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

Packages that use Description

Package	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.
de.lmu.ifi.dbs.elki.algorithm.clustering.correlation	Correlation clustering algorithms
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans	K-means clustering and variations.
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.subspace	Subspace outlier detection methods.
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.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.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.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.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.result	Result types, representation and handling

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

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

Modifier and Type	Class and Description
class	APRIORI Provides the APRIORI algorithm for Mining Association Rules.
class	DependencyDerivator<V extends NumberVector<?>,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<?>,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 Description in de.lmu.ifi.dbs.elki.algorithm.clustering

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

Modifier and Type	Class and Description
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<?>,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<?>> Provides the EM algorithm (clustering by expectation maximization).
class	OPTICS<O,D extends Distance<D>> OPTICS provides the OPTICS algorithm.
class	SLINK<O,D extends Distance<D>> Implementation of the efficient Single-Link Algorithm SLINK of R.
class	SNNClustering<O> Shared nearest neighbor clustering.

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

Modifier and Type	Class and Description
class	CASH<V extends NumberVector<?>> Provides the CASH algorithm, an subspace clustering algorithm based on the Hough transform.
class	COPAC<V extends NumberVector<?>,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<?>> 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<?>> 4C identifies local subgroups of data objects sharing a uniform correlation.
class	HiCO<V extends NumberVector<?>> Implementation of the HiCO algorithm, an algorithm for detecting hierarchies of correlation clusters.
class	ORCLUS<V extends NumberVector<?>> 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.kmeans

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

Modifier and Type	Class and Description
class	KMeansLloyd<V extends NumberVector<?>,D extends Distance<D>> Provides the k-means algorithm, using Lloyd-style bulk iterations.
class	KMeansMacQueen<V extends NumberVector<?>,D extends Distance<D>> Provides the k-means algorithm, using MacQueen style incremental updates.

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

Modifier and Type	Class and Description
class	CLIQUE<V extends NumberVector<?>> Implementation of the CLIQUE algorithm, a grid-based algorithm to identify dense clusters in subspaces of maximum dimensionality.
class	DiSH<V extends NumberVector<?>> Algorithm for detecting subspace hierarchies.
class	HiSC<V extends NumberVector<?>> Implementation of the HiSC algorithm, an algorithm for detecting hierarchies of subspace clusters.
class	PreDeCon<V extends NumberVector<?>> PreDeCon computes clusters of subspace preference weighted connected points.
class	PROCLUS<V extends NumberVector<?>> Provides the PROCLUS algorithm, an algorithm to find subspace clusters in high dimensional spaces.
class	SUBCLU<V extends NumberVector<?>> 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.clustering.trivial

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

Modifier and Type	Class and Description
class	ByLabelClustering Pseudo clustering using labels.
class	ByLabelHierarchicalClustering Pseudo clustering using labels.
class	ByModelClustering Pseudo clustering using annotated models.
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 Description in de.lmu.ifi.dbs.elki.algorithm.outlier

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

Modifier and Type	Class and Description
class	ABOD<V extends NumberVector<?>> Angle-Based Outlier Detection Outlier detection using variance analysis on angles, especially for high dimensional data sets.
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	ALOCI<O extends NumberVector<?>,D extends NumberDistance<D,?>> Fast Outlier Detection Using the "approximate Local Correlation Integral".
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<?>> outlier detection algorithm using EM Clustering.
class	GaussianModel<V extends NumberVector<?>> Outlier have smallest GMOD_PROB: the outlier scores is the probability density of the assumed distribution.
class	GaussianUniformMixture<V extends NumberVector<?>> Outlier detection algorithm using a mixture model approach.
class	HilOut<O extends NumberVector<?>> Fast Outlier Detection in High Dimensional Spaces Outlier Detection using Hilbert space filling curves Reference: F.
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	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.

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

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

Modifier and Type	Class and Description
class	HiCS<V extends NumberVector<?>> Algorithm to compute High Contrast Subspaces for Density-Based Outlier Ranking.

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

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

Modifier and Type	Class and Description
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 9(4), 412-429, 2006 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	TrimmedMeanApproach<N> A Trimmed Mean Approach to Finding Spatial Outliers.

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

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

Modifier and Type	Class and Description
class	OutRankS1 OutRank: ranking outliers in high dimensional data.
class	SOD<V extends NumberVector<?>,D extends NumberDistance<D,?>> Subspace Outlier Degree.

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

Modifier and Type	Class and Description
class	AddSingleScale Pseudo "algorithm" that computes the global min/max for a relation across all attributes.
class	DistanceStatisticsWithClasses<O,D extends NumberDistance<D,?>> Algorithm to gather statistics over the distance distribution in the data set.
class	EvaluateRankingQuality<V extends NumberVector<?>,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 Description in de.lmu.ifi.dbs.elki.database

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

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

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

Modifier and Type	Class and Description
class	DBIDRangeDatabaseConnection This is a fake datasource that produces a static DBID range only.
class	EmptyDatabaseConnection Pseudo database that is empty.
class	InputStreamDatabaseConnection Provides a database connection expecting input from an input stream such as stdin.
class	PresortedBlindJoinDatabaseConnection Joins multiple data sources by their existing order.

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

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

Modifier and Type	Class and Description
class	HistogramJitterFilter<V extends NumberVector<?>> Add Jitter, preserving the histogram properties (same sum, nonnegative).

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

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

Modifier and Type	Class and Description
class	BitVectorLabelParser Provides a parser for parsing one BitVector per line, bits 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 Deprecated. Use SparseNumberVectorLabelParser instead!
class	SparseNumberVectorLabelParser<V extends SparseNumberVector<?>> Provides a parser for parsing one point per line, attributes separated by whitespace.
class	TermFrequencyParser<V extends SparseNumberVector<?>> A parser to load term frequency data, which essentially are sparse vectors with text keys.

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

Modifier and Type	Class and Description
class	HistogramIntersectionDistanceFunction 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

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.
class	NumberDistanceParser<D extends NumberDistance<D,?>> Provides a parser for parsing one distance value per line.

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

Classes in de.lmu.ifi.dbs.elki.index.preprocessed.knn with annotations of type Description

Modifier and Type	Class and Description
class	MaterializeKNNAndRKNNPreprocessor<O,D extends Distance<D>> A preprocessor for annotation of the k nearest neighbors and the reverse k nearest neighbors (and their distances) to each database object.
class	MaterializeKNNPreprocessor<O,D extends Distance<D>> A preprocessor for annotation of the k nearest neighbors (and their distances) to each database object.
class	MetricalIndexApproximationMaterializeKNNPreprocessor<O extends NumberVector<?>,D extends Distance<D>,N extends Node<E>,E extends MTreeEntry<D>> A preprocessor for annotation of the k nearest neighbors (and their distances) to each database object.
class	PartitionApproximationMaterializeKNNPreprocessor<O,D extends Distance<D>> A preprocessor for annotation of the k nearest neighbors (and their distances) to each database object.
class	SpatialApproximationMaterializeKNNPreprocessor<O extends NumberVector<?>,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 Description in de.lmu.ifi.dbs.elki.index.preprocessed.localpca

Classes in de.lmu.ifi.dbs.elki.index.preprocessed.localpca with annotations of type Description

Modifier and Type	Class and Description
class	AbstractFilteredPCAIndex<NV extends NumberVector<?>> Abstract base class for a local PCA based index.
class	KNNQueryFilteredPCAIndex<NV extends NumberVector<?>> Provides the local neighborhood to be considered in the PCA as the k nearest neighbors of an object.
class	RangeQueryFilteredPCAIndex<NV extends NumberVector<?>> Provides the local neighborhood to be considered in the PCA as the neighbors within an epsilon range query of an object.

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

Classes in de.lmu.ifi.dbs.elki.index.preprocessed.preference with annotations of type Description

Modifier and Type	Class and Description
class	DiSHPreferenceVectorIndex<V extends NumberVector<?>> Preprocessor for DiSH preference vector assignment to objects of a certain database.
class	HiSCPreferenceVectorIndex<V extends NumberVector<?>> Preprocessor for HiSC preference vector assignment to objects of a certain database.

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

Classes in de.lmu.ifi.dbs.elki.index.preprocessed.snn with annotations of type Description

Modifier and Type	Class and Description
class	SharedNearestNeighborPreprocessor<O,D extends Distance<D>> A preprocessor for annotation of the ids of nearest neighbors to each database object.

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

Classes in de.lmu.ifi.dbs.elki.index.preprocessed.subspaceproj with annotations of type Description

Modifier and Type	Class and Description
class	AbstractSubspaceProjectionIndex<NV extends NumberVector<?>,D extends Distance<D>,P extends ProjectionResult> Abstract base class for a local PCA based index.
class	FourCSubspaceIndex<V extends NumberVector<?>,D extends Distance<D>> Preprocessor for 4C local dimensionality and locally weighted matrix assignment to objects of a certain database.
class	PreDeConSubspaceIndex<V extends NumberVector<?>,D extends Distance<D>> Preprocessor for PreDeCon local dimensionality and locally weighted matrix assignment to objects of a certain database.

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

Modifier and Type	Class and Description
class	MTree<O,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

Modifier and Type	Class and Description
class	RStarTree 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

Modifier and Type	Class and 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<?>> CovarianceMatrixBuilder with weights.

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

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

Modifier and Type	Class and Description
class	LogResultStructureResultHandler A result handler to help with ELKI development that will just show the structure of the result object.