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.classification	Classification algorithms.
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.em	Expectation-Maximization clustering algorithm.
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical	Hierarchical agglomerative clustering (HAC).
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans	K-means clustering and variations.
de.lmu.ifi.dbs.elki.algorithm.clustering.meta	Meta clustering algorithms, that get their result from other clusterings or external sources.
de.lmu.ifi.dbs.elki.algorithm.clustering.optics	OPTICS family of clustering algorithms.
de.lmu.ifi.dbs.elki.algorithm.clustering.subspace	Axis-parallel subspace clustering algorithms.
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.clustering.uncertain	Clustering algorithms for uncertain data.
de.lmu.ifi.dbs.elki.algorithm.itemsetmining	Algorithms for frequent itemset mining such as APRIORI.
de.lmu.ifi.dbs.elki.algorithm.outlier	Outlier detection algorithms
de.lmu.ifi.dbs.elki.algorithm.outlier.anglebased	Angle-based outlier detection algorithms.
de.lmu.ifi.dbs.elki.algorithm.outlier.clustering	Clustering based outlier detection.
de.lmu.ifi.dbs.elki.algorithm.outlier.distance	Distance-based outlier detection algorithms, such as DBOutlier and kNN.
de.lmu.ifi.dbs.elki.algorithm.outlier.lof	LOF family of 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.
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.transform	Data space transformations.
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.distance.distancefunction.strings	Distance functions for strings.
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.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	DependencyDerivator<V extends NumberVector> 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	KNNDistancesSampler<O> Provides an order of the kNN-distances for all objects within the database.
class	KNNJoin<V extends NumberVector,N extends SpatialNode<N,E>,E extends SpatialEntry> Joins in a given spatial database to each object its k-nearest neighbors.
class	MaterializeDistances<O> 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.classification

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

Modifier and Type	Class and Description
class	KNNClassifier<O> KNNClassifier classifies instances based on the class distribution among the k nearest neighbors in a database.
class	PriorProbabilityClassifier Classifier to classify instances based on the prior probability of classes in the database, without using the actual data values.

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> Density-Based Clustering of Applications with Noise (DBSCAN), an algorithm to find density-connected sets in a database.
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> The CASH algorithm is a subspace clustering algorithm based on the Hough transform.
class	COPAC<V extends NumberVector> COPAC is 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: Arbitrarily ORiented projected CLUSter generation.

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

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

Modifier and Type	Class and Description
class	EM<V extends NumberVector,M extends MeanModel> Clustering by expectation maximization (EM-Algorithm), also known as Gaussian Mixture Modeling (GMM).

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

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

Modifier and Type	Class and Description
class	AbstractHDBSCAN<O,R extends Result> Abstract base class for HDBSCAN variations.
class	HDBSCANLinearMemory<O> Linear memory implementation of HDBSCAN clustering.
class	SLINK<O> Implementation of the efficient Single-Link Algorithm SLINK of R.
class	SLINKHDBSCANLinearMemory<O> Linear memory implementation of HDBSCAN clustering based on SLINK.

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> The standard k-means algorithm, using Lloyd-style bulk iterations.
class	KMeansMacQueen<V extends NumberVector> The original k-means algorithm, using MacQueen style incremental updates; making this effectively an "online" (streaming) algorithm.

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

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

Modifier and Type	Class and Description
class	ExternalClustering Read an external clustering result from a file, such as produced by ClusteringVectorDumper.

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

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

Modifier and Type	Class and Description
class	AbstractOPTICS<O> The OPTICS algorithm for density-based hierarchical clustering.
class	DeLiClu<NV extends NumberVector> DeliClu: Density-Based Hierarchical Clustering, a hierarchical algorithm to find density-connected sets in a database.
class	OPTICSHeap<O> The OPTICS algorithm for density-based hierarchical clustering.
class	OPTICSList<O> The OPTICS algorithm for density-based hierarchical clustering.

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> 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.clustering.uncertain

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

Modifier and Type	Class and Description
class	FDBSCAN FDBSCAN is an adaption of DBSCAN for fuzzy (uncertain) objects.
class	FDBSCANNeighborPredicate Density-based Clustering of Applications with Noise and Fuzzy objects (FDBSCAN) is an Algorithm to find sets in a fuzzy database that are density-connected with minimum probability.

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

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

Modifier and Type	Class and Description
class	APRIORI The APRIORI algorithm for Mining Association Rules.

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	DWOF<O> Algorithm to compute dynamic-window outlier factors in a database based on a specified parameter DWOF.Parameterizer.K_ID (-dwof.k).
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	OPTICSOF<O> Optics-OF outlier detection algorithm, an algorithm to find Local Outliers in a database based on ideas from OPTICSTypeAlgorithm clustering.

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

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

Modifier and Type	Class and Description
class	ABOD<V extends NumberVector> Angle-Based Outlier Detection / Angle-Based Outlier Factor.
class	FastABOD<V extends NumberVector> Angle-Based Outlier Detection / Angle-Based Outlier Factor.
class	LBABOD<V extends NumberVector> Angle-Based Outlier Detection / Angle-Based Outlier Factor.

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

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

Modifier and Type	Class and Description
class	EMOutlier<V extends NumberVector> outlier detection algorithm using EM Clustering.

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

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

Modifier and Type	Class and Description
class	DBOutlierDetection<O> Simple distanced based outlier detection algorithm.
class	DBOutlierScore<O> Compute percentage of neighbors in the given neighborhood with size d.
class	HilOut<O extends NumberVector> Fast Outlier Detection in High Dimensional Spaces Outlier Detection using Hilbert space filling curves Reference: F.
class	KNNOutlier<O> Outlier Detection based on the distance of an object to its k nearest neighbor.
class	KNNWeightOutlier<O> Outlier Detection based on the accumulated distances of a point to its k nearest neighbors.
class	ReferenceBasedOutlierDetection 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.lof

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

Modifier and Type	Class and Description
class	ALOCI<O extends NumberVector> Fast Outlier Detection Using the "approximate Local Correlation Integral".
class	FlexibleLOF<O> Flexible variant of the "Local Outlier Factor" algorithm.
class	INFLO<O> Influence Outliers using Symmetric Relationship (INFLO) using two-way search, is an outlier detection method based on LOF; but also using the reverse kNN.
class	LDOF<O> Computes the LDOF (Local Distance-Based Outlier Factor) for all objects of a Database.
class	LOCI<O> Fast Outlier Detection Using the "Local Correlation Integral".
class	LOF<O> Algorithm to compute density-based local outlier factors in a database based on a specified parameter LOF.Parameterizer.K_ID (-lof.k).
class	LoOP<O> LoOP: Local Outlier Probabilities Distance/density based algorithm similar to LOF to detect outliers, but with statistical methods to achieve better result stability.

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<P> 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> 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	AggarwalYuEvolutionary<V extends NumberVector> Evolutionary variant (EAFOD) of the high-dimensional outlier detection algorithm by Aggarwal and Yu.
class	AggarwalYuNaive<V extends NumberVector> BruteForce variant of the high-dimensional outlier detection algorithm by Aggarwal and Yu.
class	OutRankS1 OutRank: ranking outliers in high dimensional data.
class	SOD<V extends NumberVector> 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> Algorithm to gather statistics over the distance distribution in the data set.
class	EvaluateRankingQuality<V extends NumberVector> Evaluate a distance function with respect to kNN queries.
class	RankingQualityHistogram<O> 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 Database storing data using hashtable storage, and thus allowing additional and removal of objects.
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 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.transform

Classes in de.lmu.ifi.dbs.elki.datasource.filter.transform 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).
class	PerturbationFilter<V extends NumberVector> A filter to perturb the values by adding micro-noise.

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 Parser for parsing one BitVector per line, bits separated by whitespace.
class	CategorialDataAsNumberVectorParser<V extends NumberVector> A very simple parser for categorial data, which will then be encoded as numbers.
class	SparseNumberVectorLabelParser<V extends SparseNumberVector> Parser for parsing one point per line, attributes separated by whitespace.
class	StringParser Parser that loads a text file for use with string similarity measures.
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	AsciiDistanceParser Parser for parsing one distance value per line.
class	DiskCacheBasedDoubleDistanceFunction Distance function that is based on double distances given by a distance matrix of an external binary matrix file.
class	DiskCacheBasedFloatDistanceFunction Distance function that is based on float distances given by a distance matrix of an external binary matrix file.
class	FileBasedDoubleDistanceFunction Distance function that is based on double distances given by a distance matrix of an external ASCII file.
class	FileBasedFloatDistanceFunction Distance function that is based on float distances given by a distance matrix of an external ASCII file.

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

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

Modifier and Type	Class and Description
class	LevenshteinDistanceFunction Classic Levenshtein distance on strings.
class	NormalizedLevenshteinDistanceFunction Levenshtein distance on strings, normalized by string length.

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> 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> A preprocessor for annotation of the k nearest neighbors (and their distances) to each database object.
class	MetricalIndexApproximationMaterializeKNNPreprocessor<O extends NumberVector,N extends Node<E>,E extends MTreeEntry> A preprocessor for annotation of the k nearest neighbors (and their distances) to each database object.
class	PartitionApproximationMaterializeKNNPreprocessor<O> A preprocessor for annotation of the k nearest neighbors (and their distances) to each database object.
class	SpatialApproximationMaterializeKNNPreprocessor<O extends NumberVector,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.

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> A preprocessor for annotation of the ids of nearest neighbors to each database object.

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> 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 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.