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

Packages that use Parameterizable

Package	Description
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.clustering.trivial	Trivial clustering algorithms: all in one, no clusters, label clusterings These methods are mostly useful for providing a reference result in evaluation.
de.lmu.ifi.dbs.elki.algorithm.outlier	Outlier detection algorithms
de.lmu.ifi.dbs.elki.algorithm.outlier.meta	Meta outlier detection algorithms: external scores, score rescaling.
de.lmu.ifi.dbs.elki.algorithm.outlier.spatial	Spatial outlier detection algorithms
de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood	Spatial outlier neighborhood classes
de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood.weighted	Weighted Neighborhood definitions.
de.lmu.ifi.dbs.elki.algorithm.outlier.trivial	Trivial outlier detection algorithms: no outliers, all outliers, label outliers.
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.jsmap	JavaScript based map client - server architecture.
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.datasource	Data normalization (and reconstitution) of data sets.
de.lmu.ifi.dbs.elki.datasource.filter	Data filtering, in particular for normalization and projection.
de.lmu.ifi.dbs.elki.datasource.parser	Parsers for different file formats and data types.
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.geo	Geographic (earth) distance functions.
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.index	Index structure implementations
de.lmu.ifi.dbs.elki.index.preprocessed	Index structure based on preprocessors
de.lmu.ifi.dbs.elki.index.preprocessed.knn	Indexes providing KNN and rKNN data.
de.lmu.ifi.dbs.elki.index.preprocessed.localpca	Index using a preprocessed local PCA.
de.lmu.ifi.dbs.elki.index.preprocessed.preference	Indexes storing preference vectors.
de.lmu.ifi.dbs.elki.index.preprocessed.snn	Indexes providing nearest neighbor sets
de.lmu.ifi.dbs.elki.index.preprocessed.subspaceproj	Index using a preprocessed local subspaces.
de.lmu.ifi.dbs.elki.index.tree	Tree-based index structures
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.rstarvariants	R*-Tree and variants.
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.bulk	Packages for bulk-loading R*-Trees.
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.deliclu	DeLiCluTree
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rstar	RStarTree
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.util	Utilities for R*-Tree and variants.
de.lmu.ifi.dbs.elki.math.linearalgebra.pca	Principal Component Analysis (PCA) and Eigenvector processing.
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	Visualization package of ELKI.
de.lmu.ifi.dbs.elki.visualization.gui	Package to provide a visualization GUI.
de.lmu.ifi.dbs.elki.visualization.projector	Projectors are responsible for finding appropriate projections for data relations.
de.lmu.ifi.dbs.elki.visualization.visualizers	Visualizers for various results
de.lmu.ifi.dbs.elki.visualization.visualizers.optics	Visualizers that do work on OPTICS plots
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.
de.lmu.ifi.dbs.elki.workflow	Work flow packages, e.g. following the usual KDD model, closely related to CRISP-DM

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

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

Modifier and Type	Class and Description
class	KDDTask 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

Modifier and Type	Interface and Description
interface	Algorithm 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

Modifier and Type	Class and Description
class	AbstractAlgorithm<R extends Result> This class serves also as a model of implementing an algorithm within this framework.
class	AbstractDistanceBasedAlgorithm<O,D extends Distance<D>,R extends Result> Provides an abstract algorithm already setting the distance function.
class	AbstractPrimitiveDistanceBasedAlgorithm<O,D extends Distance<D>,R extends Result> Provides an abstract algorithm already setting the distance function.
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	DummyAlgorithm<O extends NumberVector<?,?>> Dummy Algorithm, which just iterates over all points once, doing a 10NN query each.
class	KNNDistanceOrder<O,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<O,D extends NumberDistance<D,?>> Algorithm to materialize all the distances in a data set.
class	NullAlgorithm 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

Modifier and Type	Interface and Description
interface	ClusteringAlgorithm<C extends Clustering<? extends Model>> Interface for Algorithms that are capable to provide a Clustering as Result. in general, clustering algorithms are supposed to implement the Algorithm-Interface.
interface	OPTICSTypeAlgorithm<D extends Distance<D>> Interface for OPTICS type algorithms, that can be analysed by OPTICS Xi etc.

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

Modifier and Type	Class and Description
class	AbstractProjectedClustering<R extends Clustering<Model>,V extends NumberVector<V,?>> Abstract superclass for projected clustering algorithms, like PROCLUS and ORCLUS.
class	AbstractProjectedDBSCAN<R extends Clustering<Model>,V extends NumberVector<V,?>> Provides an abstract algorithm requiring a VarianceAnalysisPreprocessor.
class	DBSCAN<O,D extends Distance<D>> DBSCAN provides the DBSCAN algorithm, an algorithm to find density-connected sets in a database.
class	DeLiClu<NV extends NumberVector<NV,?>,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<V extends NumberVector<V,?>,D extends Distance<D>> Provides the k-means algorithm.
class	OPTICS<O,D extends Distance<D>> OPTICS provides the OPTICS algorithm.
class	OPTICSXi<N extends NumberDistance<N,?>> Class to handle OPTICS Xi extraction.
class	SLINK<O,D extends Distance<D>> Efficient implementation of the Single-Link Algorithm SLINK of R.
class	SNNClustering<O> Shared nearest neighbor clustering.

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

Modifier and Type	Class and Description
class	CASH Provides the CASH algorithm, an subspace clustering algorithm based on the hough transform.
class	COPAC<V extends NumberVector<V,?>,D extends Distance<D>> 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<V extends NumberVector<V,?>> 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

Modifier and Type	Class and Description
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	SUBCLU<V extends NumberVector<V,?>> 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.clustering.trivial

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

Modifier and Type	Class and Description
class	ByLabelClustering Pseudo clustering using labels.
class	ByLabelHierarchicalClustering Pseudo clustering using labels.
class	TrivialAllInOne Trivial pseudo-clustering that just considers all points to be one big cluster.
class	TrivialAllNoise Trivial pseudo-clustering that just considers all points to be noise.

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

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

Modifier and Type	Interface and Description
interface	OutlierAlgorithm Generic super interface for outlier detection algorithms.

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

Modifier and Type	Class and Description
class	ABOD<V extends NumberVector<V,?>> Angle-Based Outlier Detection Outlier detection using variance analysis on angles, especially for high dimensional data sets.
class	AbstractAggarwalYuOutlier<V extends NumberVector<?,?>> Abstract base class for the sparse-grid-cell based outlier detection of Aggarwal and Yu.
class	AbstractDBOutlier<O,D extends Distance<D>> Simple distance based outlier detection algorithms.
class	AggarwalYuEvolutionary<V extends NumberVector<?,?>> EAFOD provides the evolutionary outlier detection algorithm, an algorithm to detect outliers for high dimensional data.
class	AggarwalYuNaive<V extends NumberVector<?,?>> BruteForce provides a naive brute force algorithm in which all k-subsets of dimensions are examined and calculates the sparsity coefficient to find outliers.
class	DBOutlierDetection<O,D extends Distance<D>> Simple distanced based outlier detection algorithm.
class	DBOutlierScore<O,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,?>> Outlier have smallest GMOD_PROB: the outlier scores is the probability density of the assumed distribution.
class	GaussianUniformMixture<V extends NumberVector<V,?>> Outlier detection algorithm using a mixture model approach.
class	INFLO<O,D extends NumberDistance<D,?>> 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 In Proc.
class	KNNOutlier<O,D extends NumberDistance<D,?>> Outlier Detection based on the distance of an object to its k nearest neighbor.
class	KNNWeightOutlier<O,D extends NumberDistance<D,?>> Outlier Detection based on the accumulated distances of a point to its k nearest neighbors.
class	LDOF<O,D extends NumberDistance<D,?>> Computes the LDOF (Local Distance-Based Outlier Factor) for all objects of a Database.
class	LOCI<O,D extends NumberDistance<D,?>> Fast Outlier Detection Using the "Local Correlation Integral".
class	LOF<O,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,D extends NumberDistance<D,?>> LoOP: Local Outlier Probabilities Distance/density based algorithm similar to LOF to detect outliers, but with statistical methods to achieve better result stability.
class	OnlineLOF<O,D extends NumberDistance<D,?>> Incremental version of the LOF Algorithm, supports insertions and removals.
class	OPTICSOF<O,D extends NumberDistance<D,?>> OPTICSOF provides the Optics-of algorithm, an algorithm to find Local Outliers in a database.
class	ReferenceBasedOutlierDetection<V extends NumberVector<?,?>,D extends NumberDistance<D,?>> provides the Reference-Based Outlier Detection algorithm, an algorithm that computes kNN distances approximately, using reference points.
class	SOD<V extends NumberVector<V,?>>

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

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

Modifier and Type	Class and Description
class	ExternalDoubleOutlierScore External outlier detection scores, loading outlier scores from an external file.
class	FeatureBagging A simple ensemble method called "Feature bagging" for outlier detection.
class	RescaleMetaOutlierAlgorithm Scale another outlier score using the given scaling function.

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

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

Modifier and Type	Class and Description
class	AbstractDistanceBasedSpatialOutlier<N,O,D extends NumberDistance<D,?>> Abstract base class for distance-based spatial outlier detection methods.
class	AbstractNeighborhoodOutlier<O> Abstract base class for spatial outlier detection methods using a spatial neighborhood.
class	CTLuGLSBackwardSearchAlgorithm<V extends NumberVector<?,?>,D extends NumberDistance<D,?>> GLS-Backward Search is a statistical approach to detecting spatial outliers.
class	CTLuMeanMultipleAttributes<N,O extends NumberVector<?,?>> Mean Approach is used to discover spatial outliers with multiple attributes.
class	CTLuMedianAlgorithm<N> Median Algorithm of C.
class	CTLuMedianMultipleAttributes<N,O extends NumberVector<?,?>> Median Approach is used to discover spatial outliers with multiple attributes.
class	CTLuMoranScatterplotOutlier<N> Moran scatterplot outliers, based on the standardized deviation from the local and global means.
class	CTLuRandomWalkEC<N,D extends NumberDistance<D,?>> Spatial outlier detection based on random walks.
class	CTLuScatterplotOutlier<N> Scatterplot-outlier is a spatial outlier detection method that performs a linear regression of object attributes and their neighbors average value.
class	CTLuZTestOutlier<N> Detect outliers by comparing their attribute value to the mean and standard deviation of their neighborhood.
class	SLOM<N,O,D extends NumberDistance<D,?>> SLOM: a new measure for local spatial outliers Reference: Sanjay Chawla and Pei Sun SLOM: a new measure for local spatial outliers in Knowledge and Information Systems 2005 This implementation works around some corner cases in SLOM, in particular when an object has none or a single neighbor only (albeit the results will still not be too useful then), which will result in divisions by zero.
class	SOF<N,O,D extends NumberDistance<D,?>> The Spatial Outlier Factor (SOF) is a spatial LOF variation.
class	TrimmedMeanApproach<N> A Trimmed Mean Approach to Finding Spatial Outliers.

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

Subinterfaces of Parameterizable in de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood

Modifier and Type	Interface and Description
static interface	NeighborSetPredicate.Factory<O> Factory interface to produce instances.

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

Modifier and Type	Class and Description
static class	AbstractPrecomputedNeighborhood.Factory<O> Factory class.
static class	ExtendedNeighborhood.Factory<O> Factory class.
static class	ExternalNeighborhood.Factory Factory class.
static class	PrecomputedKNearestNeighborNeighborhood.Factory<O,D extends Distance<D>> Factory class to instantiate for a particular relation.

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

Subinterfaces of Parameterizable in de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood.weighted

Modifier and Type	Interface and Description
static interface	WeightedNeighborSetPredicate.Factory<O> Factory interface to produce instances.

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

Modifier and Type	Class and Description
static class	LinearWeightedExtendedNeighborhood.Factory<O> Factory class.
static class	UnweightedNeighborhoodAdapter.Factory<O> Factory class

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

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

Modifier and Type	Class and Description
class	ByLabelOutlier Trivial algorithm that marks outliers by their label.
class	TrivialAllOutlier Trivial method that claims all objects to be outliers.
class	TrivialNoOutlier Trivial method that claims to find no outliers.

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

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

Modifier and Type	Class and Description
class	DistanceStatisticsWithClasses<O,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<O,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

Modifier and Type	Class and Description
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 Provides a KDDCLIApplication that can be used to perform any algorithm implementing Algorithm using any DatabaseConnection implementing DatabaseConnection.

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

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

Modifier and Type	Class and Description
class	CacheDoubleDistanceInOnDiskMatrix<O,D extends NumberDistance<D,?>> Wrapper to convert a traditional text-serialized result into a on-disk matrix for random access.
class	CacheFloatDistanceInOnDiskMatrix<O,D extends NumberDistance<D,?>> 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.jsmap

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

Modifier and Type	Class and Description
class	JSONResultHandler Handle results by serving them via a web server to mapping applications.

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

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

Modifier and Type	Class and Description
class	KNNExplorer<O extends NumberVector<?,?>,D extends NumberDistance<D,?>> 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

Modifier and Type	Interface and Description
interface	ComputeColorHistogram Interface for color histogram implementations.

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

Modifier and Type	Class and Description
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

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

Modifier and Type	Class and Description
class	HashmapDatabase 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	StaticArrayDatabase This database class uses array-based storage and thus does not allow for dynamic insert, delete and update operations.

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

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

Modifier and Type	Interface and Description
interface	DatabaseConnection DatabaseConnection is used to load data into a database.

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

Modifier and Type	Class and Description
class	AbstractDatabaseConnection Abstract super class for all database connections.
class	EmptyDatabaseConnection Pseudo database that is empty.
class	ExternalIDJoinDatabaseConnection Joins multiple data sources by their label
class	FileBasedDatabaseConnection Provides a file based database connection based on the parser to be set.
class	GeneratorXMLDatabaseConnection
class	InputStreamDatabaseConnection Provides a database connection expecting input from an input stream such as stdin.
class	LabelJoinDatabaseConnection Joins multiple data sources by their label
class	RandomDoubleVectorDatabaseConnection Produce a database of random double vectors with each dimension in [0:1]

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

Subinterfaces of Parameterizable in de.lmu.ifi.dbs.elki.datasource.filter

Modifier and Type	Interface and Description
interface	Normalization<O> 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.datasource.filter that implement Parameterizable

Modifier and Type	Class and Description
class	AbstractNormalization<O> Abstract super class for all normalizations.
class	AttributeWiseErfNormalization<O extends NumberVector<O,?>> Attribute-wise Normalization using the error function.
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	InverseDocumentFrequencyNormalization Normalization for text frequency vectors, using the inverse document frequency.
class	TFIDFNormalization Perform full TF-IDF Normalization as commonly used in text mining.

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

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

Modifier and Type	Interface and Description
interface	Parser A Parser shall provide a ParsingResult by parsing an InputStream.

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

Modifier and Type	Class and Description
class	ArffParser Parser to load WEKA .arff files into ELKI.
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	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	SimplePolygonParser Parser to load polygon data (2D and 3D only) from a simple format.
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.
class	TermFrequencyParser A parser to load term frequency data, which essentially are sparse vectors with text keys.

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

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

Modifier and Type	Interface and Description
interface	DBIDDistanceFunction<D extends Distance<?>> Distance functions valid in a database context only (i.e. for DBIDs) For any "distance" that cannot be computed for arbitrary objects, only those that exist in the database and referenced by their ID.
interface	DistanceFunction<O,D extends Distance<?>> Base interface for any kind of distances.
interface	FilteredLocalPCABasedDistanceFunction<O extends NumberVector<?,?>,P extends FilteredLocalPCAIndex<? super O>,D extends Distance<D>> Interface for local PCA based preprocessors.
interface	IndexBasedDistanceFunction<O,D extends Distance<D>> Distance function relying on an index (such as preprocessed neighborhoods).
interface	PrimitiveDistanceFunction<O,D extends Distance<?>> Primitive distance function that is defined on some kind of object.
interface	PrimitiveDoubleDistanceFunction<O> Interface for distance functions that can provide a raw double value.
interface	SpatialPrimitiveDistanceFunction<V extends SpatialComparable,D extends Distance<D>> API for a spatial primitive distance function.
interface	SpatialPrimitiveDoubleDistanceFunction<V extends SpatialComparable> Interface combining spatial primitive distance functions with primitive number distance functions.

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

Modifier and Type	Class and Description
class	AbstractCosineDistanceFunction Abstract base class for Cosine and ArcCosine distances.
class	AbstractDatabaseDistanceFunction<O,D extends Distance<D>> Abstract super class for distance functions needing a database context.
class	AbstractDBIDDistanceFunction<D extends Distance<D>> AbstractDistanceFunction provides some methods valid for any extending class.
class	AbstractIndexBasedDistanceFunction<O,I extends Index,D extends Distance<D>> Abstract super class for distance functions needing a database index.
class	AbstractPrimitiveDistanceFunction<O,D extends Distance<D>> AbstractDistanceFunction provides some methods valid for any extending class.
class	AbstractVectorDoubleDistanceFunction Abstract base class for the most common family of distance functions: defined on number vectors and returning double values.
class	ArcCosineDistanceFunction Cosine distance function for feature vectors.
class	CosineDistanceFunction Cosine distance function for feature vectors.
class	EuclideanDistanceFunction Provides the Euclidean distance for FeatureVectors.
class	LocallyWeightedDistanceFunction<V extends NumberVector<?,?>> Provides a locally weighted distance function.
class	LPNormDistanceFunction Provides a LP-Norm for FeatureVectors.
class	ManhattanDistanceFunction Manhattan distance function to compute the Manhattan distance for a pair of FeatureVectors.
class	MaximumDistanceFunction Maximum distance function to compute the Maximum distance for a pair of FeatureVectors.
class	MinimumDistanceFunction Maximum distance function to compute the Minimum distance for a pair of FeatureVectors.
class	MinKDistance<O,D extends Distance<D>> A distance that is at least the distance to the kth nearest neighbor.
class	ProxyDistanceFunction<O,D extends Distance<D>> Distance function to proxy computations to another distance (that probably was run before).
class	RandomStableDistanceFunction This is a dummy distance providing random values (obviously not metrical), useful mostly for unit tests and baseline evaluations: obviously this distance provides no benefit whatsoever.
class	SharedNearestNeighborJaccardDistanceFunction<O> SharedNearestNeighborJaccardDistanceFunction computes the Jaccard coefficient, which is a proper distance metric.
class	SquaredEuclideanDistanceFunction Provides the squared Euclidean distance for FeatureVectors.
class	WeightedDistanceFunction Provides the Weighted distance for feature vectors.
class	WeightedLPNormDistanceFunction Weighted version of the Euclidean distance function.
class	WeightedSquaredEuclideanDistanceFunction Provides the squared Euclidean distance for FeatureVectors.

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

Modifier and Type	Class and Description
class	AbstractSimilarityAdapter<O> Adapter from a normalized similarity function to a distance function.
class	SimilarityAdapterArccos<O> Adapter from a normalized similarity function to a distance function using arccos(sim).
class	SimilarityAdapterLinear<O> Adapter from a normalized similarity function to a distance function using 1 - sim.
class	SimilarityAdapterLn<O> 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

Modifier and Type	Class and Description
class	HistogramIntersectionDistanceFunction Intersection distance for color histograms.
class	HSBHistogramQuadraticDistanceFunction Distance function for HSB color histograms based on a quadratic form and color similarity.
class	RGBHistogramQuadraticDistanceFunction 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

Modifier and Type	Class and Description
class	ERiCDistanceFunction Provides a distance function for building the hierarchy in the ERiC algorithm.
class	PCABasedCorrelationDistanceFunction Provides the correlation distance for real valued vectors.
class	PearsonCorrelationDistanceFunction Pearson correlation distance function for feature vectors.
class	SquaredPearsonCorrelationDistanceFunction Squared Pearson correlation distance function for feature vectors.
class	WeightedPearsonCorrelationDistanceFunction Pearson correlation distance function for feature vectors.
class	WeightedSquaredPearsonCorrelationDistanceFunction 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

Modifier and Type	Class and Description
class	DiskCacheBasedDoubleDistanceFunction Provides a DistanceFunction that is based on double distances given by a distance matrix of an external file.
class	DiskCacheBasedFloatDistanceFunction Provides a DistanceFunction that is based on float distances given by a distance matrix of an external file.
class	FileBasedDoubleDistanceFunction Provides a DistanceFunction that is based on double distances given by a distance matrix of an external file.
class	FileBasedFloatDistanceFunction Provides a DistanceFunction that is based on float distances given by a distance matrix of an external file.

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

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

Modifier and Type	Class and Description
class	DimensionSelectingLatLngDistanceFunction Distance function for 2D vectors in Latitude, Longitude form.
class	LatLngDistanceFunction Distance function for 2D vectors in Latitude, Longitude form.
class	LngLatDistanceFunction Distance function for 2D vectors in Longitude, Latitude form.

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

Modifier and Type	Class and Description
class	AbstractDimensionsSelectingDoubleDistanceFunction<V extends FeatureVector<?,?>> 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<?,?>,P extends PreferenceVectorIndex<V>> Abstract super class for all preference vector based correlation distance functions.
class	DimensionSelectingDistanceFunction Provides a distance function that computes the distance between feature vectors as the absolute difference of their values in a specified dimension.
class	DimensionsSelectingEuclideanDistanceFunction Provides a distance function that computes the Euclidean distance between feature vectors only in specified dimensions.
class	DiSHDistanceFunction Distance function used in the DiSH algorithm.
class	HiSCDistanceFunction<V extends NumberVector<?,?>> Distance function used in the HiSC algorithm.
class	SubspaceDistanceFunction 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

Modifier and Type	Class and Description
class	AbstractEditDistanceFunction Provides the Edit Distance for FeatureVectors.
class	DTWDistanceFunction Provides the Dynamic Time Warping distance for FeatureVectors.
class	EDRDistanceFunction Provides the Edit Distance on Real Sequence distance for FeatureVectors.
class	ERPDistanceFunction Provides the Edit Distance With Real Penalty distance for FeatureVectors.
class	LCSSDistanceFunction 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

Modifier and Type	Interface and Description
interface	DBIDSimilarityFunction<D extends Distance<D>> Interface DBIDSimilarityFunction describes the requirements of any similarity function defined over object IDs.
interface	IndexBasedSimilarityFunction<O,D extends Distance<D>> Interface for preprocessor/index based similarity functions.
interface	NormalizedPrimitiveSimilarityFunction<O,D extends Distance<D>> Marker interface for similarity functions working on primitive objects, and limited to the 0-1 value range.
interface	NormalizedSimilarityFunction<O,D extends Distance<?>> Marker interface to signal that the similarity function is normalized to produce values in the range of [0:1].
interface	PrimitiveSimilarityFunction<O,D extends Distance<D>> Interface SimilarityFunction describes the requirements of any similarity function.
interface	SimilarityFunction<O,D extends Distance<?>> Interface SimilarityFunction describes the requirements of any similarity function.

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

Modifier and Type	Class and Description
class	AbstractDBIDSimilarityFunction<D extends Distance<D>> Abstract super class for distance functions needing a preprocessor.
class	AbstractIndexBasedSimilarityFunction<O,I extends Index,R,D extends Distance<D>> Abstract super class for distance functions needing a preprocessor.
class	AbstractPrimitiveSimilarityFunction<O,D extends Distance<D>> Base implementation of a similarity function.
class	FractionalSharedNearestNeighborSimilarityFunction<O> SharedNearestNeighborSimilarityFunction with a pattern defined to accept Strings that define a non-negative Integer.
class	SharedNearestNeighborSimilarityFunction<O> 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

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

Modifier and Type	Class and Description
class	FooKernelFunction Provides an experimental KernelDistanceFunction for NumberVectors.
class	LinearKernelFunction<O extends NumberVector<?,?>> Provides a linear Kernel function that computes a similarity between the two feature vectors V1 and V2 defined by V1^T*V2.
class	PolynomialKernelFunction 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.index

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

Modifier and Type	Interface and Description
interface	IndexFactory<V,I extends Index> Factory interface for indexes.

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

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

Modifier and Type	Interface and Description
static interface	LocalProjectionIndex.Factory<V extends NumberVector<?,?>,I extends LocalProjectionIndex<V,?>> Factory

Uses of Parameterizable in de.lmu.ifi.dbs.elki.index.preprocessed.knn

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

Modifier and Type	Class and Description
static class	AbstractMaterializeKNNPreprocessor.Factory<O,D extends Distance<D>> The parameterizable factory.
static class	MaterializeKNNAndRKNNPreprocessor.Factory<O,D extends Distance<D>> The parameterizable factory.
static class	MaterializeKNNPreprocessor.Factory<O,D extends Distance<D>> The parameterizable factory.
static class	MetricalIndexApproximationMaterializeKNNPreprocessor.Factory<O extends NumberVector<? super O,?>,D extends Distance<D>,N extends Node<E>,E extends MTreeEntry<D>> The parameterizable factory.
static class	PartitionApproximationMaterializeKNNPreprocessor.Factory<O,D extends Distance<D>> The parameterizable factory.
static class	SpatialApproximationMaterializeKNNPreprocessor.Factory<D extends Distance<D>,N extends SpatialNode<N,E>,E extends SpatialEntry> The actual preprocessor instance.

Uses of Parameterizable in de.lmu.ifi.dbs.elki.index.preprocessed.localpca

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

Modifier and Type	Interface and Description
static interface	FilteredLocalPCAIndex.Factory<NV extends NumberVector<?,?>,I extends FilteredLocalPCAIndex<NV>> Factory interface

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

Modifier and Type	Class and Description
static class	AbstractFilteredPCAIndex.Factory<NV extends NumberVector<NV,?>,I extends AbstractFilteredPCAIndex<NV>> Factory class
static class	KNNQueryFilteredPCAIndex.Factory<V extends NumberVector<V,?>> Factory class
static class	RangeQueryFilteredPCAIndex.Factory<V extends NumberVector<V,?>> Factory class

Uses of Parameterizable in de.lmu.ifi.dbs.elki.index.preprocessed.preference

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

Modifier and Type	Interface and Description
static interface	PreferenceVectorIndex.Factory<V extends NumberVector<?,?>,I extends PreferenceVectorIndex<V>> Factory interface

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

Modifier and Type	Class and Description
static class	AbstractPreferenceVectorIndex.Factory<V extends NumberVector<?,?>,I extends PreferenceVectorIndex<V>> Factory class
static class	DiSHPreferenceVectorIndex.Factory<V extends NumberVector<?,?>> Factory class
static class	HiSCPreferenceVectorIndex.Factory<V extends NumberVector<?,?>> Factory class

Uses of Parameterizable in de.lmu.ifi.dbs.elki.index.preprocessed.snn

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

Modifier and Type	Interface and Description
static interface	SharedNearestNeighborIndex.Factory<O,I extends SharedNearestNeighborIndex<O>> Factory interface

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

Modifier and Type	Class and Description
static class	SharedNearestNeighborPreprocessor.Factory<O,D extends Distance<D>> Factory class

Uses of Parameterizable in de.lmu.ifi.dbs.elki.index.preprocessed.subspaceproj

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

Modifier and Type	Interface and Description
static interface	SubspaceProjectionIndex.Factory<NV extends NumberVector<?,?>,I extends SubspaceProjectionIndex<NV,?>> Factory interface

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

Modifier and Type	Class and Description
static class	AbstractSubspaceProjectionIndex.Factory<NV extends NumberVector<?,?>,D extends Distance<D>,I extends AbstractSubspaceProjectionIndex<NV,D,?>> Factory class
static class	FourCSubspaceIndex.Factory<V extends NumberVector<V,?>,D extends Distance<D>> Factory class for 4C preprocessors.
static class	PreDeConSubspaceIndex.Factory<V extends NumberVector<? extends V,?>,D extends Distance<D>> Factory

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

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

Modifier and Type	Class and Description
class	TreeIndexFactory<O,I extends Index> Abstract base class for tree-based indexes.

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

Modifier and Type	Class and Description
class	AbstractMTreeFactory<O,D extends Distance<D>,N extends AbstractMTreeNode<O,D,N,E>,E extends MTreeEntry<D>,I extends AbstractMTree<O,D,N,E> & Index> Abstract factory for various MTrees

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

Modifier and Type	Class and Description
class	AbstractMkTreeUnifiedFactory<O,D extends Distance<D>,N extends AbstractMTreeNode<O,D,N,E>,E extends MTreeEntry<D>,I extends AbstractMkTree<O,D,N,E> & Index> Abstract factory for various Mk-Trees

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

Modifier and Type	Class and Description
class	MkAppTreeFactory<O,D extends NumberDistance<D,?>> Factory for a MkApp-Tree

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

Modifier and Type	Class and Description
class	MkCopTreeFactory<O,D extends NumberDistance<D,?>> Factory for a MkCoPTree-Tree

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

Modifier and Type	Class and Description
class	MkMaxTreeFactory<O,D extends Distance<D>> Factory for MkMaxTrees

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

Modifier and Type	Class and Description
class	MkTabTreeFactory<O,D extends Distance<D>> Factory for MkTabTrees

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

Modifier and Type	Class and Description
class	MTreeFactory<O,D extends Distance<D>> Factory for a M-Tree

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

Modifier and Type	Class and Description
class	AbstractRStarTreeFactory<O extends NumberVector<O,?>,N extends AbstractRStarTreeNode<N,E>,E extends SpatialEntry,I extends AbstractRStarTree<N,E> & Index> Abstract factory for R*-Tree based trees.

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

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

Modifier and Type	Class and Description
class	AbstractBulkSplit Encapsulates the required parameters for a bulk split of a spatial index.
class	MaxExtensionBulkSplit Split strategy for bulk-loading a spatial tree where the split axes are the dimensions with maximum extension.
class	ZCurveBulkSplit Bulk split that orders object by their Z curve position, then splits them into pages accordingly.

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

Modifier and Type	Class and Description
class	DeLiCluTreeFactory<O extends NumberVector<O,?>> Factory for DeLiClu R*-Trees.

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

Modifier and Type	Class and Description
class	RStarTreeFactory<O extends NumberVector<O,?>> Factory for regular R*-Trees.

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

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

Modifier and Type	Interface and Description
interface	InsertionStrategy Interface for implementing insertion strategies, i.e. in which path of the tree to insert the new element.

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

Modifier and Type	Class and Description
class	ApproximateLeastOverlapInsertionStrategy Insertion strategy that exhaustively tests all childs for the least overlap when inserting.
class	LeastOverlapInsertionStrategy Insertion strategy that exhaustively tests all childs for the least overlap when inserting.

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

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

Modifier and Type	Interface and Description
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

Modifier and Type	Class and Description
class	AbstractCovarianceMatrixBuilder<V extends NumberVector<? extends V,?>> Abstract class with the task of computing a Covariance matrix to be used in PCA.
class	CompositeEigenPairFilter The CompositeEigenPairFilter can be used to build a chain of eigenpair filters.
class	FirstNEigenPairFilter The FirstNEigenPairFilter marks the n highest eigenpairs as strong eigenpairs, where n is a user specified number.
class	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<? extends V,?>> PCA runner that will do dimensionality reduction.
class	PCARunner<V extends NumberVector<? extends V,?>> 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<? extends V,?>> 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<? extends V,?>> CovarianceMatrixBuilder with weights.

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

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

Modifier and Type	Interface and Description
interface	ResultHandler Interface for any class that can handle results

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

Modifier and Type	Class and Description
class	DiscardResultHandler A dummy result handler that discards the actual result, for use in benchmarks.
class	KMLOutputHandler Class to handle KML output.
class	ResultWriter 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

Modifier and Type	Interface and Description
interface	ReferencePointsHeuristic<O> Simple Interface for an heuristic to pick reference points.

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

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

Modifier and Type	Interface and Description
interface	ScalingFunction Interface for scaling functions used e.g. 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

Modifier and Type	Class and Description
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

Modifier and Type	Interface and Description
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

Modifier and Type	Class and Description
class	HeDESNormalizationOutlierScaling Normalization used by HeDES
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	MixtureModelOutlierScalingFunction Tries to fit a mixture model (exponential for inliers and gaussian for outliers) to the outlier score distribution.
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) Useful for example for scaling ABOD, but see MinusLogStandardDeviationScaling and MinusLogGammaScaling for more advanced scalings for this algorithm.
class	OutlierSqrtScaling Scaling that can map arbitrary positive values to a value in the range of [0:1].
class	RankingPseudoOutlierScaling This is a pseudo outlier scoring obtained by only considering the ranks of the objects.
class	SigmoidOutlierScalingFunction Tries to fit a sigmoid to the outlier scores and use it to convert the values to probability estimates in the range of 0.0 to 1.0
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

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

Modifier and Type	Class and Description
class	VisualizerParameterizer Utility class to determine the visualizers for a result class.

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

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

Modifier and Type	Class and Description
class	ResultVisualizer Handler to process and visualize a Result.

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

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

Modifier and Type	Interface and Description
interface	ProjectorFactory A projector is responsible for adding projections to the visualization by detecting appropriate relations in the database.

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

Modifier and Type	Class and Description
class	HistogramFactory Produce one-dimensional projections.
class	OPTICSProjectorFactory Produce OPTICS plot projections
class	ScatterPlotFactory Produce scatterplot projections.

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

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

Modifier and Type	Interface and Description
interface	VisFactory Defines the requirements for a visualizer.

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

Modifier and Type	Class and Description
class	AbstractVisFactory Abstract superclass for Visualizers (aka: Visualization Factories).

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

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

Modifier and Type	Class and Description
static class	OPTICSClusterVisualization.Factory Factory class for OPTICS plot selections.
static class	OPTICSPlotCutVisualization.Factory Factory class
static class	OPTICSPlotSelectionVisualization.Factory Factory class for OPTICS plot selections.
static class	OPTICSPlotVisualizer.Factory Factory class for OPTICS plot.
static class	OPTICSSteepAreaVisualization.Factory Factory class for OPTICS plot selections.

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

Modifier and Type	Class and Description
static class	P1DHistogramVisualizer.Factory<NV extends NumberVector<NV,?>> Visualizer factory for 1D histograms

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

Modifier and Type	Class and Description
static class	AxisVisualization.Factory<NV extends NumberVector<NV,?>> Factory for axis visualizations
static class	BubbleVisualization.Factory<NV extends NumberVector<NV,?>> Factory for producing bubble visualizations
static class	ClusterConvexHullVisualization.Factory<NV extends NumberVector<NV,?>> Factory for visualizers to generate an SVG-Element containing the convex hull of a cluster.
static class	ClusteringVisualization.Factory<NV extends NumberVector<NV,?>> Visualization factory
static class	ClusterMeanVisualization.Factory<NV extends NumberVector<NV,?>> Factory for visualizers to generate an SVG-Element containing a marker for the mean in a KMeans-Clustering
static class	ClusterOrderVisualization.Factory<NV extends NumberVector<NV,?>> Visualize an OPTICS cluster order by drawing connection lines.
static class	DotVisualization.Factory<NV extends NumberVector<NV,?>> The visualization factory
static class	EMClusterVisualization.Factory<NV extends NumberVector<NV,?>> Visualizer for generating SVG-Elements containing ellipses for first, second and third standard deviation
static class	MoveObjectsToolVisualization.Factory<NV extends NumberVector<NV,?>> Factory for tool visualizations for changing objects in the database
static class	PolygonVisualization.Factory The visualization factory
static class	ReferencePointsVisualization.Factory<NV extends NumberVector<NV,?>> Generates a SVG-Element visualizing reference points.
static class	SelectionConvexHullVisualization.Factory<NV extends NumberVector<NV,?>> Factory for visualizers to generate an SVG-Element containing the convex hull of the selected points
static class	SelectionCubeVisualization.Factory<NV extends NumberVector<NV,?>> Factory for visualizers to generate an SVG-Element containing a cube as marker representing the selected range for each dimension
static class	SelectionDotVisualization.Factory<NV extends NumberVector<NV,?>> Factory for visualizers to generate an SVG-Element containing dots as markers representing the selected Database's objects.
static class	SelectionToolCubeVisualization.Factory<NV extends NumberVector<NV,?>> Factory for tool visualizations for selecting ranges and the inclosed objects
static class	SelectionToolDotVisualization.Factory<NV extends NumberVector<NV,?>> Factory for tool visualizations for selecting objects
static class	ToolBox2DVisualization.Factory<NV extends NumberVector<NV,?>> Factory for visualizers for a toolbox
static class	TooltipScoreVisualization.Factory<NV extends NumberVector<NV,?>> Factory for tooltip visualizers
static class	TooltipStringVisualization.Factory<NV extends NumberVector<NV,?>> Factory
static class	TreeMBRVisualization.Factory<NV extends NumberVector<NV,?>> Factory
static class	TreeSphereVisualization.Factory<NV extends NumberVector<NV,?>> Factory

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

Modifier and Type	Class and Description
class	ClusterEvaluationVisFactory Pseudo-Visualizer, that lists the cluster evaluation results found.
class	CurveVisFactory Visualizer to render a simple 2D curve such as a ROC curve.
class	HistogramVisFactory Visualizer to draw histograms.
class	KeyVisFactory Pseudo-Visualizer, that gives the key for a clustering.
class	LabelVisFactory Trivial "visualizer" that displays a static label.
static class	PixmapVisualizer.Factory Factory class for pixmap visualizers.
class	SettingsVisFactory Pseudo-Visualizer, that lists the settings of the algorithm-
static class	SimilarityMatrixVisualizer.Factory Factory class for pixmap visualizers.

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

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

Modifier and Type	Interface and Description
interface	WorkflowStep Trivial interface for workflow steps.

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

Modifier and Type	Class and Description
class	AlgorithmStep The "algorithms" step, where data is analyzed.
class	EvaluationStep The "evaluation" step, where data is analyzed.
class	InputStep Data input step of the workflow.
class	LoggingStep Pseudo-step to configure logging / verbose mode.
class	OutputStep The "output" step, where data is analyzed.