E-Index (ELKI: Environment for DeveLoping KDD-Applications Supported by Index-Structures)

A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
All Classes All Packages

E

e - Variable in class elki.math.geodesy.AbstractEarthModel: Derived model parameters: e and e squared.
e - Variable in class elki.math.linearalgebra.EigenvalueDecomposition: Arrays for internal storage of eigenvalues.
e - Variable in class elki.math.statistics.MultipleLinearRegression: The (n x 1) - double[] holding the estimated residuals (e1, ..., en)^T.
e - Variable in class elki.visualization.batikutil.AttributeModifier: Provides the attribute to be modified.
EagerIt() - Constructor for class elki.result.Metadata.EagerIt
EagerPAM<O> - Class in elki.clustering.kmedoids: Variation of PAM that eagerly performs all swaps that yield an improvement during an iteration.
EagerPAM(Distance<? super O>, int, int, KMedoidsInitialization<O>) - Constructor for class elki.clustering.kmedoids.EagerPAM: Constructor.
EagerPAM.Instance - Class in elki.clustering.kmedoids: Instance for a single dataset.
EagerPAM.Par<O> - Class in elki.clustering.kmedoids: Parameterization class.
EARLY_EXAGGERATION - Static variable in class elki.projection.TSNE: Early exaggeration factor.
EARLY_EXAGGERATION_ITERATIONS - Static variable in class elki.projection.TSNE: Number of iterations to apply early exaggeration.
EARTH_RADIUS - Static variable in class elki.math.geodesy.SphericalCosineEarthModel: Earth radius approximation in m.
EARTH_RADIUS - Static variable in class elki.math.geodesy.SphericalHaversineEarthModel: Earth radius approximation in m.
EARTH_RADIUS - Static variable in class elki.math.geodesy.SphericalVincentyEarthModel: Earth radius approximation in m.
EarthModel - Interface in elki.math.geodesy: API for handling different earth models.
ecefToLatDeg(double, double, double) - Method in class elki.math.geodesy.AbstractEarthModel
ecefToLatDeg(double, double, double) - Method in interface elki.math.geodesy.EarthModel: Convert a 3D coordinate pair to the corresponding latitude.
ecefToLatLngDegHeight(double, double, double) - Method in class elki.math.geodesy.AbstractEarthModel
ecefToLatLngDegHeight(double, double, double) - Method in interface elki.math.geodesy.EarthModel: Convert a 3D coordinate pair to the corresponding latitude, longitude and height.
ecefToLatLngRadHeight(double, double, double) - Method in class elki.math.geodesy.AbstractEarthModel
ecefToLatLngRadHeight(double, double, double) - Method in interface elki.math.geodesy.EarthModel: Convert a 3D coordinate pair to the corresponding latitude, longitude and height.
ecefToLatRad(double, double, double) - Method in class elki.math.geodesy.AbstractEarthModel
ecefToLatRad(double, double, double) - Method in interface elki.math.geodesy.EarthModel: Convert a 3D coordinate pair to the corresponding latitude.
ecefToLatRad(double, double, double) - Method in class elki.math.geodesy.SphericalCosineEarthModel
ecefToLatRad(double, double, double) - Method in class elki.math.geodesy.SphericalHaversineEarthModel
ecefToLatRad(double, double, double) - Method in class elki.math.geodesy.SphericalVincentyEarthModel
ecefToLngDeg(double, double) - Method in class elki.math.geodesy.AbstractEarthModel
ecefToLngDeg(double, double) - Method in interface elki.math.geodesy.EarthModel: Convert a 3D coordinate pair to the corresponding longitude.
ecefToLngRad(double, double) - Method in class elki.math.geodesy.AbstractEarthModel
ecefToLngRad(double, double) - Method in interface elki.math.geodesy.EarthModel: Convert a 3D coordinate pair to the corresponding longitude.
Eclat - Class in elki.itemsetmining: Eclat is a depth-first discovery algorithm for mining frequent itemsets.
Eclat(double, int, int) - Constructor for class elki.itemsetmining.Eclat: Constructor.
Eclat.Par - Class in elki.itemsetmining: Parameterization class.
Edge(int, int) - Constructor for class elki.visualization.parallel3d.layout.Layout.Edge: Constructor.
edgelength(double[][], int[], int) - Static method in class elki.index.tree.metrical.mtreevariants.strategies.split.MSTSplit: Length of edge i.
edges - Variable in class elki.visualization.parallel3d.layout.Layout: Edges
edit - Variable in class elki.evaluation.clustering.ClusterContingencyTable: Edit-Distance measures
EDIT_CLEAR - Static variable in class elki.gui.icons.StockIcon
EDIT_FIND - Static variable in class elki.gui.icons.StockIcon
EDIT_REDO - Static variable in class elki.gui.icons.StockIcon
EDIT_UNDO - Static variable in class elki.gui.icons.StockIcon
EditDistance - Class in elki.evaluation.clustering: Edit distance measures.
EditDistance(ClusterContingencyTable) - Constructor for class elki.evaluation.clustering.EditDistance
editDistanceFirst() - Method in class elki.evaluation.clustering.EditDistance: Get the editing distance to transform second clustering to first clustering (normalized, 0 = unequal)
editDistanceSecond() - Method in class elki.evaluation.clustering.EditDistance: Get the editing distance to transform second clustering to first clustering (normalized, 0 = unequal)
editFirst - Variable in class elki.evaluation.clustering.EditDistance: Edit operations for first clustering to second clustering.
editOperationsBaseline - Variable in class elki.evaluation.clustering.EditDistance: Baseline for edit operations
editOperationsBaseline() - Method in class elki.evaluation.clustering.EditDistance: Get the baseline editing Operations (= total objects)
editOperationsFirst() - Method in class elki.evaluation.clustering.EditDistance: Get the editing operations required to transform first clustering to second clustering
editOperationsSecond() - Method in class elki.evaluation.clustering.EditDistance: Get the editing operations required to transform second clustering to first clustering
editSecond - Variable in class elki.evaluation.clustering.EditDistance: Edit operations for second clustering to first clustering.
EDRDistance - Class in elki.distance.timeseries: Edit Distance on Real Sequence distance for numerical vectors.
EDRDistance(double, double) - Constructor for class elki.distance.timeseries.EDRDistance: Constructor.
EDRDistance.Par - Class in elki.distance.timeseries: Parameterization class.
effectiveBandSize(int, int) - Method in class elki.distance.timeseries.AbstractEditDistance: Compute the effective band size.
eigenFilter - Variable in class elki.math.statistics.intrinsicdimensionality.LPCAEstimator: Eigenvalue filter
EigenPair - Class in elki.math.linearalgebra.pca: Helper class which encapsulates an eigenvector and its corresponding eigenvalue.
EigenPair(double[], double) - Constructor for class elki.math.linearalgebra.pca.EigenPair: Creates a new EigenPair object.
EIGENPAIR_FILTER_ABSOLUTE - Static variable in class elki.math.linearalgebra.pca.filter.LimitEigenPairFilter.Par: "absolute" Flag
EIGENPAIR_FILTER_DELTA - Static variable in class elki.math.linearalgebra.pca.filter.LimitEigenPairFilter.Par: Parameter delta
EIGENPAIR_FILTER_N - Static variable in class elki.math.linearalgebra.pca.filter.FirstNEigenPairFilter.Par: Parameter n
EIGENPAIR_FILTER_PALPHA - Static variable in class elki.math.linearalgebra.pca.filter.ProgressiveEigenPairFilter.Par: Parameter progressive alpha.
EIGENPAIR_FILTER_RALPHA - Static variable in class elki.math.linearalgebra.pca.filter.RelativeEigenPairFilter.Par: Parameter relative alpha.
EIGENPAIR_FILTER_WALPHA - Static variable in class elki.math.linearalgebra.pca.filter.WeakEigenPairFilter.Par: OptionID for the weak alpha value of WeakEigenPairFilter, ProgressiveEigenPairFilter, and SignificantEigenPairFilter
eigenPairFilter - Variable in class elki.math.statistics.intrinsicdimensionality.LPCAEstimator.Par: EigenPairFilter to use.
EigenPairFilter - Interface in elki.math.linearalgebra.pca.filter: 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., Principal Component Analysis.
eigenPairs - Variable in class elki.math.linearalgebra.pca.PCAResult: The eigenpairs in decreasing order.
eigenvalue - Variable in class elki.math.linearalgebra.pca.EigenPair: The corresponding eigenvalue.
EigenvalueDecomposition - Class in elki.math.linearalgebra: Eigenvalues and eigenvectors of a real matrix.
EigenvalueDecomposition(double[][]) - Constructor for class elki.math.linearalgebra.EigenvalueDecomposition: Check for symmetry, then construct the eigenvalue decomposition
eigenvalues - Variable in class elki.math.linearalgebra.pca.PCAResult: The eigenvalues in decreasing order.
eigenvector - Variable in class elki.math.linearalgebra.pca.EigenPair: The eigenvector as a matrix.
eigenvectors - Variable in class elki.math.linearalgebra.pca.PCAResult: The eigenvectors in decreasing order to their corresponding eigenvalues.
element - Variable in class elki.visualization.batikutil.DragableArea: Our element node.
elementCoordinatesFromEvent(Document, Element, Event) - Static method in class elki.visualization.svg.SVGUtil: Convert the coordinates of an DOM Event from screen into element coordinates.
elementCoordinatesFromEvent(Element, Event) - Method in class elki.visualization.svg.SVGPlot: Convert screen coordinates to element coordinates.
elementLine - Variable in class elki.visualization.visualizers.optics.OPTICSPlotCutVisualization.Instance: The line element
elementPoint - Variable in class elki.visualization.visualizers.optics.OPTICSPlotCutVisualization.Instance: The drag handle element
elements - Variable in class elki.math.linearalgebra.Centroid: Vector elements.
elements - Variable in class elki.math.linearalgebra.CovarianceMatrix: The covariance matrix.
elems - Variable in class elki.utilities.datastructures.hierarchy.HashMapHierarchy: All elements, in insertion order (and will not fail badly if concurrent insertions happen).
elemText - Variable in class elki.visualization.visualizers.optics.OPTICSPlotCutVisualization.Instance: The label element
ElkanKMeans<V extends NumberVector> - Class in elki.clustering.kmeans: Elkan's fast k-means by exploiting the triangle inequality.
ElkanKMeans(NumberVectorDistance<? super V>, int, int, KMeansInitialization, boolean) - Constructor for class elki.clustering.kmeans.ElkanKMeans: Constructor.
ElkanKMeans.Instance - Class in elki.clustering.kmeans: Inner instance, storing state for a single data set.
ElkanKMeans.Par<V extends NumberVector> - Class in elki.clustering.kmeans: Parameterization class.
elki - package elki: ELKI framework "Environment for Developing KDD-Applications Supported by Index-Structures".
elki.algorithm - package elki.algorithm: Miscellaneous algorithms.
elki.algorithm.statistics - package elki.algorithm.statistics: Statistical analysis algorithms.
elki.application - package elki.application: Base classes for standalone applications.
elki.application.benchmark - package elki.application.benchmark: Benchmarking pseudo algorithms.
elki.application.cache - package elki.application.cache: Utility applications for the persistence layer such as distance cache builders.
elki.application.experiments - package elki.application.experiments: Packaged experiments to make them easy to reproduce.
elki.application.greedyensemble - package elki.application.greedyensemble: Greedy ensembles for outlier detection.
elki.application.internal - package elki.application.internal: Internal utilities for development.
elki.application.statistics - package elki.application.statistics: Applications to compute some basic data set statistics.
elki.classification - package elki.classification: Classification algorithms.
elki.clustering - package elki.clustering: Clustering algorithms.
elki.clustering.affinitypropagation - package elki.clustering.affinitypropagation: Affinity Propagation (AP) clustering.
elki.clustering.biclustering - package elki.clustering.biclustering: Biclustering algorithms.
elki.clustering.correlation - package elki.clustering.correlation: Correlation clustering algorithms.
elki.clustering.correlation.cash - package elki.clustering.correlation.cash: Helper classes for the CASH algorithm.
elki.clustering.dbscan - package elki.clustering.dbscan: DBSCAN and its generalizations.
elki.clustering.dbscan.parallel - package elki.clustering.dbscan.parallel: Parallel versions of Generalized DBSCAN.
elki.clustering.dbscan.predicates - package elki.clustering.dbscan.predicates: Neighbor and core predicated for Generalized DBSCAN.
elki.clustering.dbscan.util - package elki.clustering.dbscan.util: Utility classes for specialized DBSCAN implementations.
elki.clustering.em - package elki.clustering.em: Expectation-Maximization clustering algorithm for Gaussian Mixture Modeling (GMM).
elki.clustering.em.models - package elki.clustering.em.models
elki.clustering.hierarchical - package elki.clustering.hierarchical: Hierarchical agglomerative clustering (HAC).
elki.clustering.hierarchical.birch - package elki.clustering.hierarchical.birch: BIRCH clustering.
elki.clustering.hierarchical.extraction - package elki.clustering.hierarchical.extraction: Extraction of partitional clusterings from hierarchical results.
elki.clustering.hierarchical.linkage - package elki.clustering.hierarchical.linkage: Linkages for hierarchical clustering.
elki.clustering.kcenter - package elki.clustering.kcenter: K-center clustering.
elki.clustering.kmeans - package elki.clustering.kmeans: K-means clustering and variations.
elki.clustering.kmeans.initialization - package elki.clustering.kmeans.initialization: Initialization strategies for k-means.
elki.clustering.kmeans.initialization.betula - package elki.clustering.kmeans.initialization.betula: Initialization methods for BIRCH-based k-means and EM clustering.
elki.clustering.kmeans.parallel - package elki.clustering.kmeans.parallel: Parallelized implementations of k-means.
elki.clustering.kmeans.quality - package elki.clustering.kmeans.quality: Quality measures for k-Means results.
elki.clustering.kmeans.spherical - package elki.clustering.kmeans.spherical: Spherical k-means clustering and variations.
elki.clustering.kmedoids - package elki.clustering.kmedoids: K-medoids clustering (PAM).
elki.clustering.kmedoids.initialization - package elki.clustering.kmedoids.initialization
elki.clustering.meta - package elki.clustering.meta: Meta clustering algorithms, that get their result from other clusterings or external sources.
elki.clustering.onedimensional - package elki.clustering.onedimensional: Clustering algorithms for one-dimensional data.
elki.clustering.optics - package elki.clustering.optics: OPTICS family of clustering algorithms.
elki.clustering.silhouette - package elki.clustering.silhouette: Silhouette clustering algorithms.
elki.clustering.subspace - package elki.clustering.subspace: Axis-parallel subspace clustering algorithms.
elki.clustering.subspace.clique - package elki.clustering.subspace.clique: Helper classes for the CLIQUE algorithm.
elki.clustering.svm - package elki.clustering.svm
elki.clustering.trivial - package elki.clustering.trivial: Trivial clustering algorithms: all in one, no clusters, label clusterings.
elki.clustering.uncertain - package elki.clustering.uncertain: Clustering algorithms for uncertain data.
elki.data - package elki.data: Basic classes for different data types, database object types and label types.
elki.data.model - package elki.data.model: Cluster models classes for various algorithms.
elki.data.projection - package elki.data.projection: Data projections.
elki.data.projection.random - package elki.data.projection.random: Random projection families.
elki.data.spatial - package elki.data.spatial: Spatial data types - interfaces and utilities.
elki.data.synthetic.bymodel - package elki.data.synthetic.bymodel: Generator using a distribution model specified in an XML configuration file.
elki.data.type - package elki.data.type: Data type information, also used for type restrictions.
elki.data.uncertain - package elki.data.uncertain: Uncertain data objects.
elki.data.uncertain.uncertainifier - package elki.data.uncertain.uncertainifier: Classes to generate uncertain objects from existing certain data.
elki.database - package elki.database: ELKI database layer - loading, storing, indexing and accessing data.
elki.database.datastore - package elki.database.datastore: General data store layer API (along the lines of Map<DBID, T> - use everywhere!)
elki.database.datastore.memory - package elki.database.datastore.memory: Memory data store implementation for ELKI.
elki.database.ids - package elki.database.ids: Database object identification and ID group handling API.
elki.database.ids.integer - package elki.database.ids.integer: Integer-based DBID implementation -- do not use directly - always use DBIDUtil.
elki.database.query - package elki.database.query: Database queries - computing distances, neighbors, similarities - API and general documentation.
elki.database.query.distance - package elki.database.query.distance: Prepared queries for distances.
elki.database.query.knn - package elki.database.query.knn: Prepared queries for k nearest neighbor (kNN) queries.
elki.database.query.range - package elki.database.query.range: Prepared queries for ε-range queries, that return all objects within the radius ε.
elki.database.query.rknn - package elki.database.query.rknn: Prepared queries for reverse k nearest neighbor (rkNN) queries.
elki.database.query.similarity - package elki.database.query.similarity: Prepared queries for similarity functions.
elki.database.relation - package elki.database.relation: Relations, materialized and virtual (views).
elki.datasource - package elki.datasource: Data normalization (and reconstitution) of data sets.
elki.datasource.bundle - package elki.datasource.bundle: Object bundles - exchange container for multi-represented objects.
elki.datasource.filter - package elki.datasource.filter: Data filtering, in particular for normalization and projection.
elki.datasource.filter.cleaning - package elki.datasource.filter.cleaning: Filters for data cleaning.
elki.datasource.filter.normalization - package elki.datasource.filter.normalization: Data normalization.
elki.datasource.filter.normalization.columnwise - package elki.datasource.filter.normalization.columnwise: Normalizations operating on columns / variates; where each column is treated independently.
elki.datasource.filter.normalization.instancewise - package elki.datasource.filter.normalization.instancewise: Instancewise normalization, where each instance is normalized independently.
elki.datasource.filter.selection - package elki.datasource.filter.selection: Filters for selecting and sorting data to process.
elki.datasource.filter.transform - package elki.datasource.filter.transform: Data space transformations.
elki.datasource.filter.typeconversions - package elki.datasource.filter.typeconversions: Filters to perform data type conversions.
elki.datasource.parser - package elki.datasource.parser: Parsers for different file formats and data types.
elki.distance - package elki.distance: Distance functions for use within ELKI.
elki.distance.adapter - package elki.distance.adapter: Distance functions deriving distances from, e.g., similarity measures.
elki.distance.colorhistogram - package elki.distance.colorhistogram: Distance functions for color histograms.
elki.distance.correlation - package elki.distance.correlation: Distance functions using correlations.
elki.distance.external - package elki.distance.external: Distance functions using external data sources.
elki.distance.geo - package elki.distance.geo: Geographic (earth) distance functions.
elki.distance.histogram - package elki.distance.histogram: Distance functions for one-dimensional histograms.
elki.distance.minkowski - package elki.distance.minkowski: Minkowski space L_p norms such as the popular Euclidean and Manhattan distances.
elki.distance.probabilistic - package elki.distance.probabilistic: Distance from probability theory, mostly divergences such as K-L-divergence, J-divergence, F-divergence, χ²-divergence, etc.
elki.distance.set - package elki.distance.set: Distance functions for binary and set type data.
elki.distance.strings - package elki.distance.strings: Distance functions for strings.
elki.distance.subspace - package elki.distance.subspace: Distance functions based on subspaces.
elki.distance.timeseries - package elki.distance.timeseries: Distance functions designed for time series.
elki.evaluation - package elki.evaluation: Functionality for the evaluation of algorithms.
elki.evaluation.classification - package elki.evaluation.classification: Evaluation of classification algorithms.
elki.evaluation.classification.holdout - package elki.evaluation.classification.holdout: Holdout and cross-validation strategies for evaluating classifiers.
elki.evaluation.clustering - package elki.evaluation.clustering: Evaluation of clustering results.
elki.evaluation.clustering.extractor - package elki.evaluation.clustering.extractor: Classes to extract clusterings from hierarchical clustering.
elki.evaluation.clustering.internal - package elki.evaluation.clustering.internal: Internal evaluation measures for clusterings.
elki.evaluation.clustering.pairsegments - package elki.evaluation.clustering.pairsegments: Pair-segment analysis of multiple clusterings.
elki.evaluation.index - package elki.evaluation.index: Simple index evaluation methods.
elki.evaluation.outlier - package elki.evaluation.outlier: Evaluate an outlier score using a misclassification based cost model.
elki.evaluation.scores - package elki.evaluation.scores: Evaluation of rankings and scorings.
elki.evaluation.scores.adapter - package elki.evaluation.scores.adapter: Adapter classes for ranking and scoring measures.
elki.evaluation.similaritymatrix - package elki.evaluation.similaritymatrix: Render a distance matrix to visualize a clustering-distance-combination.
elki.gui - package elki.gui: Graphical User Interfaces for ELKI.
elki.gui.configurator - package elki.gui.configurator: Configurator components.
elki.gui.icons - package elki.gui.icons: Icons for ELKI GUI.
elki.gui.minigui - package elki.gui.minigui: A very simple UI to build ELKI command lines.
elki.gui.multistep - package elki.gui.multistep: Multi-step GUI for ELKI.
elki.gui.multistep.panels - package elki.gui.multistep.panels: Panels for the multi-step GUI.
elki.gui.util - package elki.gui.util: Utility classes for GUIs (e.g., a class to display a logging panel).
elki.index - package elki.index: Index structure implementations.
elki.index.distancematrix - package elki.index.distancematrix: Precomputed distance matrix.
elki.index.idistance - package elki.index.idistance: iDistance is a distance based indexing technique, using a reference points embedding.
elki.index.invertedlist - package elki.index.invertedlist: Indexes using inverted lists.
elki.index.laesa - package elki.index.laesa: Linear Approximating and Eliminating Search Algorithm (LAESA).
elki.index.lsh - package elki.index.lsh: Locality Sensitive Hashing.
elki.index.lsh.hashfamilies - package elki.index.lsh.hashfamilies: Hash function families for LSH.
elki.index.lsh.hashfunctions - package elki.index.lsh.hashfunctions: Hash functions for LSH.
elki.index.preprocessed.fastoptics - package elki.index.preprocessed.fastoptics: Preprocessed index used by the FastOPTICS algorithm.
elki.index.preprocessed.knn - package elki.index.preprocessed.knn: Indexes providing KNN and rKNN data.
elki.index.preprocessed.snn - package elki.index.preprocessed.snn: Indexes providing nearest neighbor sets.
elki.index.projected - package elki.index.projected: Projected indexes for data.
elki.index.tree - package elki.index.tree: Tree-based index structures.
elki.index.tree.betula - package elki.index.tree.betula: BETULA clustering by aggregating the data into cluster features.
elki.index.tree.betula.distance - package elki.index.tree.betula.distance: Distance functions for BETULA and BIRCH.
elki.index.tree.betula.features - package elki.index.tree.betula.features: Different variants of Betula and BIRCH cluster features.
elki.index.tree.metrical - package elki.index.tree.metrical: Tree-based index structures for metrical vector spaces.
elki.index.tree.metrical.covertree - package elki.index.tree.metrical.covertree: Cover-tree variations.
elki.index.tree.metrical.mtreevariants - package elki.index.tree.metrical.mtreevariants: M-tree and variants.
elki.index.tree.metrical.mtreevariants.mktrees - package 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.
elki.index.tree.metrical.mtreevariants.mktrees.mkapp - package elki.index.tree.metrical.mtreevariants.mktrees.mkapp: MkAppTree.
elki.index.tree.metrical.mtreevariants.mktrees.mkcop - package elki.index.tree.metrical.mtreevariants.mktrees.mkcop: MkCoPTree.
elki.index.tree.metrical.mtreevariants.mktrees.mkmax - package elki.index.tree.metrical.mtreevariants.mktrees.mkmax: MkMaxTree.
elki.index.tree.metrical.mtreevariants.mktrees.mktab - package elki.index.tree.metrical.mtreevariants.mktrees.mktab: MkTabTree.
elki.index.tree.metrical.mtreevariants.mtree - package elki.index.tree.metrical.mtreevariants.mtree: MTree.
elki.index.tree.metrical.mtreevariants.query - package elki.index.tree.metrical.mtreevariants.query: Classes for performing queries (knn, range, ...) on metrical trees.
elki.index.tree.metrical.mtreevariants.strategies.insert - package elki.index.tree.metrical.mtreevariants.strategies.insert: Insertion (choose path) strategies of nodes in an M-tree (and variants).
elki.index.tree.metrical.mtreevariants.strategies.split - package elki.index.tree.metrical.mtreevariants.strategies.split: Splitting strategies of nodes in an M-tree (and variants).
elki.index.tree.metrical.mtreevariants.strategies.split.distribution - package elki.index.tree.metrical.mtreevariants.strategies.split.distribution: Entry distribution strategies of nodes in an M-tree (and variants).
elki.index.tree.metrical.vptree - package elki.index.tree.metrical.vptree
elki.index.tree.spatial - package elki.index.tree.spatial: Tree-based index structures for spatial indexing.
elki.index.tree.spatial.kd - package elki.index.tree.spatial.kd: K-d-tree and variants.
elki.index.tree.spatial.kd.split - package elki.index.tree.spatial.kd.split
elki.index.tree.spatial.rstarvariants - package elki.index.tree.spatial.rstarvariants: R*-tree and variants.
elki.index.tree.spatial.rstarvariants.deliclu - package elki.index.tree.spatial.rstarvariants.deliclu: DeLiCluTree.
elki.index.tree.spatial.rstarvariants.flat - package elki.index.tree.spatial.rstarvariants.flat: FlatRStarTree.
elki.index.tree.spatial.rstarvariants.query - package elki.index.tree.spatial.rstarvariants.query: Queries on the R-Tree family of indexes: kNN and range queries.
elki.index.tree.spatial.rstarvariants.rdknn - package elki.index.tree.spatial.rstarvariants.rdknn: RdKNNTree.
elki.index.tree.spatial.rstarvariants.rstar - package elki.index.tree.spatial.rstarvariants.rstar: RStarTree.
elki.index.tree.spatial.rstarvariants.strategies.bulk - package elki.index.tree.spatial.rstarvariants.strategies.bulk: Packages for bulk-loading R*-trees.
elki.index.tree.spatial.rstarvariants.strategies.insert - package elki.index.tree.spatial.rstarvariants.strategies.insert: Insertion strategies for R-trees.
elki.index.tree.spatial.rstarvariants.strategies.overflow - package elki.index.tree.spatial.rstarvariants.strategies.overflow: Overflow treatment strategies for R-trees.
elki.index.tree.spatial.rstarvariants.strategies.reinsert - package elki.index.tree.spatial.rstarvariants.strategies.reinsert: Reinsertion strategies for R-trees.
elki.index.tree.spatial.rstarvariants.strategies.split - package elki.index.tree.spatial.rstarvariants.strategies.split: Splitting strategies for R-trees.
elki.index.tree.spatial.rstarvariants.util - package elki.index.tree.spatial.rstarvariants.util: Utilities for R*-tree and variants.
elki.index.vafile - package elki.index.vafile: Vector Approximation File.
elki.itemsetmining - package elki.itemsetmining: Algorithms for frequent itemset mining such as APRIORI.
elki.itemsetmining.associationrules - package elki.itemsetmining.associationrules: Association rule mining.
elki.itemsetmining.associationrules.interest - package elki.itemsetmining.associationrules.interest: Association rule interestingness measures.
elki.logging - package elki.logging: Logging facility for controlling logging behavior of the complete framework.
elki.logging.progress - package elki.logging.progress: Progress status objects (for UI).
elki.logging.statistics - package elki.logging.statistics: Classes for logging various statistics.
elki.math - package elki.math: Mathematical operations and utilities used throughout the framework.
elki.math.geodesy - package elki.math.geodesy: Functions for computing on the sphere / earth.
elki.math.geometry - package elki.math.geometry: Algorithms from computational geometry.
elki.math.linearalgebra - package elki.math.linearalgebra: The linear algebra package provides classes and computational methods for operations on matrices and vectors.
elki.math.linearalgebra.fitting - package elki.math.linearalgebra.fitting: Function to numerically fit a function (such as a Gaussian distribution) to given data.
elki.math.linearalgebra.pca - package elki.math.linearalgebra.pca: Principal Component Analysis (PCA) and eigenvector processing.
elki.math.linearalgebra.pca.filter - package elki.math.linearalgebra.pca.filter: Filter eigenvectors based on their eigenvalues.
elki.math.linearalgebra.pca.weightfunctions - package elki.math.linearalgebra.pca.weightfunctions: Weight functions used in weighted PCA via WeightedCovarianceMatrixBuilder.
elki.math.scales - package elki.math.scales: Scales handling for plotting.
elki.math.spacefillingcurves - package elki.math.spacefillingcurves: Space filling curves.
elki.math.statistics - package elki.math.statistics: Statistical tests and methods.
elki.math.statistics.dependence - package elki.math.statistics.dependence: Statistical measures of dependence, such as correlation.
elki.math.statistics.dependence.mcde - package elki.math.statistics.dependence.mcde: Tests tailored to be used with MCDEDependence.
elki.math.statistics.distribution - package elki.math.statistics.distribution: Standard distributions, with random generation functionalities.
elki.math.statistics.distribution.estimator - package elki.math.statistics.distribution.estimator: Estimators for statistical distributions.
elki.math.statistics.distribution.estimator.meta - package elki.math.statistics.distribution.estimator.meta: Meta estimators: estimators that do not actually estimate themselves, but instead use other estimators, e.g., on a trimmed data set, or as an ensemble.
elki.math.statistics.intrinsicdimensionality - package elki.math.statistics.intrinsicdimensionality: Methods for estimating the intrinsic dimensionality.
elki.math.statistics.kernelfunctions - package elki.math.statistics.kernelfunctions: Kernel functions from statistics.
elki.math.statistics.tests - package elki.math.statistics.tests: Statistical tests.
elki.outlier - package elki.outlier: Outlier detection algorithms.
elki.outlier.anglebased - package elki.outlier.anglebased: Angle-based outlier detection algorithms.
elki.outlier.clustering - package elki.outlier.clustering: Clustering based outlier detection.
elki.outlier.density - package elki.outlier.density: Density-based outlier detection algorithms.
elki.outlier.distance - package elki.outlier.distance: Distance-based outlier detection algorithms, such as DBOutlier and kNN.
elki.outlier.distance.parallel - package elki.outlier.distance.parallel: Parallel implementations of distance-based outlier detectors.
elki.outlier.intrinsic - package elki.outlier.intrinsic: Outlier detection algorithms based on intrinsic dimensionality.
elki.outlier.lof - package elki.outlier.lof: LOF family of outlier detection algorithms.
elki.outlier.lof.parallel - package elki.outlier.lof.parallel: Parallelized variants of LOF.
elki.outlier.meta - package elki.outlier.meta: Meta outlier detection algorithms: external scores, score rescaling.
elki.outlier.spatial - package elki.outlier.spatial: Spatial outlier detection algorithms.
elki.outlier.spatial.neighborhood - package elki.outlier.spatial.neighborhood: Spatial outlier neighborhood classes.
elki.outlier.spatial.neighborhood.weighted - package elki.outlier.spatial.neighborhood.weighted: Weighted neighborhood definitions.
elki.outlier.subspace - package elki.outlier.subspace: Subspace outlier detection methods.
elki.outlier.svm - package elki.outlier.svm: Support-Vector-Machines for outlier detection.
elki.outlier.trivial - package elki.outlier.trivial: Trivial outlier detection algorithms: no outliers, all outliers, label outliers.
elki.parallel - package elki.parallel: Parallel processing core for ELKI.
elki.parallel.processor - package elki.parallel.processor: Processor API of ELKI, and some essential shared processors.
elki.parallel.variables - package elki.parallel.variables: Variables are instantiated for each thread, and allow passing values from one processor to another within the same thread.
elki.persistent - package elki.persistent: Persistent data management.
elki.projection - package elki.projection: Data projections (see also preprocessing filters for basic projections).
elki.result - package elki.result: Result types, representation and handling.
elki.result.outlier - package elki.result.outlier: Outlier result classes.
elki.result.textwriter - package elki.result.textwriter: Text serialization (CSV, Gnuplot, Console, ...).
elki.result.textwriter.naming - package elki.result.textwriter.naming: Naming schemes for clusters (for output when an algorithm does not generate cluster names).
elki.result.textwriter.writers - package elki.result.textwriter.writers: Serialization handlers for individual data types.
elki.similarity - package elki.similarity: Similarity functions.
elki.similarity.cluster - package elki.similarity.cluster: Similarity measures for comparing clusters.
elki.similarity.kernel - package elki.similarity.kernel: Kernel functions.
elki.svm - package elki.svm
elki.svm.data - package elki.svm.data
elki.svm.model - package elki.svm.model
elki.svm.qmatrix - package elki.svm.qmatrix
elki.svm.solver - package elki.svm.solver
elki.timeseries - package elki.timeseries: Algorithms for change point detection in time series.
elki.utilities - package elki.utilities: Utility and helper classes - commonly used data structures, output formatting, exceptions, ...
elki.utilities.datastructures - package elki.utilities.datastructures: Basic memory structures such as heaps and object hierarchies.
elki.utilities.datastructures.arraylike - package elki.utilities.datastructures.arraylike: Common API for accessing objects that are "array-like", including lists, numerical vectors, database vectors and arrays.
elki.utilities.datastructures.arrays - package elki.utilities.datastructures.arrays: Utilities for arrays: advanced sorting for primitive arrays.
elki.utilities.datastructures.heap - package elki.utilities.datastructures.heap: Heap structures and variations such as bounded priority heaps.
elki.utilities.datastructures.hierarchy - package elki.utilities.datastructures.hierarchy: Delegate implementation of a hierarchy.
elki.utilities.datastructures.histogram - package elki.utilities.datastructures.histogram: Classes for computing histograms.
elki.utilities.datastructures.iterator - package elki.utilities.datastructures.iterator: ELKI Iterator API.
elki.utilities.datastructures.range - package elki.utilities.datastructures.range: Ranges of values.
elki.utilities.datastructures.unionfind - package elki.utilities.datastructures.unionfind: Union-find data structures.
elki.utilities.documentation - package elki.utilities.documentation: Documentation utilities: Annotations for Title, Description, Reference.
elki.utilities.ensemble - package elki.utilities.ensemble: Utility classes for simple ensembles.
elki.utilities.exceptions - package elki.utilities.exceptions: Exception classes and common exception messages.
elki.utilities.io - package elki.utilities.io: Utility classes for input/output.
elki.utilities.optionhandling - package elki.utilities.optionhandling: Parameter handling and option descriptions.
elki.utilities.optionhandling.constraints - package elki.utilities.optionhandling.constraints: Constraints allow to restrict possible values for parameters.
elki.utilities.optionhandling.parameterization - package elki.utilities.optionhandling.parameterization: Configuration managers.
elki.utilities.optionhandling.parameters - package elki.utilities.optionhandling.parameters: Classes for various typed parameters.
elki.utilities.pairs - package elki.utilities.pairs: Pairs utility classes.
elki.utilities.random - package elki.utilities.random: Random number generation.
elki.utilities.referencepoints - package elki.utilities.referencepoints: Package containing strategies to obtain reference points.
elki.utilities.scaling - package elki.utilities.scaling: Scaling functions: linear, logarithmic, gamma, clipping, ...
elki.utilities.scaling.outlier - package elki.utilities.scaling.outlier: Scaling of outlier scores, that require a statistical analysis of the occurring values.
elki.utilities.xml - package elki.utilities.xml: XML and XHTML utilities.
elki.visualization - package elki.visualization: Visualization package of ELKI.
elki.visualization.batikutil - package elki.visualization.batikutil: Commonly used functionality useful for Apache Batik.
elki.visualization.colors - package elki.visualization.colors: Color scheme handling for ELKI visualization.
elki.visualization.css - package elki.visualization.css: Managing CSS styles / classes.
elki.visualization.gui - package elki.visualization.gui: Package to provide a visualization GUI.
elki.visualization.gui.detail - package elki.visualization.gui.detail: Classes for managing a detail view.
elki.visualization.gui.overview - package elki.visualization.gui.overview: Classes for managing the overview plot.
elki.visualization.opticsplot - package elki.visualization.opticsplot: Code for drawing OPTICS plots.
elki.visualization.parallel3d - package elki.visualization.parallel3d: 3DPC: 3D parallel coordinate plot visualization for ELKI.
elki.visualization.parallel3d.layout - package elki.visualization.parallel3d.layout: Layouting algorithms for 3D parallel coordinate plots.
elki.visualization.parallel3d.util - package elki.visualization.parallel3d.util: Utility classes (primarily rendering utilities).
elki.visualization.projections - package elki.visualization.projections: Visualization projections.
elki.visualization.projector - package elki.visualization.projector: Projectors are responsible for finding appropriate projections for data relations.
elki.visualization.savedialog - package elki.visualization.savedialog: Save dialog for SVG plots.
elki.visualization.silhouette - package elki.visualization.silhouette: Code for drawing silhouette plots.
elki.visualization.style - package elki.visualization.style: Style management for ELKI visualizations.
elki.visualization.style.lines - package elki.visualization.style.lines: Generate line styles for plotting in CSS.
elki.visualization.style.marker - package elki.visualization.style.marker: Draw plot markers.
elki.visualization.svg - package elki.visualization.svg: Base SVG functionality (generation, markers, thumbnails, export, ...).
elki.visualization.visualizers - package elki.visualization.visualizers: Visualizers for various results.
elki.visualization.visualizers.actions - package elki.visualization.visualizers.actions: Action-only "visualizers" that only produce menu entries.
elki.visualization.visualizers.histogram - package elki.visualization.visualizers.histogram: Visualizers based on 1D projected histograms.
elki.visualization.visualizers.optics - package elki.visualization.visualizers.optics: Visualizers that do work on OPTICS plots.
elki.visualization.visualizers.pairsegments - package elki.visualization.visualizers.pairsegments: Visualizers for inspecting cluster differences using pair counting segments.
elki.visualization.visualizers.parallel - package elki.visualization.visualizers.parallel: Visualizers based on parallel coordinates.
elki.visualization.visualizers.parallel.cluster - package elki.visualization.visualizers.parallel.cluster: Visualizers for clustering results based on parallel coordinates.
elki.visualization.visualizers.parallel.index - package elki.visualization.visualizers.parallel.index: Visualizers for index structure based on parallel coordinates.
elki.visualization.visualizers.parallel.selection - package elki.visualization.visualizers.parallel.selection: Visualizers for object selection based on parallel projections.
elki.visualization.visualizers.scatterplot - package elki.visualization.visualizers.scatterplot: Visualizers based on scatterplots.
elki.visualization.visualizers.scatterplot.cluster - package elki.visualization.visualizers.scatterplot.cluster: Visualizers for clustering results based on 2D projections.
elki.visualization.visualizers.scatterplot.density - package elki.visualization.visualizers.scatterplot.density: Visualizers for data set density in a scatterplot projection.
elki.visualization.visualizers.scatterplot.index - package elki.visualization.visualizers.scatterplot.index: Visualizers for index structures based on 2D projections.
elki.visualization.visualizers.scatterplot.outlier - package elki.visualization.visualizers.scatterplot.outlier: Visualizers for outlier scores based on 2D projections.
elki.visualization.visualizers.scatterplot.selection - package elki.visualization.visualizers.scatterplot.selection: Visualizers for object selection based on 2D projections.
elki.visualization.visualizers.scatterplot.uncertain - package elki.visualization.visualizers.scatterplot.uncertain: Visualizers for uncertain data.
elki.visualization.visualizers.silhouette - package elki.visualization.visualizers.silhouette: Visualizers that do work on Silhouette plots.
elki.visualization.visualizers.thumbs - package elki.visualization.visualizers.thumbs: Thumbnail "Visualizers" (that take care of refreshing thumbnails).
elki.visualization.visualizers.visunproj - package elki.visualization.visualizers.visunproj: Visualizers that do not use a particular projection.
elki.workflow - package elki.workflow: Work flow packages, e.g., following the usual KDD model.
ELKIBuilder<T> - Class in elki.utilities: Builder utility class.
ELKIBuilder(Class<? super T>) - Constructor for class elki.utilities.ELKIBuilder: Constructor.
ELKILauncher - Class in elki.application: Class to launch ELKI.
ELKILauncher() - Constructor for class elki.application.ELKILauncher: Private constructor.
ELKILogRecord - Class in elki.logging: Base LogRecord class used in ELKI.
ELKILogRecord(Level, CharSequence) - Constructor for class elki.logging.ELKILogRecord: Constructor.
ELKIServiceLoader - Class in elki.utilities: Class that emulates the behavior of an java ServiceLoader, except that the classes are not automatically instantiated.
ELKIServiceLoader() - Constructor for class elki.utilities.ELKIServiceLoader: Constructor - do not use.
ELKIServiceRegistry - Class in elki.utilities: Registry of available implementations in ELKI.
ELKIServiceRegistry() - Constructor for class elki.utilities.ELKIServiceRegistry: Do not use constructor.
ELKIServiceRegistry.Entry - Class in elki.utilities: Entry in the service registry.
ELKIServiceScanner - Class in elki.utilities: A collection of inspection-related utility functions.
ELKIServiceScanner() - Constructor for class elki.utilities.ELKIServiceScanner: Static methods only.
ELKIServiceScanner.DirClassIterator - Class in elki.utilities: Class to iterate over a directory tree.
ellipsoidVincentyFormulaDeg(double, double, double, double, double) - Static method in class elki.math.geodesy.SphereUtil: Compute the approximate great-circle distance of two points.
ellipsoidVincentyFormulaRad(double, double, double, double, double) - Static method in class elki.math.geodesy.SphereUtil: Compute the approximate great-circle distance of two points.
ellipticalArc(double[], double, double, double, double[]) - Method in class elki.visualization.svg.SVGPath: Elliptical arc curve to the given coordinates.
ellipticalArc(double, double, double, double, double, double[]) - Method in class elki.visualization.svg.SVGPath: Elliptical arc curve to the given coordinates.
ellipticalArc(double, double, double, double, double, double, double) - Method in class elki.visualization.svg.SVGPath: Elliptical arc curve to the given coordinates.
EM<O,M extends MeanModel> - Class in elki.clustering.em: Clustering by expectation maximization (EM-Algorithm), also known as Gaussian Mixture Modeling (GMM), with optional MAP regularization.
EM(int, double, EMClusterModelFactory<? super O, M>) - Constructor for class elki.clustering.em.EM: Constructor.
EM(int, double, EMClusterModelFactory<? super O, M>, int, boolean) - Constructor for class elki.clustering.em.EM: Constructor.
EM(int, double, EMClusterModelFactory<? super O, M>, int, double, boolean) - Constructor for class elki.clustering.em.EM: Constructor.
EM(int, double, EMClusterModelFactory<? super O, M>, int, int, double, boolean) - Constructor for class elki.clustering.em.EM: Constructor.
EM_DELTA_ID - Static variable in class elki.clustering.subspace.P3C.Par: Threshold when to stop EM iterations.
EM.Par<O,M extends MeanModel> - Class in elki.clustering.em: Parameterization class.
embedOrThumbnail(int, PlotItem, VisualizationTask, Element) - Method in class elki.visualization.gui.overview.OverviewPlot: Produce thumbnail for a visualizer.
EMBLEM_IMPORTANT - Static variable in class elki.gui.icons.StockIcon
EMBORDER - Static variable in class elki.visualization.visualizers.scatterplot.cluster.EMClusterVisualization.Instance: Generic tags to indicate the type of element.
EMClusterModel<O,M extends Model> - Interface in elki.clustering.em.models: Models usable in EM clustering.
EMClusterModelFactory<O,M extends Model> - Interface in elki.clustering.em.models: Factory for initializing the EM models.
EMClusterVisualization - Class in elki.visualization.visualizers.scatterplot.cluster: Visualizer for generating SVG-Elements containing ellipses for first, second and third standard deviation.
EMClusterVisualization() - Constructor for class elki.visualization.visualizers.scatterplot.cluster.EMClusterVisualization: Constructor
EMClusterVisualization.Instance - Class in elki.visualization.visualizers.scatterplot.cluster: Instance.
emDelta - Variable in class elki.clustering.subspace.P3C: Threshold when to stop EM iterations.
emDelta - Variable in class elki.clustering.subspace.P3C.Par: Threshold when to stop EM iterations.
EMGOlivierNorbergEstimator - Class in elki.math.statistics.distribution.estimator: Naive distribution estimation using mean and sample variance.
EMGOlivierNorbergEstimator() - Constructor for class elki.math.statistics.distribution.estimator.EMGOlivierNorbergEstimator: Private constructor, use static instance!
EMGOlivierNorbergEstimator.Par - Class in elki.math.statistics.distribution.estimator: Parameterization class.
EMModel - Class in elki.data.model: Cluster model of an EM cluster, providing a mean and a full covariance Matrix.
EMModel(double[], double[][]) - Constructor for class elki.data.model.EMModel: Constructor.
EMOutlier<V extends NumberVector> - Class in elki.outlier.clustering: Outlier detection algorithm using EM Clustering.
EMOutlier(int, double, EMClusterModelFactory<? super V, ?>, int, int, double) - Constructor for class elki.outlier.clustering.EMOutlier: Constructor.
EMOutlier.Par<V extends NumberVector> - Class in elki.outlier.clustering: Parameterization class.
EmpiricalQueryOptimizer - Class in elki.database.query: Class to automatically add indexes to a database.
EmpiricalQueryOptimizer() - Constructor for class elki.database.query.EmpiricalQueryOptimizer: Constructor.
empty() - Static method in class elki.utilities.datastructures.iterator.EmptyIterator: Get an empty hierarchy iterator.
EMPTY - Static variable in interface elki.database.ids.DoubleDBIDListIter: Static empty iterator.
EMPTY - Static variable in class elki.datasource.parser.ArffParser: Empty line pattern.
EMPTY_ALIASES - Static variable in class elki.utilities.ELKIServiceRegistry.Entry: Reusable empty array.
EMPTY_ARRAY - Static variable in class elki.datasource.filter.normalization.columnwise.AttributeWiseMinMaxNormalization: Empty double array.
EMPTY_CHILDREN - Static variable in class elki.itemsetmining.FPGrowth.FPNode: Constant for leaf nodes.
EMPTY_CHILDREN - Static variable in class elki.result.Metadata: Empty list.
EMPTY_DISTS - Static variable in class elki.database.ids.integer.DoubleIntegerDBIDArrayList: Empty.
EMPTY_ENUMERATION - Variable in class elki.index.tree.BreadthFirstEnumeration: Represents an empty enumeration.
EMPTY_IDS - Static variable in class elki.database.ids.integer.DoubleIntegerDBIDArrayList: Empty.
EMPTY_INTS - Static variable in class elki.math.MathUtil: Empty integer array.
EMPTY_ITERATOR - Static variable in class elki.database.ids.EmptyDBIDs: Empty DBID iterator.
EMPTY_LABELS - Static variable in class elki.data.LabelList: Empty label list.
EMPTY_PAGE - Static variable in class elki.persistent.OnDiskArrayPageFile: Indicates an empty page.
EMPTY_PAGE - Static variable in class elki.persistent.PersistentPageFile: Indicates an empty page.
EMPTY_PARENTS - Static variable in class elki.result.Metadata: Empty list.
EMPTY_STRING - Static variable in class elki.utilities.io.ParseUtil: Preallocated exceptions.
EMPTY_VECTOR - Static variable in class elki.data.model.CorrelationAnalysisSolution: Empty constant vector returned when no subspace was used.
EmptyDatabaseConnection - Class in elki.datasource: Pseudo database that is empty.
EmptyDatabaseConnection() - Constructor for class elki.datasource.EmptyDatabaseConnection: Constructor.
EmptyDataException - Exception in elki.utilities.exceptions: Exception thrown when a database / relation is empty.
EmptyDataException() - Constructor for exception elki.utilities.exceptions.EmptyDataException: Constructor.
EmptyDBIDIterator() - Constructor for class elki.database.ids.EmptyDBIDs.EmptyDBIDIterator
EmptyDBIDs - Class in elki.database.ids: Empty DBID collection.
EmptyDBIDs() - Constructor for class elki.database.ids.EmptyDBIDs: Constructor.
EMPTYDBIDS - Static variable in class elki.database.ids.DBIDUtil: Final, global copy of empty DBIDs.
EmptyDBIDs.EmptyDBIDIterator - Class in elki.database.ids: Iterator for empty DBIDs-
EmptyIterator<O> - Class in elki.utilities.datastructures.iterator: Empty object iterator.
EmptyIterator() - Constructor for class elki.utilities.datastructures.iterator.EmptyIterator: Private constructor, use static EmptyIterator.empty() instead.
emptyPages - Variable in class elki.persistent.AbstractStoringPageFile: A stack holding the empty page ids.
emptyPagesSize - Variable in class elki.index.tree.TreeIndexHeader: The number of bytes additionally needed for the listing of empty pages of the headed page file.
EmptyParameterization - Class in elki.utilities.optionhandling.parameterization: Parameterization handler that only allows the use of default values.
EmptyParameterization() - Constructor for class elki.utilities.optionhandling.parameterization.EmptyParameterization
enabled() - Method in interface elki.visualization.VisualizationMenuAction: Indicate if the menu option is enabled or greyed out.
enabled() - Method in interface elki.visualization.VisualizationMenuToggle: Indicate if the menu option is enabled or greyed out.
enabled() - Method in class elki.visualization.visualizers.actions.ClusterStyleAction.SetStyleAction
enableExport(boolean) - Method in class elki.visualization.gui.ResultWindow.DynamicMenu: Enable / disable the export menu.
enableOverview(boolean) - Method in class elki.visualization.gui.ResultWindow.DynamicMenu: Enable / disable the overview menu.
enableStart() - Method in class elki.visualization.batikutil.DragableArea: Enable capturing of 'mousedown' events.
enableStop() - Method in class elki.visualization.batikutil.DragableArea: Enable capturing of 'mousemove' and 'mouseup' events.
ENABLEVIS_ID - Static variable in class elki.visualization.VisualizerParameterizer.Par: Parameter to enable visualizers
enableVisualizers - Variable in class elki.visualization.VisualizerParameterizer.Par: Pattern to enable visualizers
enableWriter(boolean) - Method in class elki.visualization.gui.ResultWindow.DynamicMenu: Enable / disable the writer menu.
encoder - Variable in class elki.utilities.io.ByteArrayUtil.StringSerializer: Encoder.
end - Variable in class elki.clustering.hierarchical.AGNES.Instance: Active set size
end - Variable in class elki.clustering.kmeans.KDTreePruningKMeans.KDNode: End index of child nodes (exclusive).
end - Variable in class elki.database.ids.integer.ArrayModifiableIntegerDBIDs.Slice: Slice positions.
end - Variable in class elki.database.ids.integer.ArrayStaticIntegerDBIDs.Slice: Slice positions.
end - Variable in class elki.database.ids.integer.DoubleIntegerDBIDSubList: End offset.
end - Variable in class elki.database.ids.integer.IntegerDBIDPair.Slice: Slice positions.
end - Variable in class elki.logging.statistics.MillisTimeDuration: Tracking variables.
end - Variable in class elki.logging.statistics.NanoDuration: Tracking variables.
end - Variable in class elki.outlier.density.HySortOD.TreeStrategy.Node: Index information.
end - Variable in class elki.parallel.ParallelExecutor.BlockArrayRunner: End position
end - Variable in class elki.utilities.datastructures.range.ExponentialIntGenerator: End value.
end - Variable in class elki.utilities.io.LineReader: Current position, and length of buffer
end - Variable in class elki.utilities.io.Tokenizer: Current positions of result and iterator.
end() - Method in interface elki.logging.statistics.Duration: Finish the timer.
end() - Method in class elki.logging.statistics.MillisTimeDuration
end() - Method in class elki.logging.statistics.NanoDuration
END_OF_STREAM - elki.datasource.bundle.BundleStreamSource.Event
END_VALUE - Static variable in class elki.utilities.datastructures.iterator.IterableIt: End sentinel value.
endcg - Variable in class elki.application.greedyensemble.EvaluatePrecomputedOutlierScores: Normalization term E[NDCG].
endDrag(SVGPoint, SVGPoint, Event, boolean) - Method in interface elki.visualization.batikutil.DragableArea.DragListener: Method called when a drag was ended.
endDrag(SVGPoint, SVGPoint, Event, boolean) - Method in class elki.visualization.batikutil.DragableArea: Method called when a drag was ended.
endDrag(SVGPoint, SVGPoint, Event, boolean) - Method in class elki.visualization.visualizers.optics.OPTICSPlotCutVisualization.Instance
endDrag(SVGPoint, SVGPoint, Event, boolean) - Method in class elki.visualization.visualizers.optics.OPTICSPlotSelectionVisualization.Instance
endDrag(SVGPoint, SVGPoint, Event, boolean) - Method in class elki.visualization.visualizers.parallel.selection.SelectionToolAxisRangeVisualization.Instance
endDrag(SVGPoint, SVGPoint, Event, boolean) - Method in class elki.visualization.visualizers.parallel.selection.SelectionToolLineVisualization.Instance
endDrag(SVGPoint, SVGPoint, Event, boolean) - Method in class elki.visualization.visualizers.scatterplot.selection.MoveObjectsToolVisualization.Instance
endDrag(SVGPoint, SVGPoint, Event, boolean) - Method in class elki.visualization.visualizers.scatterplot.selection.SelectionToolCubeVisualization.Instance
endDrag(SVGPoint, SVGPoint, Event, boolean) - Method in class elki.visualization.visualizers.scatterplot.selection.SelectionToolDotVisualization.Instance
endDrag(SVGPoint, SVGPoint, Event, boolean) - Method in class elki.visualization.visualizers.silhouette.SilhouettePlotSelectionToolVisualization.Instance
endindex - Variable in class elki.clustering.optics.OPTICSXi.SteepArea: End index of steep area
endIndex - Variable in class elki.data.model.OPTICSModel: End index
ENDLABEL - elki.visualization.svg.SVGSimpleLinearAxis.LabelStyle
endsWith(CharSequence, CharSequence) - Static method in class elki.utilities.io.FormatUtil: Similar to String.endsWith(String) but for buffers.
endvec - Variable in class elki.visualization.parallel3d.util.Arcball1DOFAdapter: Ending point of drag.
enlargement(SpatialComparable, SpatialComparable) - Static method in class elki.data.spatial.SpatialUtil: Compute the enlargement obtained by adding an object to an existing object.
EnsembleEstimator - Class in elki.math.statistics.intrinsicdimensionality: Ensemble estimator taking the median of three of our best estimators.
EnsembleEstimator() - Constructor for class elki.math.statistics.intrinsicdimensionality.EnsembleEstimator
EnsembleVoting - Interface in elki.utilities.ensemble: Interface for ensemble voting rules
EnsembleVotingInverseMultiplicative - Class in elki.utilities.ensemble: Inverse multiplicative voting: \( 1-\prod_i(1-s_i) \)
EnsembleVotingInverseMultiplicative() - Constructor for class elki.utilities.ensemble.EnsembleVotingInverseMultiplicative: Constructor.
EnsembleVotingInverseMultiplicative.Par - Class in elki.utilities.ensemble: Parameterization class.
EnsembleVotingMax - Class in elki.utilities.ensemble: Simple combination rule, by taking the maximum.
EnsembleVotingMax() - Constructor for class elki.utilities.ensemble.EnsembleVotingMax: Constructor.
EnsembleVotingMean - Class in elki.utilities.ensemble: Simple combination rule, by taking the mean
EnsembleVotingMean() - Constructor for class elki.utilities.ensemble.EnsembleVotingMean: Constructor.
EnsembleVotingMedian - Class in elki.utilities.ensemble: Simple combination rule, by taking the median.
EnsembleVotingMedian(double) - Constructor for class elki.utilities.ensemble.EnsembleVotingMedian: Constructor.
EnsembleVotingMedian.Par - Class in elki.utilities.ensemble: Parameterization class.
EnsembleVotingMin - Class in elki.utilities.ensemble: Simple combination rule, by taking the minimum.
EnsembleVotingMin() - Constructor for class elki.utilities.ensemble.EnsembleVotingMin: Constructor.
EnsembleVotingMultiplicative - Class in elki.utilities.ensemble: Inverse multiplicative voting: \( \prod_i s_i \)
EnsembleVotingMultiplicative() - Constructor for class elki.utilities.ensemble.EnsembleVotingMultiplicative: Constructor.
EnsembleVotingMultiplicative.Par - Class in elki.utilities.ensemble: Parameterization class.
ensureArray(DBIDs) - Static method in class elki.database.ids.DBIDUtil: Ensure that the given DBIDs are array-indexable.
ensureBuffer(int, ByteBuffer, WritableByteChannel) - Method in class elki.datasource.bundle.BundleWriter: Ensure the buffer is large enough.
ensureClusteringResult(Database) - Static method in class elki.evaluation.AutomaticEvaluation: Ensure that the result contains at least one Clustering.
ensureCompleted(Logging) - Method in class elki.logging.progress.FiniteProgress: Ensure that the progress was completed, to make progress bars disappear
ensureCompleted(FiniteProgress) - Method in class elki.logging.Logging: Increment a progress (unless null).
ensureModifiable(DBIDs) - Static method in class elki.database.ids.DBIDUtil: Ensure modifiable.
ensureSelectionResult(Database) - Static method in class elki.result.SelectionResult: Ensure that there also is a selection container object.
ensureSet(DBIDs) - Static method in class elki.database.ids.DBIDUtil: Ensure that the given DBIDs support fast "contains" operations.
ensureSize() - Method in class elki.itemsetmining.FPGrowth.FPNode: Ensure we have enough storage.
ensureSize(int) - Method in class elki.database.ids.integer.ArrayModifiableIntegerDBIDs: Resize as desired.
ensureSize(int) - Method in class elki.persistent.OnDiskArray: Ensure that the file can fit the given number of records.
entries - Variable in class elki.index.tree.AbstractNode: The entries (children) of this node.
entries - Variable in class elki.index.tree.spatial.rstarvariants.strategies.split.TopologicalSplitter.Split: The entries we process.
entropy - Variable in class elki.evaluation.clustering.ClusterContingencyTable: Entropy-based measures
Entropy - Class in elki.evaluation.clustering: Entropy based measures, implemented using natural logarithms.
Entropy(ClusterContingencyTable) - Constructor for class elki.evaluation.clustering.Entropy: Constructor.
entropyFirst - Variable in class elki.evaluation.clustering.Entropy: Entropy in first
entropyFirst() - Method in class elki.evaluation.clustering.Entropy: Get the entropy of the first clustering (not normalized, 0 = equal).
entropyJoint - Variable in class elki.evaluation.clustering.Entropy: Joint entropy
entropyJoint() - Method in class elki.evaluation.clustering.Entropy: Get the joint entropy of both clusterings (not normalized, 0 = equal).
entropyPowers() - Method in class elki.evaluation.clustering.Entropy: Get Powers entropy (normalized, 0 = equal) Powers = 1 - NMI_Sum
entropySecond - Variable in class elki.evaluation.clustering.Entropy: Entropy in second
entropySecond() - Method in class elki.evaluation.clustering.Entropy: Get the entropy of the second clustering (not normalized, 0 = equal).
entry - Variable in class elki.index.tree.IndexTreePath: The entry of this component.
entry - Variable in class elki.index.tree.metrical.mtreevariants.strategies.split.distribution.DistanceEntry: The entry of the Index.
Entry() - Constructor for class elki.utilities.ELKIServiceRegistry.Entry
entry1 - Variable in class elki.clustering.optics.DeLiClu.SpatialObjectPair: The first entry of this pair.
entry2 - Variable in class elki.clustering.optics.DeLiClu.SpatialObjectPair: The second entry of this pair.
entrySet() - Method in class elki.visualization.gui.overview.RectangleArranger: The items contained in the map.
enumClass - Variable in class elki.utilities.optionhandling.parameters.EnumParameter: Reference to the actual enum type, for T.valueOf().
EnumParameter<E extends java.lang.Enum<E>> - Class in elki.utilities.optionhandling.parameters: Parameter class for a parameter specifying an enum type.
EnumParameter(OptionID, Class<E>) - Constructor for class elki.utilities.optionhandling.parameters.EnumParameter: Constructs an enum parameter with the given optionID, constraints and default value.
EnumParameter(OptionID, Class<E>, E) - Constructor for class elki.utilities.optionhandling.parameters.EnumParameter: Constructs an enum parameter with the given optionID, constraints and default value.
EnumParameterConfigurator - Class in elki.gui.configurator: Panel to configure EnumParameters by offering a dropdown to choose from.
EnumParameterConfigurator(EnumParameter<?>, JComponent) - Constructor for class elki.gui.configurator.EnumParameterConfigurator
EpanechnikovKernelDensityFunction - Class in elki.math.statistics.kernelfunctions: Epanechnikov kernel density estimator.
EpanechnikovKernelDensityFunction() - Constructor for class elki.math.statistics.kernelfunctions.EpanechnikovKernelDensityFunction: Private, empty constructor.
EpanechnikovKernelDensityFunction.Par - Class in elki.math.statistics.kernelfunctions: Parameterization stub.
eps - Variable in class elki.outlier.subspace.OUTRES: The epsilon (in 2d) parameter
eps - Variable in class elki.outlier.subspace.OUTRES.Par: Query radius
eps - Variable in class elki.svm.AbstractSingleSVM
eps - Variable in class elki.svm.solver.Solver
EPS - elki.result.ExportVisualizations.Format
epsilon - Variable in class elki.clustering.correlation.COPAC.Settings: Epsilon value for GDBSCAN.
epsilon - Variable in class elki.clustering.correlation.FourC.Settings: Query radius epsilon.
epsilon - Variable in class elki.clustering.dbscan.DBSCAN: Holds the epsilon radius threshold.
epsilon - Variable in class elki.clustering.dbscan.DBSCAN.Par: Holds the epsilon radius threshold.
epsilon - Variable in class elki.clustering.dbscan.GriDBSCAN: Holds the epsilon radius threshold.
epsilon - Variable in class elki.clustering.dbscan.GriDBSCAN.Instance: Holds the epsilon radius threshold.
epsilon - Variable in class elki.clustering.dbscan.predicates.AbstractRangeQueryNeighborPredicate: Range to query with.
epsilon - Variable in class elki.clustering.dbscan.predicates.EpsilonNeighborPredicate: Range to query with
epsilon - Variable in class elki.clustering.dbscan.predicates.EpsilonNeighborPredicate.Instance: Range to query with
epsilon - Variable in class elki.clustering.dbscan.predicates.SimilarityNeighborPredicate: Range to query with
epsilon - Variable in class elki.clustering.dbscan.predicates.SimilarityNeighborPredicate.Instance: Range to query with
epsilon - Variable in class elki.clustering.optics.AbstractOPTICS: Holds the maximum distance to search for objects (performance parameter)
epsilon - Variable in class elki.clustering.SNNClustering: Epsilon radius threshold.
epsilon - Variable in class elki.clustering.subspace.DiSH: Holds the value of DiSH.Par.EPSILON_ID.
epsilon - Variable in class elki.clustering.subspace.DiSH.Par: The epsilon value for each dimension.
epsilon - Variable in class elki.clustering.subspace.PreDeCon.Settings: Query radius parameter epsilon.
epsilon - Variable in class elki.clustering.subspace.SUBCLU: Maximum radius of the neighborhood to be considered.
epsilon - Variable in class elki.clustering.subspace.SUBCLU.Par: Maximum radius of the neighborhood to be considered.
epsilon - Variable in class elki.clustering.uncertain.FDBSCAN.Par: Epsilon radius
epsilon - Variable in class elki.clustering.uncertain.FDBSCANNeighborPredicate: Epsilon radius
epsilon - Variable in class elki.clustering.uncertain.FDBSCANNeighborPredicate.Instance: The epsilon distance a neighbor may have at most.
epsilon - Variable in class elki.clustering.uncertain.FDBSCANNeighborPredicate.Par: Epsilon radius
epsilon - Variable in class elki.visualization.visualizers.optics.OPTICSPlotCutVisualization.Instance: The current epsilon value.
EPSILON_ID - Static variable in class elki.clustering.dbscan.DBSCAN.Par: Parameter to specify the maximum radius of the neighborhood to be considered, must be suitable to the distance function specified.
EPSILON_ID - Static variable in class elki.clustering.subspace.DiSH.Par: A comma separated list of positive doubles specifying the maximum radius of the neighborhood to be considered in each dimension for determination of the preference vector (default is DiSH.Par.DEFAULT_EPSILON in each dimension).
EPSILON_ID - Static variable in class elki.clustering.subspace.HiSC.Par: Parameter to specify the maximum distance between two vectors with equal preference vectors before considering them as parallel, must be a double equal to or greater than 0.
EPSILON_ID - Static variable in class elki.clustering.subspace.SUBCLU.Par: Maximum radius of the neighborhood to be considered.
EpsilonNeighborPredicate<O> - Class in elki.clustering.dbscan.predicates: The default DBSCAN and OPTICS neighbor predicate, using an epsilon-neighborhood.
EpsilonNeighborPredicate(double, Distance<? super O>) - Constructor for class elki.clustering.dbscan.predicates.EpsilonNeighborPredicate: Full constructor.
EpsilonNeighborPredicate.Instance - Class in elki.clustering.dbscan.predicates: Instance for a particular data set.
epsilons - Variable in class elki.outlier.subspace.OUTRES.KernelDensityEstimator: Epsilon values for different subspace dimensionalities
epsilonsq - Variable in class elki.clustering.dbscan.predicates.COPACNeighborPredicate: Squared value of epsilon.
epsilonsq - Variable in class elki.clustering.uncertain.FDBSCANNeighborPredicate.Instance: The epsilon distance a neighbor may have at most.
EpsilonSVR - Class in elki.svm
EpsilonSVR(double, boolean, double, double, double) - Constructor for class elki.svm.EpsilonSVR
equal(long[], long[]) - Static method in class elki.utilities.datastructures.BitsUtil: Test two bitsets for equality
equal(long, long) - Static method in class elki.utilities.datastructures.BitsUtil: Test two bitsets for equality
equal(DBIDRef, DBIDRef) - Method in interface elki.database.ids.DBIDFactory: Compare two DBIDs, for equality testing.
equal(DBIDRef, DBIDRef) - Static method in class elki.database.ids.DBIDUtil: Test two DBIDs for equality.
equal(DBIDRef, DBIDRef) - Method in class elki.database.ids.integer.AbstractIntegerDBIDFactory
equals(double[][], double[][]) - Static method in class elki.math.linearalgebra.VMath: Test for equality
equals(double[], double[]) - Static method in class elki.math.linearalgebra.VMath: Compare for equality.
equals(SpatialComparable, SpatialComparable) - Static method in class elki.data.spatial.SpatialUtil: Test two SpatialComparables for equality.
equals(Object) - Method in class elki.clustering.correlation.cash.CASHInterval
equals(Object) - Method in class elki.clustering.optics.DeLiClu.SpatialObjectPair: equals is used in updating the heap!
equals(Object) - Method in class elki.clustering.optics.OPTICSHeapEntry: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in class elki.data.BitVector: Indicates whether some other object is "equal to" this BitVector.
equals(Object) - Method in class elki.data.ClassLabel: Any ClassLabel should ensure a natural ordering that is consistent with equals.
equals(Object) - Method in class elki.data.ExternalID
equals(Object) - Method in class elki.data.HyperBoundingBox
equals(Object) - Method in class elki.data.SimpleClassLabel
equals(Object) - Method in class elki.data.Subspace
equals(Object) - Method in interface elki.database.ids.DBID: Deprecated.
equals(Object) - Method in interface elki.database.ids.DBIDRef: Deprecated.
Use DBIDUtil.equal(elki.database.ids.DBIDRef, elki.database.ids.DBIDRef) instead.
equals(Object) - Method in class elki.database.ids.EmptyDBIDs.EmptyDBIDIterator
equals(Object) - Method in class elki.database.ids.integer.ArrayStaticIntegerDBIDs.Itr
equals(Object) - Method in class elki.database.ids.integer.IntegerDBID: Deprecated.
equals(Object) - Method in class elki.database.ids.integer.IntegerDBID.Itr
equals(Object) - Method in class elki.database.ids.integer.IntegerDBIDPair
equals(Object) - Method in class elki.database.ids.integer.IntegerDBIDRange.Itr
equals(Object) - Method in class elki.database.ids.integer.IntegerDBIDVar.Itr
equals(Object) - Method in class elki.distance.adapter.AbstractSimilarityAdapter
equals(Object) - Method in class elki.distance.ArcCosineDistance
equals(Object) - Method in class elki.distance.ArcCosineUnitlengthDistance
equals(Object) - Method in class elki.distance.BrayCurtisDistance
equals(Object) - Method in class elki.distance.CanberraDistance
equals(Object) - Method in class elki.distance.ClarkDistance
equals(Object) - Method in class elki.distance.colorhistogram.HistogramIntersectionDistance
equals(Object) - Method in class elki.distance.correlation.AbsolutePearsonCorrelationDistance
equals(Object) - Method in class elki.distance.correlation.AbsoluteUncenteredCorrelationDistance
equals(Object) - Method in class elki.distance.correlation.PearsonCorrelationDistance
equals(Object) - Method in class elki.distance.correlation.SquaredPearsonCorrelationDistance
equals(Object) - Method in class elki.distance.correlation.SquaredUncenteredCorrelationDistance
equals(Object) - Method in class elki.distance.correlation.UncenteredCorrelationDistance
equals(Object) - Method in class elki.distance.correlation.WeightedPearsonCorrelationDistance
equals(Object) - Method in class elki.distance.correlation.WeightedSquaredPearsonCorrelationDistance
equals(Object) - Method in class elki.distance.CosineDistance
equals(Object) - Method in class elki.distance.CosineUnitlengthDistance
equals(Object) - Method in class elki.distance.external.DiskCacheBasedDoubleDistance
equals(Object) - Method in class elki.distance.external.DiskCacheBasedFloatDistance
equals(Object) - Method in class elki.distance.external.FileBasedSparseDoubleDistance
equals(Object) - Method in class elki.distance.external.FileBasedSparseFloatDistance
equals(Object) - Method in class elki.distance.geo.DimensionSelectingLatLngDistance
equals(Object) - Method in class elki.distance.geo.LatLngDistance
equals(Object) - Method in class elki.distance.geo.LngLatDistance
equals(Object) - Method in class elki.distance.histogram.HistogramMatchDistance
equals(Object) - Method in class elki.distance.histogram.KolmogorovSmirnovDistance
equals(Object) - Method in class elki.distance.MatrixWeightedQuadraticDistance
equals(Object) - Method in class elki.distance.minkowski.EuclideanDistance
equals(Object) - Method in class elki.distance.minkowski.LPIntegerNormDistance
equals(Object) - Method in class elki.distance.minkowski.LPNormDistance
equals(Object) - Method in class elki.distance.minkowski.ManhattanDistance
equals(Object) - Method in class elki.distance.minkowski.MaximumDistance
equals(Object) - Method in class elki.distance.minkowski.MinimumDistance
equals(Object) - Method in class elki.distance.minkowski.SquaredEuclideanDistance
equals(Object) - Method in class elki.distance.minkowski.WeightedLPNormDistance
equals(Object) - Method in class elki.distance.minkowski.WeightedSquaredEuclideanDistance
equals(Object) - Method in class elki.distance.probabilistic.ChiSquaredDistance
equals(Object) - Method in class elki.distance.probabilistic.FisherRaoDistance
equals(Object) - Method in class elki.distance.probabilistic.HellingerDistance
equals(Object) - Method in class elki.distance.probabilistic.JeffreyDivergenceDistance
equals(Object) - Method in class elki.distance.probabilistic.KullbackLeiblerDivergenceAsymmetricDistance
equals(Object) - Method in class elki.distance.probabilistic.KullbackLeiblerDivergenceReverseAsymmetricDistance
equals(Object) - Method in class elki.distance.probabilistic.TriangularDiscriminationDistance
equals(Object) - Method in class elki.distance.probabilistic.TriangularDistance
equals(Object) - Method in class elki.distance.RandomStableDistance
equals(Object) - Method in class elki.distance.set.HammingDistance
equals(Object) - Method in class elki.distance.set.JaccardSimilarityDistance
equals(Object) - Method in class elki.distance.strings.LevenshteinDistance
equals(Object) - Method in class elki.distance.strings.NormalizedLevenshteinDistance
equals(Object) - Method in class elki.distance.subspace.AbstractDimensionsSelectingDistance
equals(Object) - Method in class elki.distance.subspace.OnedimensionalDistance
equals(Object) - Method in class elki.distance.subspace.SubspaceEuclideanDistance
equals(Object) - Method in class elki.distance.subspace.SubspaceLPNormDistance
equals(Object) - Method in class elki.distance.subspace.SubspaceManhattanDistance
equals(Object) - Method in class elki.distance.subspace.SubspaceMaximumDistance
equals(Object) - Method in class elki.distance.timeseries.AbstractEditDistance
equals(Object) - Method in class elki.distance.timeseries.EDRDistance
equals(Object) - Method in class elki.distance.timeseries.ERPDistance
equals(Object) - Method in class elki.distance.timeseries.LCSSDistance
equals(Object) - Method in class elki.evaluation.clustering.pairsegments.Segment
equals(Object) - Method in class elki.index.tree.IndexTreePath
equals(Object) - Method in class elki.index.tree.metrical.mtreevariants.mktrees.mkcop.ApproximationLine: Returns true if this object is the same as the o argument; false otherwise.
equals(Object) - Method in class elki.index.tree.metrical.mtreevariants.MTreeDirectoryEntry
equals(Object) - Method in class elki.index.tree.metrical.mtreevariants.MTreeLeafEntry
equals(Object) - Method in class elki.index.tree.metrical.mtreevariants.query.MTreeSearchCandidate
equals(Object) - Method in class elki.index.tree.spatial.SpatialDirectoryEntry
equals(Object) - Method in class elki.index.tree.spatial.SpatialPointLeafEntry
equals(Object) - Method in class elki.itemsetmining.DenseItemset
equals(Object) - Method in class elki.itemsetmining.Itemset
equals(Object) - Method in class elki.itemsetmining.OneItemset
equals(Object) - Method in class elki.itemsetmining.SmallDenseItemset
equals(Object) - Method in class elki.outlier.density.HySortOD.Hypercube
equals(Object) - Method in class elki.outlier.subspace.AggarwalYuEvolutionary.Individuum
equals(Object) - Method in class elki.persistent.AbstractExternalizablePage: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in class elki.utilities.pairs.DoubleDoublePair: Trivial equals implementation
equals(Object) - Method in class elki.utilities.pairs.DoubleIntPair: Trivial equals implementation
equals(Object) - Method in class elki.utilities.pairs.DoubleObjPair
equals(Object) - Method in class elki.utilities.pairs.IntDoublePair: Trivial equals implementation
equals(Object) - Method in class elki.utilities.pairs.IntIntPair: Trivial equals implementation
equals(Object) - Method in class elki.utilities.pairs.Pair: Simple equals statement.
equals(Object) - Method in class elki.visualization.VisualizationTask
equalsPlusTimes(double[], double[], double[], double) - Method in class elki.visualization.visualizers.scatterplot.cluster.EMClusterVisualization.Instance: Compute out = x + y * a, for 2d.
equationsToString(int) - Method in class elki.math.linearalgebra.LinearEquationSystem: Returns a string representation of this equation system.
equationsToString(String, int) - Method in class elki.math.linearalgebra.LinearEquationSystem: Returns a string representation of this equation system.
equationsToString(String, NumberFormat) - Method in class elki.math.linearalgebra.LinearEquationSystem: Returns a string representation of this equation system.
equationsToString(NumberFormat) - Method in class elki.math.linearalgebra.LinearEquationSystem: Returns a string representation of this equation system.
erf(double) - Static method in class elki.math.statistics.distribution.NormalDistribution: Error function for Gaussian distributions = Normal distributions.
ERF_COEFF1 - Static variable in class elki.math.statistics.distribution.NormalDistribution: T.
ERF_COEFF2 - Static variable in class elki.math.statistics.distribution.NormalDistribution: T.
erfc(double) - Static method in class elki.math.statistics.distribution.NormalDistribution: Complementary error function for Gaussian distributions = Normal distributions.
erfcinv(double) - Static method in class elki.math.statistics.distribution.NormalDistribution: Inverse error function.
ErfcStddevWeight - Class in elki.math.linearalgebra.pca.weightfunctions: Gaussian Error Function Weight function, scaled using stddev using: \( \text{erfc}(\frac{1}{\sqrt{2}} \frac{\text{distance}}{\sigma}) \).
ErfcStddevWeight() - Constructor for class elki.math.linearalgebra.pca.weightfunctions.ErfcStddevWeight
ErfcWeight - Class in elki.math.linearalgebra.pca.weightfunctions: Gaussian Error Function Weight function, scaled such that the result it 0.1 when the distance is the maximum using: \( \text{erfc}(1.1630871536766736 \frac{\text{distance}}{\max}) \).
ErfcWeight() - Constructor for class elki.math.linearalgebra.pca.weightfunctions.ErfcWeight
ERiC - Class in elki.clustering.correlation: 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.
ERiC(ERiC.Settings) - Constructor for class elki.clustering.correlation.ERiC: Constructor.
ERiC.Par - Class in elki.clustering.correlation: Parameterization class.
ERiC.Settings - Class in elki.clustering.correlation: Class to wrap the ERiC settings.
ERiCNeighborPredicate - Class in elki.clustering.dbscan.predicates: ERiC neighborhood predicate.
ERiCNeighborPredicate(ERiC.Settings) - Constructor for class elki.clustering.dbscan.predicates.ERiCNeighborPredicate: Constructor.
ERiCNeighborPredicate.Instance - Class in elki.clustering.dbscan.predicates: Instance for a particular data set.
ERiCNeighborPredicate.Par - Class in elki.clustering.dbscan.predicates: Parameterization class.
ERPDistance - Class in elki.distance.timeseries: Edit Distance With Real Penalty distance for numerical vectors.
ERPDistance(double, double) - Constructor for class elki.distance.timeseries.ERPDistance: Constructor.
ERPDistance.Par - Class in elki.distance.timeseries: Parameterization class.
err - Variable in class elki.logging.CLISmartHandler: Output stream for error output.
ERR_DIMENSIONS - Static variable in class elki.math.linearalgebra.VMath: Error message when dimensionalities do not agree.
ERR_INVALID_RANGE - Static variable in class elki.math.linearalgebra.VMath: Error message when min > max is given as a range.
ERR_MATRIX_DIMENSIONS - Static variable in class elki.math.linearalgebra.VMath: Error message when matrix dimensionalities do not agree.
ERR_MATRIX_INNERDIM - Static variable in class elki.math.linearalgebra.VMath: Error message when matrix dimensionalities do not agree.
ERR_MATRIX_NONSQUARE - Static variable in class elki.math.linearalgebra.VMath: Error when a non-square matrix is used with determinant.
ERR_MATRIX_NOT_SPD - Static variable in class elki.math.linearalgebra.VMath: When a symmetric positive definite matrix is required.
ERR_MATRIX_RANK_DEFICIENT - Static variable in class elki.math.linearalgebra.QRDecomposition: When a matrix is rank deficient.
ERR_MATRIX_RANK_DEFICIENT - Static variable in class elki.math.linearalgebra.VMath: When a matrix is rank deficient.
ERR_SINGULAR - Static variable in class elki.math.linearalgebra.VMath: Error with a singular matrix.
ERR_TOO_LITTLE_WEIGHT - Static variable in class elki.math.linearalgebra.CovarianceMatrix: Error message reported when too little data (weight <= 1) in matrix.
ERR_VEC_DIMENSIONS - Static variable in class elki.math.linearalgebra.VMath: Error message when vector dimensionalities do not agree.
errformat - Variable in class elki.gui.util.LogPane: Formatter for error messages
errformat - Variable in class elki.logging.CLISmartHandler: Formatter for error messages
error(CharSequence) - Method in class elki.logging.Logging: Log a message at the 'severe' level.
error(CharSequence, Throwable) - Method in class elki.logging.Logging: Log a message at the 'severe' level.
ERROR - elki.application.internal.CheckParameterizables.State
ErrorFormatter - Class in elki.logging: Format a log record for error output, including a stack trace if available.
ErrorFormatter() - Constructor for class elki.logging.ErrorFormatter: Constructor.
errors - Variable in class elki.utilities.optionhandling.parameterization.AbstractParameterization: Errors
errors - Variable in class elki.utilities.optionhandling.parameterization.UnParameterization: Errors
errorsTo(Parameterization) - Method in class elki.utilities.optionhandling.parameterization.ChainedParameterization: Set the error target, since there is no unique way where errors can be reported.
errorTarget - Variable in class elki.utilities.optionhandling.parameterization.ChainedParameterization: Error target
errorVector(NumberVector) - Method in class elki.data.model.CorrelationAnalysisSolution: Returns the error vectors after projection.
errStyle - Variable in class elki.gui.util.LogPane: Error message style
esq - Variable in class elki.math.geodesy.AbstractEarthModel: Derived model parameters: e and e squared.
estimate(double[]) - Method in interface elki.math.statistics.distribution.estimator.DistributionEstimator: General form of the parameter estimation
estimate(double[]) - Method in interface elki.math.statistics.intrinsicdimensionality.DistanceBasedIntrinsicDimensionalityEstimator: Estimate from a distance list.
estimate(double[], int) - Method in interface elki.math.statistics.intrinsicdimensionality.DistanceBasedIntrinsicDimensionalityEstimator: Estimate from a distance list.
estimate(double, double) - Method in class elki.math.statistics.distribution.estimator.UniformMinMaxEstimator: Estimate parameters from minimum and maximum observed.
estimate(double, double, int) - Method in class elki.math.statistics.distribution.estimator.UniformEnhancedMinMaxEstimator: Estimate from simple characteristics.
estimate(A, NumberArrayAdapter<?, ? super A>) - Method in interface elki.math.statistics.intrinsicdimensionality.DistanceBasedIntrinsicDimensionalityEstimator: Estimate from a distance list.
estimate(A, NumberArrayAdapter<?, ? super A>, int) - Method in class elki.math.statistics.intrinsicdimensionality.AggregatedHillEstimator
estimate(A, NumberArrayAdapter<?, ? super A>, int) - Method in interface elki.math.statistics.intrinsicdimensionality.DistanceBasedIntrinsicDimensionalityEstimator: Estimate from a distance list.
estimate(A, NumberArrayAdapter<?, ? super A>, int) - Method in class elki.math.statistics.intrinsicdimensionality.EnsembleEstimator
estimate(A, NumberArrayAdapter<?, ? super A>, int) - Method in class elki.math.statistics.intrinsicdimensionality.GEDEstimator
estimate(A, NumberArrayAdapter<?, ? super A>, int) - Method in class elki.math.statistics.intrinsicdimensionality.HillEstimator
estimate(A, NumberArrayAdapter<?, ? super A>, int) - Method in class elki.math.statistics.intrinsicdimensionality.LMomentsEstimator
estimate(A, NumberArrayAdapter<?, ? super A>, int) - Method in class elki.math.statistics.intrinsicdimensionality.MOMEstimator
estimate(A, NumberArrayAdapter<?, ? super A>, int) - Method in class elki.math.statistics.intrinsicdimensionality.PWM2Estimator
estimate(A, NumberArrayAdapter<?, ? super A>, int) - Method in class elki.math.statistics.intrinsicdimensionality.PWMEstimator
estimate(A, NumberArrayAdapter<?, ? super A>, int) - Method in class elki.math.statistics.intrinsicdimensionality.RABIDEstimator
estimate(A, NumberArrayAdapter<?, ? super A>, int) - Method in class elki.math.statistics.intrinsicdimensionality.RVEstimator
estimate(A, NumberArrayAdapter<?, ? super A>, int) - Method in class elki.math.statistics.intrinsicdimensionality.ZipfEstimator
estimate(A, NumberArrayAdapter<?, A>) - Method in interface elki.math.statistics.distribution.estimator.DistributionEstimator: General form of the parameter estimation
estimate(A, NumberArrayAdapter<?, A>) - Method in class elki.math.statistics.distribution.estimator.ExpGammaExpMOMEstimator
estimate(A, NumberArrayAdapter<?, A>) - Method in class elki.math.statistics.distribution.estimator.GammaChoiWetteEstimator
estimate(A, NumberArrayAdapter<?, A>) - Method in class elki.math.statistics.distribution.estimator.InverseGaussianMLEstimator
estimate(A, NumberArrayAdapter<?, A>) - Method in class elki.math.statistics.distribution.estimator.LaplaceMLEEstimator
estimate(A, NumberArrayAdapter<?, A>) - Method in interface elki.math.statistics.distribution.estimator.LMMDistributionEstimator
estimate(A, NumberArrayAdapter<?, A>) - Method in interface elki.math.statistics.distribution.estimator.LogMADDistributionEstimator
estimate(A, NumberArrayAdapter<?, A>) - Method in interface elki.math.statistics.distribution.estimator.LogMeanVarianceEstimator
estimate(A, NumberArrayAdapter<?, A>) - Method in interface elki.math.statistics.distribution.estimator.LogMOMDistributionEstimator
estimate(A, NumberArrayAdapter<?, A>) - Method in class elki.math.statistics.distribution.estimator.LogNormalLevenbergMarquardtKDEEstimator
estimate(A, NumberArrayAdapter<?, A>) - Method in interface elki.math.statistics.distribution.estimator.MADDistributionEstimator
estimate(A, NumberArrayAdapter<?, A>) - Method in interface elki.math.statistics.distribution.estimator.MeanVarianceDistributionEstimator
estimate(A, NumberArrayAdapter<?, A>) - Method in class elki.math.statistics.distribution.estimator.meta.BestFitEstimator
estimate(A, NumberArrayAdapter<?, A>) - Method in class elki.math.statistics.distribution.estimator.meta.TrimmedEstimator
estimate(A, NumberArrayAdapter<?, A>) - Method in class elki.math.statistics.distribution.estimator.meta.WinsorizingEstimator
estimate(A, NumberArrayAdapter<?, A>) - Method in interface elki.math.statistics.distribution.estimator.MOMDistributionEstimator
estimate(A, NumberArrayAdapter<?, A>) - Method in class elki.math.statistics.distribution.estimator.NormalLevenbergMarquardtKDEEstimator
estimate(A, NumberArrayAdapter<?, A>) - Method in class elki.math.statistics.distribution.estimator.RayleighMLEEstimator
estimate(A, NumberArrayAdapter<?, A>) - Method in class elki.math.statistics.distribution.estimator.UniformEnhancedMinMaxEstimator
estimate(A, NumberArrayAdapter<?, A>) - Method in class elki.math.statistics.distribution.estimator.UniformMinMaxEstimator
estimate(DBIDs, Relation<? extends NumberVector>) - Method in class elki.math.statistics.intrinsicdimensionality.LPCAEstimator: Returns an ID estimate based on the specified filter for the given point DBID set and relation.
estimate(KNNSearcher<DBIDRef>, DistanceQuery<?>, DBIDRef, int) - Method in class elki.math.statistics.intrinsicdimensionality.ABIDEstimator
estimate(KNNSearcher<DBIDRef>, DistanceQuery<?>, DBIDRef, int) - Method in class elki.math.statistics.intrinsicdimensionality.ALIDEstimator
estimate(KNNSearcher<DBIDRef>, DistanceQuery<?>, DBIDRef, int) - Method in class elki.math.statistics.intrinsicdimensionality.RABIDEstimator
estimate(KNNSearcher<DBIDRef>, DistanceQuery<? extends NumberVector>, DBIDRef, int) - Method in class elki.math.statistics.intrinsicdimensionality.LPCAEstimator
estimate(KNNSearcher<DBIDRef>, DistanceQuery<? extends Object>, DBIDRef, int) - Method in interface elki.math.statistics.intrinsicdimensionality.DistanceBasedIntrinsicDimensionalityEstimator
estimate(KNNSearcher<DBIDRef>, DistanceQuery<? extends Object>, DBIDRef, int) - Method in class elki.math.statistics.intrinsicdimensionality.TightLIDEstimator
estimate(KNNSearcher<DBIDRef>, DistanceQuery<? extends O>, DBIDRef, int) - Method in interface elki.math.statistics.intrinsicdimensionality.IntrinsicDimensionalityEstimator: Estimate from a Reference Point, a KNNSearcher and the neighborhood size k.
estimate(RangeSearcher<DBIDRef>, DistanceQuery<?>, DBIDRef, double) - Method in class elki.math.statistics.intrinsicdimensionality.ALIDEstimator
estimate(RangeSearcher<DBIDRef>, DistanceQuery<? extends NumberVector>, DBIDRef, double) - Method in class elki.math.statistics.intrinsicdimensionality.LPCAEstimator
estimate(RangeSearcher<DBIDRef>, DistanceQuery<? extends Object>, DBIDRef, double) - Method in interface elki.math.statistics.intrinsicdimensionality.DistanceBasedIntrinsicDimensionalityEstimator
estimate(RangeSearcher<DBIDRef>, DistanceQuery<? extends Object>, DBIDRef, double) - Method in class elki.math.statistics.intrinsicdimensionality.TightLIDEstimator
estimate(RangeSearcher<DBIDRef>, DistanceQuery<? extends O>, DBIDRef, double) - Method in interface elki.math.statistics.intrinsicdimensionality.IntrinsicDimensionalityEstimator: Estimate from a distance list.
estimate(DoubleMinMax) - Method in class elki.math.statistics.distribution.estimator.UniformMinMaxEstimator: Estimate parameters from minimum and maximum observed.
estimateDensities(Relation<O>, KNNSearcher<DBIDRef>, DBIDs, WritableDataStore<double[]>) - Method in class elki.outlier.lof.KDEOS: Perform the kernel density estimation step.
estimateEigenvalue(double[][], double[]) - Method in class elki.datasource.filter.transform.FastMultidimensionalScalingTransform: Estimate the (singed!)
estimateFromExpMeanVariance(MeanVariance) - Method in class elki.math.statistics.distribution.estimator.ExpGammaExpMOMEstimator
estimateFromLMoments(double[]) - Method in class elki.math.statistics.distribution.estimator.ExponentialLMMEstimator
estimateFromLMoments(double[]) - Method in class elki.math.statistics.distribution.estimator.GammaLMMEstimator
estimateFromLMoments(double[]) - Method in class elki.math.statistics.distribution.estimator.GeneralizedExtremeValueLMMEstimator
estimateFromLMoments(double[]) - Method in class elki.math.statistics.distribution.estimator.GeneralizedLogisticAlternateLMMEstimator
estimateFromLMoments(double[]) - Method in class elki.math.statistics.distribution.estimator.GeneralizedParetoLMMEstimator
estimateFromLMoments(double[]) - Method in class elki.math.statistics.distribution.estimator.GumbelLMMEstimator
estimateFromLMoments(double[]) - Method in class elki.math.statistics.distribution.estimator.LaplaceLMMEstimator
estimateFromLMoments(double[]) - Method in interface elki.math.statistics.distribution.estimator.LMMDistributionEstimator: Estimate from the L-Moments.
estimateFromLMoments(double[]) - Method in class elki.math.statistics.distribution.estimator.LogisticLMMEstimator
estimateFromLMoments(double[]) - Method in class elki.math.statistics.distribution.estimator.LogNormalBilkovaLMMEstimator
estimateFromLMoments(double[]) - Method in class elki.math.statistics.distribution.estimator.LogNormalLMMEstimator
estimateFromLMoments(double[]) - Method in class elki.math.statistics.distribution.estimator.NormalLMMEstimator
estimateFromLMoments(double[]) - Method in class elki.math.statistics.distribution.estimator.RayleighLMMEstimator
estimateFromLMoments(double[]) - Method in class elki.math.statistics.distribution.estimator.SkewGNormalLMMEstimator
estimateFromLMoments(double[]) - Method in class elki.math.statistics.distribution.estimator.UniformLMMEstimator
estimateFromLMoments(double[]) - Method in class elki.math.statistics.distribution.estimator.WeibullLMMEstimator
estimateFromLogMeanVariance(MeanVariance, double) - Method in interface elki.math.statistics.distribution.estimator.LogMeanVarianceEstimator: Estimate the distribution from mean and variance.
estimateFromLogMeanVariance(MeanVariance, double) - Method in class elki.math.statistics.distribution.estimator.LogNormalLogMOMEstimator
estimateFromLogMedianMAD(double, double, double) - Method in class elki.math.statistics.distribution.estimator.LogLogisticMADEstimator
estimateFromLogMedianMAD(double, double, double) - Method in interface elki.math.statistics.distribution.estimator.LogMADDistributionEstimator: General form of the parameter estimation
estimateFromLogMedianMAD(double, double, double) - Method in class elki.math.statistics.distribution.estimator.LogNormalLogMADEstimator
estimateFromLogMedianMAD(double, double, double) - Method in class elki.math.statistics.distribution.estimator.WeibullLogMADEstimator
estimateFromLogStatisticalMoments(StatisticalMoments, double) - Method in class elki.math.statistics.distribution.estimator.LogGammaLogMOMEstimator
estimateFromLogStatisticalMoments(StatisticalMoments, double) - Method in interface elki.math.statistics.distribution.estimator.LogMeanVarianceEstimator
estimateFromLogStatisticalMoments(StatisticalMoments, double) - Method in interface elki.math.statistics.distribution.estimator.LogMOMDistributionEstimator: General form of the parameter estimation
estimateFromMeanVariance(MeanVariance) - Method in class elki.math.statistics.distribution.estimator.ExponentialMOMEstimator
estimateFromMeanVariance(MeanVariance) - Method in class elki.math.statistics.distribution.estimator.GammaMOMEstimator
estimateFromMeanVariance(MeanVariance) - Method in class elki.math.statistics.distribution.estimator.InverseGaussianMOMEstimator
estimateFromMeanVariance(MeanVariance) - Method in interface elki.math.statistics.distribution.estimator.MeanVarianceDistributionEstimator: Estimate the distribution from mean and variance.
estimateFromMeanVariance(MeanVariance) - Method in class elki.math.statistics.distribution.estimator.NormalMOMEstimator
estimateFromMedianMAD(double, double) - Method in class elki.math.statistics.distribution.estimator.CauchyMADEstimator
estimateFromMedianMAD(double, double) - Method in class elki.math.statistics.distribution.estimator.ExponentialMADEstimator
estimateFromMedianMAD(double, double) - Method in class elki.math.statistics.distribution.estimator.ExponentialMedianEstimator
estimateFromMedianMAD(double, double) - Method in class elki.math.statistics.distribution.estimator.GumbelMADEstimator
estimateFromMedianMAD(double, double) - Method in class elki.math.statistics.distribution.estimator.LaplaceMADEstimator
estimateFromMedianMAD(double, double) - Method in class elki.math.statistics.distribution.estimator.LogisticMADEstimator
estimateFromMedianMAD(double, double) - Method in interface elki.math.statistics.distribution.estimator.MADDistributionEstimator: General form of the parameter estimation
estimateFromMedianMAD(double, double) - Method in class elki.math.statistics.distribution.estimator.NormalMADEstimator
estimateFromMedianMAD(double, double) - Method in class elki.math.statistics.distribution.estimator.RayleighMADEstimator
estimateFromMedianMAD(double, double) - Method in class elki.math.statistics.distribution.estimator.UniformMADEstimator
estimateFromStatisticalMoments(StatisticalMoments) - Method in class elki.math.statistics.distribution.estimator.EMGOlivierNorbergEstimator
estimateFromStatisticalMoments(StatisticalMoments) - Method in interface elki.math.statistics.distribution.estimator.MeanVarianceDistributionEstimator
estimateFromStatisticalMoments(StatisticalMoments) - Method in interface elki.math.statistics.distribution.estimator.MOMDistributionEstimator: General form of the parameter estimation
estimateID(DBIDRef, DoubleDBIDListIter, double[]) - Method in class elki.outlier.intrinsic.ISOS: Estimate the local intrinsic dimensionality.
estimateInitialBeta(double[], double) - Static method in class elki.projection.PerplexityAffinityMatrixBuilder: Estimate beta from the distances in a row.
estimateInitialBeta(DBIDRef, DoubleDBIDListIter, double) - Static method in class elki.outlier.distance.SOS: Estimate beta from the distances in a row.
EstimateIntrinsicDimensionality<O> - Class in elki.application.statistics: Estimate global average intrinsic dimensionality of a data set.
EstimateIntrinsicDimensionality(InputStep, Distance<? super O>, IntrinsicDimensionalityEstimator<? super O>, double, double) - Constructor for class elki.application.statistics.EstimateIntrinsicDimensionality: Constructor.
estimateLogDensity(NumberVector) - Method in class elki.clustering.em.models.DiagonalGaussianModel
estimateLogDensity(NumberVector) - Method in class elki.clustering.em.models.MultivariateGaussianModel
estimateLogDensity(NumberVector) - Method in class elki.clustering.em.models.SphericalGaussianModel
estimateLogDensity(NumberVector) - Method in class elki.clustering.em.models.TextbookMultivariateGaussianModel
estimateLogDensity(NumberVector) - Method in class elki.clustering.em.models.TextbookSphericalGaussianModel
estimateLogDensity(NumberVector) - Method in class elki.clustering.em.models.TwoPassMultivariateGaussianModel
estimateLogDensity(ClusterFeature) - Method in interface elki.clustering.em.models.BetulaClusterModel: Estimate the log likelihood of a clustering feature.
estimateLogDensity(ClusterFeature) - Method in class elki.clustering.em.models.DiagonalGaussianModel
estimateLogDensity(ClusterFeature) - Method in class elki.clustering.em.models.MultivariateGaussianModel
estimateLogDensity(ClusterFeature) - Method in class elki.clustering.em.models.SphericalGaussianModel
estimateLogDensity(O) - Method in interface elki.clustering.em.models.EMClusterModel: Estimate the log likelihood of a vector.
estimateThreshold(CFTree.TreeNode) - Method in class elki.clustering.hierarchical.birch.CFTree
estimateThreshold(CFNode<L>, ArrayList<L>, double[]) - Method in class elki.index.tree.betula.CFTree
estimateViewport() - Method in class elki.visualization.projections.AffineProjection
estimateViewport() - Method in interface elki.visualization.projections.Projection2D: Estimate the viewport requirements
estimateViewport() - Method in class elki.visualization.projections.Simple2D
estimateY(double) - Method in class elki.math.statistics.PolynomialRegression: Performs an estimation of y on the specified x value.
estimateY(double[][]) - Method in class elki.math.statistics.MultipleLinearRegression: Perform an estimation of y on the specified matrix.
estimator - Variable in class elki.application.statistics.EstimateIntrinsicDimensionality: Estimation method.
estimator - Variable in class elki.outlier.intrinsic.IDOS: Estimator for intrinsic dimensionality.
estimator - Variable in class elki.outlier.intrinsic.IDOS.Par: Estimator for intrinsic dimensionality.
estimator - Variable in class elki.outlier.intrinsic.ISOS: Estimator of intrinsic dimensionality.
estimator - Variable in class elki.outlier.intrinsic.LID: Estimator for intrinsic dimensionality.
estimator - Variable in class elki.outlier.intrinsic.LID.Par: Estimator for intrinsic dimensionality.
estimator - Variable in class elki.projection.IntrinsicNearestNeighborAffinityMatrixBuilder: Estimator of intrinsic dimensionality.
estimator - Variable in class elki.projection.IntrinsicNearestNeighborAffinityMatrixBuilder.Par: Estimator of intrinsic dimensionality.
ESTIMATOR_ID - Static variable in class elki.outlier.intrinsic.IDOS.Par: The class used for estimating the intrinsic dimensionality.
ESTIMATOR_ID - Static variable in class elki.outlier.intrinsic.LID.Par: Class to use for estimating the ID.
ESTIMATOR_ID - Static variable in class elki.projection.IntrinsicNearestNeighborAffinityMatrixBuilder.Par: Parameter for ID estimation.
estimators - Variable in class elki.datasource.filter.normalization.columnwise.AttributeWiseBetaNormalization.Par: Stores the distribution estimators
estimators - Variable in class elki.datasource.filter.normalization.columnwise.AttributeWiseCDFNormalization: Stores the distribution estimators
estimators - Variable in class elki.datasource.filter.normalization.columnwise.AttributeWiseCDFNormalization.Par: Stores the distribution estimators
EU - Static variable in class elki.math.statistics.distribution.estimator.GeneralizedExtremeValueLMMEstimator: Euler-Mascheroni constant.
EUCLIDEAN - elki.application.greedyensemble.GreedyEnsembleExperiment.Distance
EUCLIDEAN - elki.visualization.visualizers.scatterplot.index.TreeSphereVisualization.Mode
EUCLIDEAN_KAPPA - Static variable in class elki.visualization.svg.SVGHyperSphere: Factor used for approximating circles with cubic beziers.
EuclideanDistance - Class in elki.distance.minkowski: Euclidean distance for NumberVectors.
EuclideanDistance() - Constructor for class elki.distance.minkowski.EuclideanDistance: Deprecated.
Use static instance!
EuclideanDistance.Par - Class in elki.distance.minkowski: Parameterization class.
EuclideanDistanceCriterion - Class in elki.clustering.hierarchical.birch: Distance criterion.
EuclideanDistanceCriterion() - Constructor for class elki.clustering.hierarchical.birch.EuclideanDistanceCriterion
EuclideanHashFunctionFamily - Class in elki.index.lsh.hashfamilies: 2-stable hash function family for Euclidean distances.
EuclideanHashFunctionFamily(RandomFactory, double, int) - Constructor for class elki.index.lsh.hashfamilies.EuclideanHashFunctionFamily: Constructor.
EuclideanHashFunctionFamily.Par - Class in elki.index.lsh.hashfamilies: Parameterization class.
euclideanLength(double[]) - Static method in class elki.math.linearalgebra.VMath: Euclidean length of the vector sqrt(v1^T v1).
EuclideanRStarTreeDistancePrioritySearcher<O extends SpatialComparable> - Class in elki.index.tree.spatial.rstarvariants.query: Instance of priority search for a particular spatial index.
EuclideanRStarTreeDistancePrioritySearcher(AbstractRStarTree<?, ?, ?>, Relation<? extends O>) - Constructor for class elki.index.tree.spatial.rstarvariants.query.EuclideanRStarTreeDistancePrioritySearcher: Constructor.
EuclideanRStarTreeKNNQuery<O extends NumberVector> - Class in elki.index.tree.spatial.rstarvariants.query: Instance of a KNN query for a particular spatial index.
EuclideanRStarTreeKNNQuery(AbstractRStarTree<?, ?, ?>, Relation<? extends O>) - Constructor for class elki.index.tree.spatial.rstarvariants.query.EuclideanRStarTreeKNNQuery: Constructor.
EuclideanRStarTreeRangeQuery<O extends NumberVector> - Class in elki.index.tree.spatial.rstarvariants.query: Instance of a range query for a particular spatial index.
EuclideanRStarTreeRangeQuery(AbstractRStarTree<?, ?, ?>, Relation<? extends O>) - Constructor for class elki.index.tree.spatial.rstarvariants.query.EuclideanRStarTreeRangeQuery: Constructor.
EuclideanSphericalElkanKMeans<V extends NumberVector> - Class in elki.clustering.kmeans.spherical: Elkan's fast k-means by exploiting the triangle inequality in the corresponding Euclidean space.
EuclideanSphericalElkanKMeans(int, int, KMeansInitialization, boolean) - Constructor for class elki.clustering.kmeans.spherical.EuclideanSphericalElkanKMeans: Constructor.
EuclideanSphericalElkanKMeans.Instance - Class in elki.clustering.kmeans.spherical: Inner instance, storing state for a single data set.
EuclideanSphericalElkanKMeans.Par<V extends NumberVector> - Class in elki.clustering.kmeans.spherical: Parameterization class.
EuclideanSphericalHamerlyKMeans<V extends NumberVector> - Class in elki.clustering.kmeans.spherical: A spherical k-Means algorithm based on Hamerly's fast k-means by exploiting the triangle inequality in the corresponding Euclidean space.
EuclideanSphericalHamerlyKMeans(int, int, KMeansInitialization, boolean) - Constructor for class elki.clustering.kmeans.spherical.EuclideanSphericalHamerlyKMeans: Constructor.
EuclideanSphericalHamerlyKMeans.Instance - Class in elki.clustering.kmeans.spherical: Inner instance, storing state for a single data set.
EuclideanSphericalHamerlyKMeans.Par<V extends NumberVector> - Class in elki.clustering.kmeans.spherical: Parameterization class.
EuclideanSphericalSimplifiedElkanKMeans<V extends NumberVector> - Class in elki.clustering.kmeans.spherical: A spherical k-Means algorithm based on Hamerly's fast k-means by exploiting the triangle inequality in the corresponding Euclidean space.
EuclideanSphericalSimplifiedElkanKMeans(int, int, KMeansInitialization, boolean) - Constructor for class elki.clustering.kmeans.spherical.EuclideanSphericalSimplifiedElkanKMeans: Constructor.
EuclideanSphericalSimplifiedElkanKMeans.Instance - Class in elki.clustering.kmeans.spherical: Inner instance, storing state for a single data set.
EuclideanSphericalSimplifiedElkanKMeans.Par<V extends NumberVector> - Class in elki.clustering.kmeans.spherical: Parameterization class.
EULERMASCHERONI - Static variable in class elki.math.MathUtil: Euler–Mascheroni constant.
EULERS_CONST - Static variable in class elki.math.statistics.distribution.GammaDistribution: Euler–Mascheroni constant
eval(double, double[]) - Method in interface elki.math.linearalgebra.fitting.FittingFunction: Compute value at position x as well as gradients for the parameters
eval(double, double[]) - Method in class elki.math.linearalgebra.fitting.GaussianFittingFunction: Compute the mixture of Gaussians at the given position
evals - Variable in class elki.gui.multistep.panels.EvaluationTabPanel: The data input configured
evals - Variable in class elki.gui.multistep.panels.OutputTabPanel: Algorithm step to run on.
evalTab - Variable in class elki.gui.multistep.MultiStepGUI: Evaluation panel.
evaluate(DBIDs, DoubleDBIDList) - Method in interface elki.evaluation.scores.ScoreEvaluation: Evaluate given a list of positives and a scoring.
evaluate(ScoreEvaluation.Adapter) - Method in class elki.evaluation.scores.AUPRCEvaluation
evaluate(ScoreEvaluation.Adapter) - Method in class elki.evaluation.scores.AveragePrecisionEvaluation
evaluate(ScoreEvaluation.Adapter) - Method in class elki.evaluation.scores.DCGEvaluation
evaluate(ScoreEvaluation.Adapter) - Method in class elki.evaluation.scores.MaximumF1Evaluation
evaluate(ScoreEvaluation.Adapter) - Method in class elki.evaluation.scores.NDCGEvaluation
evaluate(ScoreEvaluation.Adapter) - Method in class elki.evaluation.scores.PrecisionAtKEvaluation
evaluate(ScoreEvaluation.Adapter) - Method in class elki.evaluation.scores.PRGCEvaluation
evaluate(ScoreEvaluation.Adapter) - Method in class elki.evaluation.scores.ROCEvaluation
evaluate(ScoreEvaluation.Adapter) - Method in interface elki.evaluation.scores.ScoreEvaluation: Evaluate a given predicate and iterator.
evaluate(EvaluationResult, int, int, Supplier<ScoreEvaluation.Adapter>) - Method in class elki.evaluation.outlier.OutlierRankingEvaluation: Produce various evaluation statistics
evaluateBy(ScoreEvaluation) - Method in class elki.result.outlier.OutlierResult: Evaluate given a set of positives and a scoring.
evaluateClustering(Database, Relation<? extends NumberVector>, Clustering<?>) - Method in class elki.evaluation.clustering.internal.ClusterRadius: Evaluate a single clustering.
evaluateClustering(Relation<? extends NumberVector>, Clustering<?>) - Method in class elki.evaluation.clustering.internal.ConcordantPairsGammaTau: Evaluate a single clustering.
evaluateClustering(Relation<? extends NumberVector>, Clustering<?>) - Method in class elki.evaluation.clustering.internal.DaviesBouldinIndex: Evaluate a single clustering.
evaluateClustering(Relation<? extends NumberVector>, Clustering<?>) - Method in class elki.evaluation.clustering.internal.PBMIndex: Evaluate a single clustering.
evaluateClustering(Relation<? extends NumberVector>, Clustering<?>) - Method in class elki.evaluation.clustering.internal.SimplifiedSilhouette: Evaluate a single clustering.
evaluateClustering(Relation<? extends NumberVector>, Clustering<?>) - Method in class elki.evaluation.clustering.internal.SquaredErrors: Evaluate a single clustering.
evaluateClustering(Relation<? extends NumberVector>, Clustering<?>) - Method in class elki.evaluation.clustering.internal.VarianceRatioCriterion: Evaluate a single clustering.
evaluateClustering(Relation<? extends O>, DistanceQuery<O>, Clustering<?>) - Method in class elki.evaluation.clustering.internal.CIndex: Evaluate a single clustering.
evaluateClustering(Relation<O>, Clustering<?>) - Method in class elki.evaluation.clustering.internal.DBCV: Evaluate a single clustering.
evaluateClustering(Relation<O>, DistanceQuery<O>, Clustering<?>) - Method in class elki.evaluation.clustering.internal.Silhouette: Evaluate a single clustering.
EvaluateClustering - Class in elki.evaluation.clustering: Evaluate a clustering result by comparing it to an existing cluster label.
EvaluateClustering(ClusteringAlgorithm<?>, boolean, boolean) - Constructor for class elki.evaluation.clustering.EvaluateClustering: Constructor.
EvaluateClustering.Par - Class in elki.evaluation.clustering: Parameterization class.
EvaluateClustering.ScoreResult - Class in elki.evaluation.clustering: Result object for outlier score judgements.
evaluateClusters(ArrayList<PROCLUS.PROCLUSCluster>, long[][], Relation<? extends NumberVector>) - Method in class elki.clustering.subspace.PROCLUS: Evaluates the quality of the clusters.
evaluateConstrainedQuadraticFunction(double[][], double[], double, double[], double[], ConstrainedQuadraticProblemSolver.DimensionState[], boolean, double[], double) - Method in class elki.math.linearalgebra.ConstrainedQuadraticProblemSolver: Main recursive function.
evaluateConstrainedQuadraticFunction1D(double, double, double, double, double, double[], double) - Method in class elki.math.linearalgebra.ConstrainedQuadraticProblemSolver: Finds the maximum for a 1d constrained quadratic function.
EvaluateIntrinsicDimensionalityEstimators - Class in elki.application.experiments: Class for testing the estimation quality of intrinsic dimensionality estimators.
EvaluateIntrinsicDimensionalityEstimators(int, int, int, int, EvaluateIntrinsicDimensionalityEstimators.Aggregate, EvaluateIntrinsicDimensionalityEstimators.OutputFormat, RandomFactory) - Constructor for class elki.application.experiments.EvaluateIntrinsicDimensionalityEstimators: Constructor.
EvaluateIntrinsicDimensionalityEstimators.Aggregate - Enum in elki.application.experiments: Aggregation methods.
EvaluateIntrinsicDimensionalityEstimators.OutputFormat - Enum in elki.application.experiments: Output format
EvaluateIntrinsicDimensionalityEstimators.Par - Class in elki.application.experiments: Parameterization class.
evaluateKNN(double[], ModifiableDoubleDBIDList, Relation<?>, Object2IntOpenHashMap<Object>, Object) - Method in class elki.algorithm.statistics.EvaluateRetrievalPerformance.KNNEvaluator: Evaluate by simulating kNN classification for k=1...maxk
evaluateOutlierResult(Database, OutlierResult) - Method in class elki.evaluation.outlier.ComputeOutlierHistogram: Evaluate a single outlier result as histogram.
EvaluatePrecomputedOutlierScores - Class in elki.application.greedyensemble: Class to load an outlier detection summary file, as produced by ComputeKNNOutlierScores, and compute popular evaluation metrics.
EvaluatePrecomputedOutlierScores(URI, StreamingParser, Pattern, Path, String) - Constructor for class elki.application.greedyensemble.EvaluatePrecomputedOutlierScores: Constructor.
EvaluatePrecomputedOutlierScores.Par - Class in elki.application.greedyensemble: Parameterization class.
evaluateQuadraticFormula(double[][], double[], double, double[]) - Static method in class elki.math.linearalgebra.ConstrainedQuadraticProblemSolver: calculate \( \tfrac12 x^T A x + x^t b + c \) for the given values.
evaluateRanking(ScoreEvaluation, Cluster<?>, DoubleDBIDList) - Static method in class elki.evaluation.clustering.EvaluateClustering: Evaluate given a cluster (of positive elements) and a scoring list.
EvaluateRankingQuality<V extends NumberVector> - Class in elki.algorithm.statistics: Evaluate a distance function with respect to kNN queries.
EvaluateRankingQuality(Distance<? super V>, int) - Constructor for class elki.algorithm.statistics.EvaluateRankingQuality: Constructor.
EvaluateRetrievalPerformance<O> - Class in elki.algorithm.statistics: Evaluate a distance functions performance by computing the mean average precision, ROC, and NN classification performance when ranking the objects by distance.
EvaluateRetrievalPerformance(Distance<? super O>, double, RandomFactory, boolean, int) - Constructor for class elki.algorithm.statistics.EvaluateRetrievalPerformance: Constructor.
EvaluateRetrievalPerformance.KNNEvaluator - Class in elki.algorithm.statistics: Evaluate kNN retrieval performance.
EvaluateRetrievalPerformance.RetrievalPerformanceResult - Class in elki.algorithm.statistics: Result object for MAP scores.
Evaluation(List<? extends Evaluator>, Database) - Constructor for class elki.workflow.EvaluationStep.Evaluation: Constructor.
evaluationName - Variable in class elki.evaluation.classification.ConfusionMatrixEvaluationResult: Holds the used EvaluationProcedure.
EvaluationResult - Class in elki.result: Abstract evaluation result.
EvaluationResult() - Constructor for class elki.result.EvaluationResult: Constructor.
EvaluationResult.Measurement - Class in elki.result: Class representing a single measurement.
EvaluationResult.MeasurementGroup - Class in elki.result: A group of evaluation measurements.
evaluationStep - Variable in class elki.KDDTask: The evaluation step.
evaluationStep - Variable in class elki.KDDTask.Par
EvaluationStep - Class in elki.workflow: The "evaluation" step, where data is analyzed.
EvaluationStep(List<? extends Evaluator>) - Constructor for class elki.workflow.EvaluationStep: Constructor.
EvaluationStep.Evaluation - Class in elki.workflow: Class to handle running the evaluators on a database instance.
EvaluationStep.Par - Class in elki.workflow: Parameterization class.
EvaluationTabPanel - Class in elki.gui.multistep.panels: Panel to handle result evaluation
EvaluationTabPanel(InputTabPanel, AlgorithmTabPanel) - Constructor for class elki.gui.multistep.panels.EvaluationTabPanel: Constructor.
EvaluationVisualization - Class in elki.visualization.visualizers.visunproj: Pseudo-Visualizer, that lists the cluster evaluation results found.
EvaluationVisualization() - Constructor for class elki.visualization.visualizers.visunproj.EvaluationVisualization: Constructor.
Evaluator - Interface in elki.evaluation: Interface for post-algorithm evaluations, such as histograms, outlier score evaluations, ...
EVALUATOR_ID - Static variable in class elki.workflow.EvaluationStep.Par: Parameter ID to specify the evaluators to run.
evaluators - Variable in class elki.workflow.EvaluationStep.Evaluation: Evaluators to run.
evaluators - Variable in class elki.workflow.EvaluationStep: Evaluators to run.
evaluators - Variable in class elki.workflow.EvaluationStep.Par: Evaluators to run
evaluteResult(Database, Clustering<?>, Clustering<?>) - Method in class elki.evaluation.clustering.EvaluateClustering: Evaluate a clustering result.
Event() - Constructor for enum elki.datasource.bundle.BundleStreamSource.Event
eventarea - Variable in class elki.visualization.visualizers.optics.OPTICSPlotCutVisualization.Instance: Sensitive (clickable) area
eventManager - Variable in class elki.database.AbstractDatabase: The event manager, collects events and fires them on demand.
EvolutionarySearch(Relation<? extends NumberVector>, ArrayList<ArrayList<DBIDs>>, Random) - Constructor for class elki.outlier.subspace.AggarwalYuEvolutionary.EvolutionarySearch: Constructor.
EVT_DBLCLICK_DELAY - Static variable in class elki.visualization.visualizers.pairsegments.CircleSegmentsVisualizer.Instance.SegmentListenerProxy: Mouse double click time window in milliseconds
ewma - Variable in class elki.timeseries.SigniTrendChangeDetection.Instance: Moving average and variance.
ewmv - Variable in class elki.timeseries.SigniTrendChangeDetection.Instance: Moving average and variance.
exact - Variable in class elki.algorithm.statistics.DistanceStatisticsWithClasses: Compute exactly (slower).
exact - Variable in class elki.database.query.ExactPrioritySearcher: Refined candidates.
EXACT_ASSIGN_ID - Static variable in class elki.clustering.em.KDTreeEM.Par: Parameter to produce more precise final assignments
exactAssign - Variable in class elki.clustering.em.KDTreeEM: Perform exact cluster assignments
exactAssign - Variable in class elki.clustering.em.KDTreeEM.Par: Perform the slower exact assignment step.
exactMinMax(Relation<O>, DistanceQuery<O>) - Method in class elki.algorithm.statistics.DistanceStatisticsWithClasses: Compute the exact maximum and minimum.
exactOnly() - Method in class elki.database.query.QueryBuilder: Only accept exact methods, no approximate methods.
ExactPrioritySearcher<O> - Class in elki.database.query: Priority searcher that refines all objects to their exact distances, using another priority searcher inside to provide candidates.
ExactPrioritySearcher(PrioritySearcher<O>) - Constructor for class elki.database.query.ExactPrioritySearcher: Constructor.
exception(CharSequence, Throwable) - Method in class elki.logging.Logging: Log a message with exception at the 'severe' level.
exception(String, Throwable) - Static method in class elki.logging.LoggingUtil: Static version to log a severe exception.
exception(Throwable) - Method in class elki.logging.Logging: Log an exception at the 'severe' level.
exception(Throwable) - Static method in class elki.logging.LoggingUtil: Static version to log a severe exception.
excessOfMass() - Method in class elki.clustering.hierarchical.extraction.HDBSCANHierarchyExtraction.TempCluster: Excess of mass measure.
excludeNotCovered(ModifiableDoubleDBIDList, double, ModifiableDoubleDBIDList) - Method in class elki.index.tree.metrical.covertree.AbstractCoverTree: Retain all elements within the current cover.
execute() - Method in class elki.gui.multistep.panels.ParameterTabPanel: Execute the task.
executed - Variable in class elki.gui.multistep.panels.InputTabPanel: Signal when an database input has been executed.
executeResize(double) - Method in class elki.visualization.batikutil.LazyCanvasResizer: Callback function that needs to be overridden with actual implementations.
executeStep() - Method in class elki.gui.multistep.panels.AlgorithmTabPanel
executeStep() - Method in class elki.gui.multistep.panels.EvaluationTabPanel
executeStep() - Method in class elki.gui.multistep.panels.InputTabPanel
executeStep() - Method in class elki.gui.multistep.panels.LoggingTabPanel
executeStep() - Method in class elki.gui.multistep.panels.OutputTabPanel
executeStep() - Method in class elki.gui.multistep.panels.ParameterTabPanel: Execute the configured step.
executor - Variable in class elki.parallel.ParallelCore: Executor service.
Executor - Interface in elki.parallel: Processor executor.
existed - Variable in class elki.persistent.OnDiskArrayPageFile: Whether or not the file originally existed
existed - Variable in class elki.persistent.PersistentPageFile: Whether we are initializing from an existing file.
existing - Variable in class elki.data.ClassLabel.Factory: Set for reusing the same objects.
existing - Variable in class elki.database.relation.ConvertToStringView: The database we use
exists(URI) - Static method in class elki.utilities.io.FileUtil: Check if the file/resource identified by an URI exists.
exp - Variable in class elki.result.EvaluationResult.Measurement: Observed value, minimum, maximum, expected value.
exp(double) - Static method in class elki.math.MathUtil: Delegate to FastMath.exp.
expandCluster(int, WritableIntegerDataStore, KNNSearcher<DBIDRef>, DBIDs, double, FiniteProgress) - Method in class elki.clustering.dbscan.LSDBC: Set-based expand cluster implementation.
expandCluster(DBIDRef, int, WritableIntegerDataStore, ModifiableDoubleDBIDList, ArrayModifiableDBIDs, RangeSearcher<DBIDRef>, FiniteProgress) - Method in class elki.clustering.dbscan.GriDBSCAN.Instance: Set-based expand cluster implementation.
expandCluster(DBIDRef, int, WritableIntegerDataStore, T, ArrayModifiableDBIDs, FiniteProgress) - Method in class elki.clustering.dbscan.GeneralizedDBSCAN.Instance: Set-based expand cluster implementation.
expandCluster(DBIDRef, ArrayModifiableDBIDs) - Method in class elki.clustering.dbscan.DBSCAN.Instance: DBSCAN-function expandCluster.
expandCluster(SimilarityQuery<O>, DBIDRef, FiniteProgress, IndefiniteProgress) - Method in class elki.clustering.SNNClustering: DBSCAN-function expandCluster adapted to SNN criterion.
expandClusterOrder(DBIDRef) - Method in class elki.clustering.optics.OPTICSHeap.Instance: OPTICS-function expandClusterOrder.
expandClusterOrder(DBIDRef) - Method in class elki.clustering.optics.OPTICSList.Instance: OPTICS-function expandClusterOrder.
expandClusterOrder(DBID, ClusterOrder, DistanceQuery<V>, FiniteProgress) - Method in class elki.clustering.optics.FastOPTICS: OPTICS algorithm for processing a point, but with different density estimates
expandDBID(DBIDRef) - Method in class elki.clustering.correlation.HiCO.Instance
expandDBID(DBIDRef) - Method in class elki.clustering.optics.GeneralizedOPTICS.Instance: Add the current DBID to the cluster order, and expand its neighbors if minPts and similar conditions are satisfied.
expandDBID(DBIDRef) - Method in class elki.clustering.subspace.DiSH.Instance
expandDBID(DBIDRef) - Method in class elki.clustering.subspace.HiSC.Instance
expandDirNodes(DeLiCluNode, DeLiCluNode) - Method in class elki.clustering.optics.DeLiClu: Expands the specified directory nodes.
expanded - Variable in class elki.index.tree.spatial.rstarvariants.deliclu.DeLiCluTree: Holds the ids of the expanded nodes.
expandLeafNodes(DeLiCluNode, DeLiCluNode, DataStore<KNNList>) - Method in class elki.clustering.optics.DeLiClu: Expands the specified leaf nodes.
expandNewSolution(double[], double[], int, double) - Method in class elki.math.linearalgebra.ConstrainedQuadraticProblemSolver: Expands the redRes to a problem with dim+1 and saves it into result
expandNode(O, KNNHeap, DoubleIntegerMinHeap, double, int) - Method in class elki.index.tree.spatial.rstarvariants.query.EuclideanRStarTreeKNNQuery
expandNode(O, KNNHeap, DoubleIntegerMinHeap, double, int) - Method in class elki.index.tree.spatial.rstarvariants.query.RStarTreeKNNSearcher
expandNodes(DeLiCluTree, DeLiClu.SpatialObjectPair, DataStore<KNNList>) - Method in class elki.clustering.optics.DeLiClu: Expands the spatial nodes of the specified pair.
expansion - Variable in class elki.index.tree.metrical.covertree.AbstractCoverTree: Constant expansion rate. 2 would be the intuitive value, but the original version used 1.3, so we copy this.
expansion - Variable in class elki.index.tree.metrical.covertree.AbstractCoverTree.Factory: Constant expansion rate. 2 would be the intuitive value, but the original version used 1.3, so we copy this.
expansion - Variable in class elki.index.tree.metrical.covertree.AbstractCoverTree.Factory.Par: Expansion rate.
EXPANSION_ID - Static variable in class elki.index.tree.metrical.covertree.AbstractCoverTree.Factory.Par: Expansion rate of the tree (going upward).
expect - Variable in class elki.outlier.COP: Expected amount of outliers.
expect - Variable in class elki.outlier.COP.Par: Expected amount of outliers.
expect - Variable in class elki.outlier.trivial.TrivialGeneratedOutlier: Expected share of outliers.
expect - Variable in class elki.outlier.trivial.TrivialGeneratedOutlier.Par: Expected share of outliers
EXPECT_ID - Static variable in class elki.outlier.COP.Par: Expected share of outliers.
EXPECT_ID - Static variable in class elki.outlier.trivial.TrivialGeneratedOutlier.Par: Expected share of outliers
expected(int, int) - Method in class elki.evaluation.scores.AUPRCEvaluation
expected(int, int) - Method in class elki.evaluation.scores.AveragePrecisionEvaluation
expected(int, int) - Method in class elki.evaluation.scores.DCGEvaluation
expected(int, int) - Method in class elki.evaluation.scores.MaximumF1Evaluation
expected(int, int) - Method in class elki.evaluation.scores.NDCGEvaluation
expected(int, int) - Method in class elki.evaluation.scores.PrecisionAtKEvaluation
expected(int, int) - Method in class elki.evaluation.scores.PRGCEvaluation
expected(int, int) - Method in class elki.evaluation.scores.ROCEvaluation
expected(int, int) - Method in interface elki.evaluation.scores.ScoreEvaluation: Expected score for a random result.
expectedMutualInformation - Variable in class elki.evaluation.clustering.Entropy: Expected mutual information
expectedMutualInformation() - Method in class elki.evaluation.clustering.Entropy: Get the expected mutual information.
ExpGammaDistribution - Class in elki.math.statistics.distribution: Exp-Gamma Distribution, with random generation and density functions.
ExpGammaDistribution(double, double, double) - Constructor for class elki.math.statistics.distribution.ExpGammaDistribution: Constructor for Gamma distribution.
ExpGammaDistribution.Par - Class in elki.math.statistics.distribution: Parameterization class
ExpGammaExpMOMEstimator - Class in elki.math.statistics.distribution.estimator: Simple parameter estimation for the ExpGamma distribution.
ExpGammaExpMOMEstimator() - Constructor for class elki.math.statistics.distribution.estimator.ExpGammaExpMOMEstimator: Private constructor.
ExpGammaExpMOMEstimator.Par - Class in elki.math.statistics.distribution.estimator: Parameterization class.
expirePage(P) - Method in class elki.persistent.LRUCache: Write page through to disk.
explain - Variable in class elki.math.linearalgebra.pca.AutotuningPCA.Cand: Score
explainedVariance - Variable in class elki.math.linearalgebra.pca.PCAFilteredResult: The amount of Variance explained by strong Eigenvalues
EXPLORE - elki.visualization.parallel3d.OpenGL3DParallelCoordinates.Instance.State
EXPONENT_OVERFLOW - Static variable in class elki.utilities.io.ParseUtil: Preallocated exceptions.
ExponentialDistribution - Class in elki.math.statistics.distribution: Exponential distribution.
ExponentialDistribution(double) - Constructor for class elki.math.statistics.distribution.ExponentialDistribution: Constructor.
ExponentialDistribution(double, double) - Constructor for class elki.math.statistics.distribution.ExponentialDistribution: Constructor.
ExponentialDistribution.Par - Class in elki.math.statistics.distribution: Parameterization class
ExponentialIntGenerator - Class in elki.utilities.datastructures.range: Generate an exponential range.
ExponentialIntGenerator(int, int, int) - Constructor for class elki.utilities.datastructures.range.ExponentialIntGenerator: Constructor.
ExponentialLMMEstimator - Class in elki.math.statistics.distribution.estimator: Estimate the parameters of a Gamma Distribution, using the methods of L-Moments (LMM).
ExponentialLMMEstimator() - Constructor for class elki.math.statistics.distribution.estimator.ExponentialLMMEstimator: Constructor.
ExponentialLMMEstimator.Par - Class in elki.math.statistics.distribution.estimator: Parameterization class.
ExponentiallyModifiedGaussianDistribution - Class in elki.math.statistics.distribution: Exponentially modified Gaussian (EMG) distribution (ExGaussian distribution) is a combination of a normal distribution and an exponential distribution.
ExponentiallyModifiedGaussianDistribution(double, double, double) - Constructor for class elki.math.statistics.distribution.ExponentiallyModifiedGaussianDistribution: Constructor for ExGaussian distribution
ExponentiallyModifiedGaussianDistribution.Par - Class in elki.math.statistics.distribution: Parameterization class
ExponentialMADEstimator - Class in elki.math.statistics.distribution.estimator: Estimate Exponential distribution parameters using Median and MAD.
ExponentialMADEstimator() - Constructor for class elki.math.statistics.distribution.estimator.ExponentialMADEstimator: Private constructor, use static instance!
ExponentialMADEstimator.Par - Class in elki.math.statistics.distribution.estimator: Parameterization class.
ExponentialMedianEstimator - Class in elki.math.statistics.distribution.estimator: Estimate Exponential distribution parameters using Median and MAD.
ExponentialMedianEstimator() - Constructor for class elki.math.statistics.distribution.estimator.ExponentialMedianEstimator: Private constructor, use static instance!
ExponentialMedianEstimator.Par - Class in elki.math.statistics.distribution.estimator: Parameterization class.
ExponentialMOMEstimator - Class in elki.math.statistics.distribution.estimator: Estimate Exponential distribution parameters using the mean, which is the maximum-likelihood estimate (MLE), but not very robust.
ExponentialMOMEstimator() - Constructor for class elki.math.statistics.distribution.estimator.ExponentialMOMEstimator: Private constructor, use static instance!
ExponentialMOMEstimator.Par - Class in elki.math.statistics.distribution.estimator: Parameterization class.
ExponentialStddevWeight - Class in elki.math.linearalgebra.pca.weightfunctions: Exponential Weight function, scaled using the standard deviation using: \( \sigma \exp(-\frac{1}{2} \frac{\text{distance}}{\sigma}) \).
ExponentialStddevWeight() - Constructor for class elki.math.linearalgebra.pca.weightfunctions.ExponentialStddevWeight
ExponentialWeight - Class in elki.math.linearalgebra.pca.weightfunctions: Exponential Weight function, scaled such that the result it 0.1 at distance equal max, so it does not completely disappear using: \( \exp(-2.3025850929940455 \frac{\text{distance}}{\max}) \)
ExponentialWeight() - Constructor for class elki.math.linearalgebra.pca.weightfunctions.ExponentialWeight
EXPONENTS_ID - Static variable in class tutorial.distancefunction.MultiLPNorm.Par: Option ID for the exponents
ExponionKMeans<V extends NumberVector> - Class in elki.clustering.kmeans: Newlings's Exponion k-means algorithm, exploiting the triangle inequality.
ExponionKMeans(NumberVectorDistance<? super V>, int, int, KMeansInitialization, boolean) - Constructor for class elki.clustering.kmeans.ExponionKMeans: Constructor.
ExponionKMeans.Instance - Class in elki.clustering.kmeans: Inner instance, storing state for a single data set.
ExponionKMeans.Par<V extends NumberVector> - Class in elki.clustering.kmeans: Parameterization class.
exportItem - Variable in class elki.visualization.gui.ResultWindow.DynamicMenu: The "Export Image" button, to save the image
ExportVisualizations - Class in elki.result: Class that automatically generates all visualizations and exports them into SVG files.
ExportVisualizations(Path, VisualizerParameterizer, double, ExportVisualizations.Format) - Constructor for class elki.result.ExportVisualizations: Constructor.
ExportVisualizations(Path, VisualizerParameterizer, double, ExportVisualizations.Format, int) - Constructor for class elki.result.ExportVisualizations: Constructor.
ExportVisualizations.Format - Enum in elki.result: File format
ExportVisualizations.Par - Class in elki.result: Parameterization class
expungeStaleEntries() - Static method in class elki.result.Metadata: Expunges stale entries from the table.
extend(A, ArrayAdapter<T, A>, T) - Static method in class elki.utilities.datastructures.arraylike.ExtendedArray: Static wrapper that has a nicer generics signature.
extend(SpatialComparable) - Method in class elki.data.ModifiableHyperBoundingBox: Extend the bounding box by some other spatial object.
ExtendedArray<T> - Class in elki.utilities.datastructures.arraylike: Class to extend an array with a single element virtually.
ExtendedArray(Object, ArrayAdapter<T, Object>, T) - Constructor for class elki.utilities.datastructures.arraylike.ExtendedArray: Constructor.
ExtendedNeighborhood - Class in elki.outlier.spatial.neighborhood: Neighborhood obtained by computing the k-fold closure of an existing neighborhood.
ExtendedNeighborhood(DataStore<DBIDs>) - Constructor for class elki.outlier.spatial.neighborhood.ExtendedNeighborhood: Constructor.
ExtendedNeighborhood.Factory<O> - Class in elki.outlier.spatial.neighborhood: Factory class.
ExtendedNeighborhood.Factory.Par<O> - Class in elki.outlier.spatial.neighborhood: Parameterization class.
extendMBR(SpatialComparable) - Method in class elki.index.tree.spatial.SpatialDirectoryEntry: Extend the MBR of this node.
extendNeighborhood(Database, Relation<? extends O>) - Method in class elki.outlier.spatial.neighborhood.ExtendedNeighborhood.Factory: Method to load the external neighbors.
EXTENSION - Static variable in class elki.result.textwriter.MultipleFilesOutput: File name extension.
ExternalClustering - Class in elki.clustering.meta: Read an external clustering result from a file, such as produced by ClusteringVectorDumper.
ExternalClustering(URI) - Constructor for class elki.clustering.meta.ExternalClustering: Constructor.
ExternalClustering.Par - Class in elki.clustering.meta: Parameterization class
ExternalDoubleOutlierScore - Class in elki.outlier.meta: External outlier detection scores, loading outlier scores from an external file.
ExternalDoubleOutlierScore(URI, Pattern, Pattern, boolean, ScalingFunction) - Constructor for class elki.outlier.meta.ExternalDoubleOutlierScore: Constructor.
ExternalDoubleOutlierScore.Par - Class in elki.outlier.meta: Parameterization class
ExternalID - Class in elki.data: External ID objects.
ExternalID(String) - Constructor for class elki.data.ExternalID: Constructor.
EXTERNALID - Static variable in class elki.data.type.TypeUtil: External ID type.
EXTERNALID_INDEX_ID - Static variable in class elki.datasource.filter.typeconversions.ExternalIDFilter.Par: Parameter that specifies the index of the label to be used as external Id, starting at 0.
ExternalIDFilter - Class in elki.datasource.filter.typeconversions: Class that turns a label column into an external ID column.
ExternalIDFilter(int) - Constructor for class elki.datasource.filter.typeconversions.ExternalIDFilter: Constructor.
ExternalIDFilter.Par - Class in elki.datasource.filter.typeconversions: Parameterization class.
externalIdIndex - Variable in class elki.datasource.filter.typeconversions.ExternalIDFilter: The index of the label to be used as external Id.
externalIdIndex - Variable in class elki.datasource.filter.typeconversions.ExternalIDFilter.Par
ExternalIDJoinDatabaseConnection - Class in elki.datasource: Joins multiple data sources by their label
ExternalIDJoinDatabaseConnection(List<? extends ObjectFilter>, List<? extends DatabaseConnection>) - Constructor for class elki.datasource.ExternalIDJoinDatabaseConnection: Constructor.
ExternalIDJoinDatabaseConnection.Par - Class in elki.datasource: Parameterization class.
ExternalizablePage - Interface in elki.persistent: Base interface for externalizable pages.
ExternalNeighborhood - Class in elki.outlier.spatial.neighborhood: A precomputed neighborhood, loaded from an external file.
ExternalNeighborhood(DataStore<DBIDs>) - Constructor for class elki.outlier.spatial.neighborhood.ExternalNeighborhood: Constructor.
ExternalNeighborhood.Factory - Class in elki.outlier.spatial.neighborhood: Factory class.
ExternalNeighborhood.Factory.Par - Class in elki.outlier.spatial.neighborhood: Parameterization class.
extra - Variable in class elki.result.Metadata.Hierarchy.ItrAnc: Additional object to return as first result.
extra - Variable in class elki.result.Metadata.Hierarchy.ItrDesc: Additional object to return as first result.
extra - Variable in class elki.utilities.datastructures.arraylike.ExtendedArray: The extra element
extra - Variable in class elki.utilities.datastructures.hierarchy.HashMapHierarchy.ItrAnc: Additional object to return as first result.
extra - Variable in class elki.utilities.datastructures.hierarchy.HashMapHierarchy.ItrDesc: Additional object to return as first result.
EXTRA_INTEGRITY_CHECKS - Static variable in class elki.index.tree.metrical.mtreevariants.AbstractMTree: Debugging flag: do extra integrity checks.
EXTRA_INTEGRITY_CHECKS - Static variable in class elki.index.tree.spatial.rstarvariants.AbstractRStarTree: Development flag: This will enable some extra integrity checks on the tree.
extract(int, int, int, boolean, FPGrowth.FPTree.Collector) - Method in class elki.itemsetmining.FPGrowth.FPTree: Extract itemsets ending in the given item.
extract(int, int, int, int, int[], int, int[], int[], boolean, FPGrowth.FPTree.Collector) - Method in class elki.itemsetmining.FPGrowth.FPTree: Extract itemsets ending in the given item.
extractClusters() - Method in class elki.clustering.hierarchical.extraction.AbstractCutDendrogram.Instance: Extract all clusters from the pi-lambda-representation.
extractClusters(ClusterOrder, double, int) - Method in class elki.clustering.optics.OPTICSXi: Extract clusters from a cluster order result.
extractClusters(Relation<? extends NumberVector>, DiSH.DiSHClusterOrder) - Method in class elki.clustering.subspace.DiSH: Extracts the clusters from the cluster order.
extractCorrelationClusters(Clustering<Model>, Relation<? extends NumberVector>, int, ERiCNeighborPredicate.Instance) - Method in class elki.clustering.correlation.ERiC: Extracts the correlation clusters and noise from the copac result and returns a mapping of correlation dimension to maps of clusters within this correlation dimension.
extractItemsets(DBIDs[], int, int, List<Itemset>) - Method in class elki.itemsetmining.Eclat
extractItemsets(DBIDs, DBIDs[], int[], int, int, int, List<Itemset>) - Method in class elki.itemsetmining.Eclat
extractLinear(int, int, int, int, int[], int, int[], FPGrowth.FPTree.Collector) - Method in class elki.itemsetmining.FPGrowth.FPTree: Extract itemsets from a linear tree.
extremum_alpha_n(int, double[]) - Method in class elki.clustering.correlation.cash.ParameterizationFunction: Determines the value for alpha_n where this function has a (local) extremum.
extremumType - Variable in class elki.clustering.correlation.cash.ParameterizationFunction: Holds the type of the global extremum.
extremumType(int, double[], HyperBoundingBox) - Method in class elki.clustering.correlation.cash.ParameterizationFunction: Returns the type of the extremum at the specified alpha values.
ExtremumType() - Constructor for enum elki.clustering.correlation.cash.ParameterizationFunction.ExtremumType

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