Uses of Class
de.lmu.ifi.dbs.elki.utilities.documentation.Description

Packages that use Description

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.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.colorhistogram	Distance functions using correlations.
de.lmu.ifi.dbs.elki.distance.distancefunction.external	Distance functions using external data sources.
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mtree	MTree
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.preprocessing	Preprocessors used for data preparation in a first step of various algorithms or distance and similarity measures.

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

Classes in de.lmu.ifi.dbs.elki.algorithm with annotations of type Description

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<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 Description in de.lmu.ifi.dbs.elki.algorithm.clustering

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering with annotations of type Description

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	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 Description in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation with annotations of type Description

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

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace with annotations of type 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,?>,D extends Distance<D>> Implementation of the SUBCLU algorithm, an algorithm to detect arbitrarily shaped and positioned clusters in subspaces.

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

Classes in de.lmu.ifi.dbs.elki.algorithm.outlier with annotations of type 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	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 Description in de.lmu.ifi.dbs.elki.algorithm.statistics

Classes in de.lmu.ifi.dbs.elki.algorithm.statistics with annotations of type Description

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

Classes in de.lmu.ifi.dbs.elki.database with annotations of type Description

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

Classes in de.lmu.ifi.dbs.elki.database.connection with annotations of type Description

class	EmptyDatabaseConnection<O extends DatabaseObject> Pseudo database that is empty.
class	InputStreamDatabaseConnection<O extends DatabaseObject> Provides a database connection expecting input from an input stream such as stdin.

Uses of Description in de.lmu.ifi.dbs.elki.distance.distancefunction.colorhistogram

Classes in de.lmu.ifi.dbs.elki.distance.distancefunction.colorhistogram with annotations of type Description

class

HistogramIntersectionDistanceFunction<V extends NumberVector<V,?>> Intersection distance for color histograms.

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

Classes in de.lmu.ifi.dbs.elki.distance.distancefunction.external with annotations of type Description

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

Classes in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mtree with annotations of type Description

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

Classes in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rstar with annotations of type Description

class

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

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

Classes in de.lmu.ifi.dbs.elki.math.linearalgebra.pca with annotations of type Description

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	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	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 Description in de.lmu.ifi.dbs.elki.normalization

Classes in de.lmu.ifi.dbs.elki.normalization with annotations of type Description

class

DummyNormalization<O extends DatabaseObject> Dummy normalization that does nothing.

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

Classes in de.lmu.ifi.dbs.elki.parser with annotations of type Description

class	BitVectorLabelParser Provides a parser for parsing one BitVector per line, bits separated by whitespace.
class	NumberDistanceParser<D extends NumberDistance<D,N>,N extends Number> Provides a parser for parsing one distance value per line.
class	ParameterizationFunctionLabelParser Provides a parser for parsing one point per line, attributes separated by whitespace.
class	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 Description in de.lmu.ifi.dbs.elki.preprocessing

Classes in de.lmu.ifi.dbs.elki.preprocessing with annotations of type Description

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	LocalPCAPreprocessor<V extends NumberVector<V,?>> Abstract superclass for preprocessors performing for each object of a certain database a filtered PCA based on the local neighborhood of the 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.