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.data.images	Package for processing image data (e.g. compute color histograms)
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.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.colorhistogram	Distance functions using correlations.
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.histogram	Functionality for the evaluation of algorithms using histograms.
de.lmu.ifi.dbs.elki.evaluation.roc	Evaluation of rankings using ROC AUC (Receiver Operation Characteristics - Area Under Curve)
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.referencepoints	Package containing strategies to obtain reference points Shared code for various algorithms that use reference points.
de.lmu.ifi.dbs.elki.utilities.scaling	Scaling functions: linear, logarithmic, gamma, clipping, ...
de.lmu.ifi.dbs.elki.utilities.scaling.outlier	Scaling of Outlier scores, that require a statistical analysis of the occurring values
de.lmu.ifi.dbs.elki.visualization.gui	Package to provide a visualization GUI.
de.lmu.ifi.dbs.elki.visualization.visualizers	Visualizers for various results
de.lmu.ifi.dbs.elki.visualization.visualizers.adapter	Adapters to map results to visualizers.
de.lmu.ifi.dbs.elki.visualization.visualizers.vis1d	Visualizers based on 1D projections.
de.lmu.ifi.dbs.elki.visualization.visualizers.vis2d	Visualizers based on 2D projections.
de.lmu.ifi.dbs.elki.visualization.visualizers.visunproj	Visualizers that do not use a particular projection.

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 NumberVector<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 DatabaseObject,D extends NumberDistance<D,N>,N extends Number> Algorithm to materialize all the distances in a data set.
class	MetaMultiAlgorithm<O extends DatabaseObject> Meta algorithm that will run multiple algorithms and join the result.
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 NumberVector<V,?>> Provides the EM algorithm (clustering by expectation maximization).
class	KMeans<D extends Distance<D>,V extends NumberVector<V,?>> Provides the k-means algorithm.
class	OPTICS<O extends DatabaseObject,D extends Distance<D>> OPTICS provides the OPTICS algorithm.
class	ProjectedDBSCAN<V extends NumberVector<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 NumberVector<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 NumberVector<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 NumberVector<O,?>> 4C identifies local subgroups of data objects sharing a uniform correlation.
class	HiCO<V extends NumberVector<V,?>> Implementation of the HiCO algorithm, an algorithm for detecting hierarchies of correlation clusters.
class	ORCLUS<V extends NumberVector<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 NumberVector<V,?>> Implementation of the CLIQUE algorithm, a grid-based algorithm to identify dense clusters in subspaces of maximum dimensionality.
class	DiSH<V extends NumberVector<V,?>> Algorithm for detecting subspace hierarchies.
class	HiSC<V extends NumberVector<V,?>> Implementation of the HiSC algorithm, an algorithm for detecting hierarchies of subspace clusters.
class	PreDeCon<V extends NumberVector<V,?>> PreDeCon computes clusters of subspace preference weighted connected points.
class	PROCLUS<V extends NumberVector<V,?>> Provides the PROCLUS algorithm, an algorithm to find subspace clusters in high dimensional spaces.
class	ProjectedClustering<V extends NumberVector<V,?>> Abstract superclass for projected clustering algorithms, like PROCLUS and ORCLUS.
class	SUBCLU<V extends NumberVector<V,?>,D extends Distance<D>> Implementation of the SUBCLU algorithm, an algorithm to detect arbitrarily shaped and positioned clusters in subspaces.

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 NumberVector<V,?>> Angle-Based Outlier Detection Outlier detection using variance analysis on angles, especially for high dimensional data sets.
class	AbstractDBOutlier<O extends DatabaseObject,D extends Distance<D>> Simple distance based outlier detection algorithms.
class	DBOutlierDetection<O extends DatabaseObject,D extends Distance<D>> Simple distanced based outlier detection algorithm.
class	DBOutlierScore<O extends DatabaseObject,D extends Distance<D>> Compute percentage of neighbors in the given neighborhood with size d.
class	EMOutlier<V extends NumberVector<V,?>> outlier detection algorithm using EM Clustering.
class	GaussianModel<V extends NumberVector<V,Double>> Outlier have smallest GMOD_PROB: the outlier scores is the probability density of the assumed distribution.
class	GaussianUniformMixture<V extends NumberVector<V,Double>> Outlier detection algorithm using a mixture model approach.
class	INFLO<O extends DatabaseObject> INFLO provides the Mining Algorithms (Two-way Search Method) for Influence Outliers using Symmetric Relationship Reference: Jin, W., Tung, A., Han, J., and Wang, W. 2006 Ranking outliers using symmetric neighborhood relationship< br/> In Proc.
class	KNNOutlier<O extends DatabaseObject,D extends DoubleDistance> Outlier Detection based on the distance of an object to its k nearest neighbor.
class	KNNWeightOutlier<O extends DatabaseObject,D extends DoubleDistance> Outlier Detection based on the accumulated distances of a point to its k nearest neighbors.
class	LDOF<O extends DatabaseObject> Computes the LDOF (Local Distance-Based Outlier Factor) for all objects of a Database.
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	LoOP<O extends DatabaseObject> LoOP: Local Outlier Probabilities Distance/density based algorithm similar to LOF to detect outliers, but with statistical methods to achieve better result stability.
class	OPTICSOF<O extends DatabaseObject> OPTICSOF provides the Optics-of algorithm, an algorithm to find Local Outliers in a database.
class	ReferenceBasedOutlierDetection<V extends NumberVector<V,N>,N extends Number> provides the Reference-Based Outlier Detection algorithm, an algorithm that computes kNN distances approximately, using reference points.
class	SOD<V extends NumberVector<V,?>,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 DatabaseObject,D extends NumberDistance<D,?>> Algorithm to gather statistics over the distance distribution in the data set.
class	EvaluateRankingQuality<V extends NumberVector<V,?>,D extends NumberDistance<D,?>> Evaluate a distance function with respect to kNN queries.
class	RankingQualityHistogram<V extends DatabaseObject,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	ComputeSingleColorHistogram Application that computes the color histogram vector for a single image.
class	GeneratorXMLSpec Generate a data set based on a specified model (using an XML specification)
class	KDDCLIApplication<O extends DatabaseObject> Provides a KDDCLIApplication that can be used to perform any algorithm implementing Algorithm using any DatabaseConnection implementing DatabaseConnection.
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.

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

Subinterfaces of Parameterizable in de.lmu.ifi.dbs.elki.data.images

interface

ComputeColorHistogram Interface for color histogram implementations.

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

class	AbstractComputeColorHistogram Abstract class for color histogram computation.
class	ComputeHSBColorHistogram Compute color histograms in a Hue-Saturation-Brightness model.
class	ComputeNaiveHSBColorHistogram Compute color histograms in a Hue-Saturation-Brightness model.
class	ComputeNaiveRGBColorHistogram Compute a (rather naive) RGB color histogram.

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	EmptyDatabaseConnection<O extends DatabaseObject> Pseudo database that is empty.
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.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.
interface	LocalPCAPreprocessorBasedDistanceFunction<O extends NumberVector<O,?>,P extends LocalPCAPreprocessor<O>,D extends Distance<D>> Interface for local PCA based preprocessors.
interface	PreprocessorBasedDistanceFunction<O extends DatabaseObject,P extends Preprocessor<O>,D extends Distance<D>> Interface to mark preprocessor based distance functions.

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	AbstractLocallyWeightedDistanceFunction<O extends NumberVector<O,?>,P extends LocalPCAPreprocessor<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 NumberVector<V,?>> Cosine distance function for feature vectors.
class	CosineDistanceFunction<V extends NumberVector<V,?>> Cosine distance function for feature vectors.
class	EuclideanDistanceFunction<V extends NumberVector<V,?>> Provides the Euclidean distance for FeatureVectors.
class	KernelBasedLocallyWeightedDistanceFunction<V extends NumberVector<V,?>,P extends LocalPCAPreprocessor<V>> Provides a kernel based locally weighted distance function.
class	LocallyWeightedDistanceFunction<V extends NumberVector<V,?>,P extends LocalPCAPreprocessor<V>> Provides a locally weighted distance function.
class	LPNormDistanceFunction<V extends NumberVector<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 FeatureVectors.
class	MaximumDistanceFunction<V extends NumberVector<V,?>> Maximum distance function to compute the Maximum distance for a pair of FeatureVectors.
class	MinimumDistanceFunction<V extends NumberVector<V,?>> Maximum distance function to compute the Minimum distance for a pair of FeatureVectors.
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 NumberVector<V,?>> Adapter from a normalized similarity function to a distance function.
class	SimilarityAdapterArccos<V extends NumberVector<V,?>> Adapter from a normalized similarity function to a distance function using arccos(sim).
class	SimilarityAdapterLinear<V extends NumberVector<V,?>> Adapter from a normalized similarity function to a distance function using 1 - sim.
class	SimilarityAdapterLn<V extends NumberVector<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.colorhistogram

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

class	HistogramIntersectionDistanceFunction<V extends NumberVector<V,?>> Intersection distance for color histograms.
class	HSBHistogramQuadraticDistanceFunction<V extends NumberVector<V,?>> Distance function for HSB color histograms based on a quadratic form and color similarity.
class	RGBHistogramQuadraticDistanceFunction<V extends NumberVector<V,?>> Distance function for RGB color histograms based on a quadratic form and color similarity.

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 FeatureVector<V,?>,P extends Preprocessor<V>,D extends CorrelationDistance<D>> Abstract super class for correlation based distance functions.
class	ERiCDistanceFunction<V extends NumberVector<V,?>,P extends LocalPCAPreprocessor<V>> Provides a distance function for building the hierarchy in the ERiC algorithm.
class	PCABasedCorrelationDistanceFunction<V extends NumberVector<V,?>,P extends LocalPCAPreprocessor<V>> Provides the correlation distance for real valued vectors.
class	PearsonCorrelationDistanceFunction<V extends NumberVector<V,N>,N extends Number> Pearson correlation distance function for feature vectors.
class	SquaredPearsonCorrelationDistanceFunction<V extends NumberVector<V,N>,N extends Number> Squared Pearson correlation distance function for feature 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 FeatureVector<V,?>> Provides a distance function that computes the distance (which is a double distance) between feature vectors only in specified dimensions.
class	AbstractPreferenceVectorBasedCorrelationDistanceFunction<V extends NumberVector<V,?>,P extends PreferenceVectorPreprocessor<V>> Abstract super class for all preference vector based correlation distance functions.
class	DimensionSelectingDistanceFunction<V extends NumberVector<V,?>> 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 NumberVector<V,?>,P extends PreferenceVectorPreprocessor<V>> Distance function used in the DiSH algorithm.
class	HiSCDistanceFunction<V extends NumberVector<V,?>,P extends PreferenceVectorPreprocessor<V>> Distance function used in the HiSC algorithm.
class	SubspaceDistanceFunction<V extends NumberVector<V,?>,P extends LocalPCAPreprocessor<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 FeatureVectors.
class	DTWDistanceFunction<V extends NumberVector<V,?>> Provides the Dynamic Time Warping distance for FeatureVectors.
class	EDRDistanceFunction<V extends NumberVector<V,?>> Provides the Edit Distance on Real Sequence distance for FeatureVectors.
class	ERPDistanceFunction<V extends NumberVector<V,?>> Provides the Edit Distance With Real Penalty distance for FeatureVectors.
class	LCSSDistanceFunction<V extends NumberVector<V,?>> Provides the Longest Common Subsequence distance for FeatureVectors.

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	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>> SharedNearestNeighborSimilarityFunction with a pattern defined to accept Strings that define a non-negative Integer.
class	SharedNearestNeighborSimilarityFunction<O extends DatabaseObject,D extends Distance<D>> SharedNearestNeighborSimilarityFunction with a pattern defined to accept Strings that define a non-negative Integer.

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	AbstractKernelFunction<O extends DatabaseObject,D extends Distance<D>> AbstractKernelFunction provides some methods valid for any extending class.
class	ArbitraryKernelFunctionWrapper<O extends FeatureVector<O,?>> Provides a wrapper for arbitrary kernel functions whose kernel matrix has been precomputed.
class	FooKernelFunction<O extends NumberVector<?,?>> Provides an experimental KernelDistanceFunction for NumberVectors.
class	LinearKernelFunction<O extends NumberVector<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 NumberVector<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.histogram

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

class

ComputeOutlierHistogram<O extends DatabaseObject> Compute a Histogram to evaluate a ranking algorithm.

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

Classes in de.lmu.ifi.dbs.elki.evaluation.roc 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 NumberVector<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 NumberVector<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 NumberVector<V,?>,D extends NumberDistance<D,?>> PCA runner that will do dimensionality reduction.
class	PCARunner<V extends NumberVector<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 NumberVector<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 NumberVector<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 NumberVector<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 NumberVector<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	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	NumberVectorLabelParser<V extends NumberVector<?,?>> Provides a parser for parsing one point per line, attributes separated by whitespace.
class	ParameterizationFunctionLabelParser 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 NumberVector<V,?>> A ProjectionParser projects the ParsingResult of its base parser onto a subspace specified by a BitSet.
class	RandomProjectionParser<V extends NumberVector<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

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

class	DiSHPreprocessor<V extends NumberVector<V,?>> Preprocessor for DiSH preference vector assignment to objects of a certain database.
class	FourCPreprocessor<D extends Distance<D>,V extends NumberVector<V,?>> Preprocessor for 4C local dimensionality and locally weighted matrix assignment to objects of a certain database.
class	HiSCPreprocessor<V extends NumberVector<V,?>> Preprocessor for HiSC preference vector assignment to objects of a certain database.
class	KnnQueryBasedLocalPCAPreprocessor<V extends NumberVector<V,?>> Provides the local neighborhood to be considered in the PCA as the k nearest neighbors of an object.
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 NumberVector<V,?>> Preprocessor for PreDeCon local dimensionality and locally weighted matrix assignment to objects of a certain database.
class	RangeQueryBasedLocalPCAPreprocessor<V extends NumberVector<V,?>> Provides the local neighborhood to be considered in the PCA as the neighbors within an epsilon range query of an object.
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.referencepoints

Subinterfaces of Parameterizable in de.lmu.ifi.dbs.elki.utilities.referencepoints

interface

ReferencePointsHeuristic<O extends NumberVector<O,?>> Simple Interface for an heuristic to pick reference points.

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

class	AxisBasedReferencePoints<O extends NumberVector<O,?>> Strategy to pick reference points by placing them on the axis ends.
class	FullDatabaseReferencePoints<O extends NumberVector<O,?>> Strategy to use the complete database as reference points.
class	GridBasedReferencePoints<O extends NumberVector<O,?>> Grid-based strategy to pick reference points.
class	RandomGeneratedReferencePoints<O extends NumberVector<O,?>> Reference points generated randomly within the used data space.
class	RandomSampleReferencePoints<O extends NumberVector<O,?>> Random-Sampling strategy for picking reference points.
class	StarBasedReferencePoints<O extends NumberVector<O,?>> Star-based strategy to pick reference points.

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

Subinterfaces of Parameterizable in de.lmu.ifi.dbs.elki.utilities.scaling

interface	ScalingFunction Interface for scaling functions used by Outlier evaluation such as Histograms and visualization.
interface	StaticScalingFunction Interface for Scaling functions that do NOT depend on analyzing the data set.

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

class	ClipScaling Scale implementing a simple clipping.
class	GammaScaling Non-linear scaling function using a Gamma curve.
class	IdentityScaling The trivial "identity" scaling function.
class	LinearScaling Simple linear scaling function.
class	MinusLogScaling Scaling function to invert values by computing -1 * Math.log(x)

Uses of Parameterizable in de.lmu.ifi.dbs.elki.utilities.scaling.outlier

Subinterfaces of Parameterizable in de.lmu.ifi.dbs.elki.utilities.scaling.outlier

interface

OutlierScalingFunction Interface for scaling functions used by Outlier evaluation such as Histograms and visualization.

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

class	MinusLogGammaScaling Scaling that can map arbitrary values to a probability in the range of [0:1], by assuming a Gamma distribution on the data and evaluating the Gamma CDF.
class	MinusLogStandardDeviationScaling Scaling that can map arbitrary values to a probability in the range of [0:1].
class	MultiplicativeInverseScaling Scaling function to invert values basically by computing 1/x, but in a variation that maps the values to the [0:1] interval and avoiding division by 0.
class	OutlierGammaScaling Scaling that can map arbitrary values to a probability in the range of [0:1] by assuming a Gamma distribution on the values.
class	OutlierLinearScaling Scaling that can map arbitrary values to a probability in the range of [0:1].
class	OutlierMinusLogScaling Scaling function to invert values by computing -1 * Math.log(x)
class	OutlierSqrtScaling Scaling that can map arbitrary positive values to a value in the range of [0:1].
class	SqrtStandardDeviationScaling Scaling that can map arbitrary values to a probability in the range of [0:1].
class	StandardDeviationScaling Scaling that can map arbitrary values to a probability in the range of [0:1].
class	TopKOutlierScaling Outlier scaling function that only keeps the top k outliers.

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

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

class

ResultVisualizer Handler to process and visualize a Result.

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

Subinterfaces of Parameterizable in de.lmu.ifi.dbs.elki.visualization.visualizers

interface	ProjectedVisualizer A projected visualizer needs a projection for visualization.
interface	UnprojectedVisualizer An unprojected Visualizer can run stand-alone.
interface	Visualizer Defines the requirements for a visualizer.

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

class	AbstractVisualizer Abstract superclass for Visualizers.
class	VisualizersForResult Utility class to determine the visualizers for a result class.

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

Subinterfaces of Parameterizable in de.lmu.ifi.dbs.elki.visualization.visualizers.adapter

interface

AlgorithmAdapter Defines the requirements for an algorithm-adapter.

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

class	ClusteringAdapter<NV extends NumberVector<NV,?>> Class to add generic clustering visualizations.
class	ClusterOrderAdapter Visualize a cluster order by connecting the points with arrows
class	CurveAdapter Adapter that will look for visualizable 2D curves and create visualizations for them.
class	DefaultAdapter<NV extends NumberVector<NV,?>> Class to add various default visualizations.
class	HistogramAdapter Adapter to visualize general Histogram results found in the result.
class	OutlierScoreAdapter<NV extends NumberVector<NV,?>> This class activates bubble and tooltip visualizers when there is an Outlier result found.
class	ReferencePointsAdapter<NV extends NumberVector<NV,?>> Adapter to generate a reference points visualizer when reference points were found in the data.
class	RStarTreeAdapter Adapter that will look for an AbstractRStarTree to visualize

Uses of Parameterizable in de.lmu.ifi.dbs.elki.visualization.visualizers.vis1d

Classes in de.lmu.ifi.dbs.elki.visualization.visualizers.vis1d that implement Parameterizable

class	Projection1DHistogramVisualizer<NV extends NumberVector<NV,?>> Generates a SVG-Element containing a histogram representing the distribution of the database's objects.
class	Projection1DVisualizer<NV extends NumberVector<NV,?>> Produces visualizations of 1-dimensional projections.

Uses of Parameterizable in de.lmu.ifi.dbs.elki.visualization.visualizers.vis2d

Classes in de.lmu.ifi.dbs.elki.visualization.visualizers.vis2d that implement Parameterizable

class	AxisVisualizer<NV extends NumberVector<NV,?>> Generates a SVG-Element containing axes, including labeling.
class	BubbleVisualizer<NV extends NumberVector<NV,?>> Generates a SVG-Element containing bubbles.
class	ClusteringVisualizer<NV extends NumberVector<NV,?>> Visualize a clustering using different markers for different clusters.
class	ClusterOrderVisualizer<NV extends NumberVector<NV,?>> Visualize an OPTICS cluster order by drawing connection lines.
class	DataDotVisualizer<NV extends NumberVector<NV,?>> Generates a SVG-Element containing "dots" as markers representing the Database's objects.
class	Projection2DVisualizer<NV extends NumberVector<NV,?>> Produces visualizations of 2-dimensional projections.
class	ReferencePointsVisualizer<NV extends NumberVector<NV,?>> Generates a SVG-Element visualizing reference points.
class	TooltipVisualizer<NV extends NumberVector<NV,?>> Generates a SVG-Element containing Tooltips.
class	TreeMBRVisualizer<NV extends NumberVector<NV,?>,N extends AbstractRStarTreeNode<N,E>,E extends SpatialEntry> Visualize the bounding rectangles of an rtree based index.

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

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

class	CurveVisualizer Visualizer to render a simple 2D curve such as a ROC curve.
class	HistogramVisualizer Visualizer to draw histograms.
class	KeyVisualizer Pseudo-Visualizer, that gives the key for a clustering.
class	OPTICSPlotVisualizer<D extends Distance<?>> Visualize an OPTICS result by constructing an OPTICS plot for it.
class	SettingsVisualizer Pseudo-Visualizer, that lists the settings of the algorithm-