to the list.
static String	OptionUtil.describeParameterizable(Parameterizable p) Produce a description of a Parameterizable instance (including recursive options).
static StringBuffer	OptionUtil.describeParameterizable(StringBuffer buf, Parameterizable p, int width, String indent) Format a description of a Parameterizable (including recursive options).
protected void	AbstractParameterizable.removeParameterizable(Parameterizable p) Remove a parameterizable from the list.

Method parameters in de.lmu.ifi.dbs.elki.utilities.optionhandling with type arguments of type Parameterizable

void	Parameterizable.collectOptions(List<Pair<Parameterizable,Option<?>>> collection) Fill the given collection with a list of available options.
void	AbstractParameterizable.collectOptions(List<Pair<Parameterizable,Option<?>>> collection) Get all possible options.
static void	OptionUtil.formatForConsole(StringBuffer buf, int width, String indent, List<Pair<Parameterizable,Option<?>>> options) Format a list of options (and associated owning objects) for console help output.

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

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

class

ResultROCCurveVisualizer<O extends DatabaseObject> Visualize a ROC curve.