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

Packages that use Reference

Package	Description
de.lmu.ifi.dbs.elki.algorithm	Algorithms suitable as a task for the KDDTask main routine.
de.lmu.ifi.dbs.elki.algorithm.clustering	Clustering algorithms.
de.lmu.ifi.dbs.elki.algorithm.clustering.affinitypropagation	Affinity Propagation (AP) clustering.
de.lmu.ifi.dbs.elki.algorithm.clustering.biclustering	Biclustering algorithms.
de.lmu.ifi.dbs.elki.algorithm.clustering.correlation	Correlation clustering algorithms
de.lmu.ifi.dbs.elki.algorithm.clustering.em	Expectation-Maximization clustering algorithm.
de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan	Generalized DBSCAN.
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical	Hierarchical agglomerative clustering (HAC).
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.extraction	Extraction of partitional clusterings from hierarchical results.
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans	K-means clustering and variations.
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization	Initialization strategies for k-means.
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.quality	Quality measures for k-Means results.
de.lmu.ifi.dbs.elki.algorithm.clustering.optics	OPTICS family of clustering algorithms.
de.lmu.ifi.dbs.elki.algorithm.clustering.subspace	Axis-parallel subspace clustering algorithms The clustering algorithms in this package are instances of both, projected clustering algorithms or subspace clustering algorithms according to the classical but somewhat obsolete classification schema of clustering algorithms for axis-parallel subspaces.
de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain	Clustering algorithms for uncertain data.
de.lmu.ifi.dbs.elki.algorithm.itemsetmining	Algorithms for frequent itemset mining such as APRIORI.
de.lmu.ifi.dbs.elki.algorithm.outlier	Outlier detection algorithms
de.lmu.ifi.dbs.elki.algorithm.outlier.anglebased	Angle-based outlier detection algorithms.
de.lmu.ifi.dbs.elki.algorithm.outlier.clustering	Clustering based outlier detection.
de.lmu.ifi.dbs.elki.algorithm.outlier.distance	Distance-based outlier detection algorithms, such as DBOutlier and kNN.
de.lmu.ifi.dbs.elki.algorithm.outlier.distance.parallel	Parallel implementations of distance-based outlier detectors.
de.lmu.ifi.dbs.elki.algorithm.outlier.lof	LOF family of outlier detection algorithms.
de.lmu.ifi.dbs.elki.algorithm.outlier.lof.parallel	Parallelized variants of LOF.
de.lmu.ifi.dbs.elki.algorithm.outlier.meta	Meta outlier detection algorithms: external scores, score rescaling.
de.lmu.ifi.dbs.elki.algorithm.outlier.spatial	Spatial outlier detection algorithms
de.lmu.ifi.dbs.elki.algorithm.outlier.subspace	Subspace outlier detection methods.
de.lmu.ifi.dbs.elki.algorithm.outlier.svm	Support-Vector-Machines for outlier detection.
de.lmu.ifi.dbs.elki.algorithm.statistics	Statistical analysis algorithms The algorithms in this package perform statistical analysis of the data (e.g. compute distributions, distance distributions etc.)
de.lmu.ifi.dbs.elki.application	Base classes for stand alone applications.
de.lmu.ifi.dbs.elki.application.greedyensemble	Greedy ensembles for outlier detection.
de.lmu.ifi.dbs.elki.application.internal	Internal utilities for development.
de.lmu.ifi.dbs.elki.data.uncertain	Uncertain data objects.
de.lmu.ifi.dbs.elki.database.ids.integer	Integer-based DBID implementation -- do not use directly - always use DBIDUtil.
de.lmu.ifi.dbs.elki.datasource.filter.transform	Data space transformations.
de.lmu.ifi.dbs.elki.distance.distancefunction	Distance functions for use within ELKI.
de.lmu.ifi.dbs.elki.distance.distancefunction.colorhistogram	Distance functions using correlations.
de.lmu.ifi.dbs.elki.distance.distancefunction.geo	Geographic (earth) distance functions.
de.lmu.ifi.dbs.elki.distance.distancefunction.histogram	Distance functions for one-dimensional histograms.
de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic	Distance from probability theory, mostly divergences such as K-L-divergence, J-divergence.
de.lmu.ifi.dbs.elki.distance.distancefunction.set	Distance functions for binary and set type data.
de.lmu.ifi.dbs.elki.distance.distancefunction.strings	Distance functions for strings.
de.lmu.ifi.dbs.elki.distance.distancefunction.timeseries	Distance functions designed for time series.
de.lmu.ifi.dbs.elki.distance.similarityfunction	Similarity functions.
de.lmu.ifi.dbs.elki.distance.similarityfunction.cluster	Similarity measures for comparing clusters.
de.lmu.ifi.dbs.elki.evaluation.clustering	Evaluation of clustering results.
de.lmu.ifi.dbs.elki.evaluation.clustering.internal	Internal evaluation measures for clusterings.
de.lmu.ifi.dbs.elki.evaluation.clustering.pairsegments	Pair-segment analysis of multiple clusterings.
de.lmu.ifi.dbs.elki.evaluation.outlier	Evaluate an outlier score using a misclassification based cost model.
de.lmu.ifi.dbs.elki.index.idistance	iDistance is a distance based indexing technique, using a reference points embedding.
de.lmu.ifi.dbs.elki.index.lsh.hashfamilies	Hash function families for LSH.
de.lmu.ifi.dbs.elki.index.lsh.hashfunctions	Hash functions for LSH
de.lmu.ifi.dbs.elki.index.preprocessed.fastoptics	Preprocessed index used by the FastOPTICS algorithm.
de.lmu.ifi.dbs.elki.index.preprocessed.knn	Indexes providing KNN and rKNN data.
de.lmu.ifi.dbs.elki.index.projected	Projected indexes for data.
de.lmu.ifi.dbs.elki.index.tree.metrical.covertree	Cover-tree variations.
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mtree	MTree
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.insert	Insertion (choose path) strategies of nodes in an M-Tree (and variants).
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.split	Splitting strategies of nodes in an M-Tree (and variants).
de.lmu.ifi.dbs.elki.index.tree.spatial.kd	K-d-tree and variants.
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.query	Queries on the R-Tree family of indexes: kNN and range queries.
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rstar	RStarTree
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.bulk	Packages for bulk-loading R*-Trees.
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.insert	Insertion strategies for R-Trees
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.overflow	Overflow treatment strategies for R-Trees
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.reinsert	Reinsertion strategies for R-Trees
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.split	Splitting strategies for R-Trees
de.lmu.ifi.dbs.elki.index.vafile	Vector Approximation File
de.lmu.ifi.dbs.elki.math	Mathematical operations and utilities used throughout the framework.
de.lmu.ifi.dbs.elki.math.dimensionsimilarity	Functions to compute the similarity of dimensions (or the interestingness of the combination).
de.lmu.ifi.dbs.elki.math.geodesy
de.lmu.ifi.dbs.elki.math.geometry	Algorithms from computational geometry.
de.lmu.ifi.dbs.elki.math.linearalgebra.pca	Principal Component Analysis (PCA) and Eigenvector processing.
de.lmu.ifi.dbs.elki.math.linearalgebra.randomprojections	Random projection families.
de.lmu.ifi.dbs.elki.math.random	Random number generation.
de.lmu.ifi.dbs.elki.math.spacefillingcurves	Space filling curves.
de.lmu.ifi.dbs.elki.math.statistics	Statistical tests and methods.
de.lmu.ifi.dbs.elki.math.statistics.dependence	Statistical measures of dependence, such as correlation.
de.lmu.ifi.dbs.elki.math.statistics.distribution	Standard distributions, with random generation functionalities.
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator	Estimators for statistical distributions.
de.lmu.ifi.dbs.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.
de.lmu.ifi.dbs.elki.math.statistics.intrinsicdimensionality	Methods for estimating the intrinsic dimensionality.
de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions	Kernel functions from statistics.
de.lmu.ifi.dbs.elki.math.statistics.tests	Statistical tests.
de.lmu.ifi.dbs.elki.result	Result types, representation and handling
de.lmu.ifi.dbs.elki.utilities.datastructures.arrays	Utilities for arrays: advanced sorting for primitvie arrays.
de.lmu.ifi.dbs.elki.utilities.documentation	Documentation utilities: Annotations for Title, Description, Reference
de.lmu.ifi.dbs.elki.utilities.scaling.outlier	Scaling of Outlier scores, that require a statistical analysis of the occurring values
de.lmu.ifi.dbs.elki.visualization.visualizers.pairsegments	Visualizers for inspecting cluster differences using pair counting segments.
de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.density	Visualizers for data set density in a scatterplot projection.
de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.outlier	Visualizers for outlier scores based on 2D projections.
tutorial.clustering	Classes from the tutorial on implementing a custom k-means variation.
tutorial.outlier

Packages with annotations of type Reference

Package	Description
de.lmu.ifi.dbs.elki.algorithm.outlier.lof.parallel	Parallelized variants of LOF.

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

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

Modifier and Type	Class and Description
class	DependencyDerivator<V extends NumberVector> Dependency derivator computes quantitatively linear dependencies among attributes of a given dataset based on a linear correlation PCA.

Uses of Reference in de.lmu.ifi.dbs.elki.algorithm.clustering

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

Modifier and Type	Class and Description
class	CanopyPreClustering<O> Canopy pre-clustering is a simple preprocessing step for clustering.
class	DBSCAN<O> Density-Based Clustering of Applications with Noise (DBSCAN), an algorithm to find density-connected sets in a database.
class	NaiveMeanShiftClustering<V extends NumberVector> Mean-shift based clustering algorithm.
class	SNNClustering<O> Shared nearest neighbor clustering.

Uses of Reference in de.lmu.ifi.dbs.elki.algorithm.clustering.affinitypropagation

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

Modifier and Type	Class and Description
class	AffinityPropagationClusteringAlgorithm<O> Cluster analysis by affinity propagation.

Uses of Reference in de.lmu.ifi.dbs.elki.algorithm.clustering.biclustering

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

Modifier and Type	Class and Description
class	ChengAndChurch<V extends NumberVector> Perform Cheng and Church biclustering.

Uses of Reference in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation

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

Modifier and Type	Class and Description
class	CASH<V extends NumberVector> The CASH algorithm is a subspace clustering algorithm based on the Hough transform.
class	COPAC<V extends NumberVector> COPAC is an algorithm to partition a database according to the correlation dimension of its objects and to then perform an arbitrary clustering algorithm over the partitions.
class	ERiC<V extends NumberVector> Performs correlation clustering on the data partitioned according to local correlation dimensionality and builds a hierarchy of correlation clusters that allows multiple inheritance from the clustering result.
class	FourC<V extends NumberVector> 4C identifies local subgroups of data objects sharing a uniform correlation.
class	HiCO<V extends NumberVector> Implementation of the HiCO algorithm, an algorithm for detecting hierarchies of correlation clusters.
class	LMCLUS Linear manifold clustering in high dimensional spaces by stochastic search.
class	ORCLUS<V extends NumberVector> ORCLUS: Arbitrarily ORiented projected CLUSter generation.

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

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

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

Uses of Reference in de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan

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

Modifier and Type	Class and Description
class	COPACNeighborPredicate<V extends NumberVector> COPAC neighborhood predicate.
class	EpsilonNeighborPredicate<O> The default DBSCAN and OPTICS neighbor predicate, using an epsilon-neighborhood.
class	ERiCNeighborPredicate<V extends NumberVector> ERiC neighborhood predicate.
class	FourCCorePredicate The PreDeCon core point predicate -- having at least minpts. neighbors, and a maximum preference dimensionality of lambda.
class	FourCNeighborPredicate<V extends NumberVector> 4C identifies local subgroups of data objects sharing a uniform correlation.
class	GeneralizedDBSCAN Generalized DBSCAN, density-based clustering with noise.
class	LSDBC<O extends NumberVector> Locally scaled Density Based Clustering.
class	MinPtsCorePredicate The DBSCAN default core point predicate -- having at least MinPtsCorePredicate.minpts neighbors.
class	PreDeConCorePredicate The PreDeCon core point predicate -- having at least minpts. neighbors, and a maximum preference dimensionality of lambda.
class	PreDeConNeighborPredicate<V extends NumberVector> Neighborhood predicate used by PreDeCon.

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

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

Modifier and Type	Class and Description
class	AbstractHDBSCAN<O,R extends Result> Abstract base class for HDBSCAN variations.
class	AGNES<O> Hierarchical Agglomerative Clustering (HAC) or Agglomerative Nesting (AGNES) is a classic hierarchical clustering algorithm.
class	AnderbergHierarchicalClustering<O> This is a modification of the classic AGNES algorithm for hierarchical clustering using a nearest-neighbor heuristic for acceleration.
class	CentroidLinkageMethod Centroid linkage clustering method, aka UPGMC: Unweighted Pair-Group Method using Centroids.
class	CLINK<O> CLINK algorithm for complete linkage.
class	GroupAverageLinkageMethod Group-average linkage clustering method.
class	HDBSCANLinearMemory<O> Linear memory implementation of HDBSCAN clustering.
interface	LinkageMethod Abstract interface for implementing a new linkage method into hierarchical clustering.
class	MedianLinkageMethod Median-linkage clustering method: Weighted pair group method using centroids (WPGMC).
class	SingleLinkageMethod Single-linkage clustering method.
class	SLINK<O> Implementation of the efficient Single-Link Algorithm SLINK of R.
class	SLINKHDBSCANLinearMemory<O> Linear memory implementation of HDBSCAN clustering based on SLINK.
class	WardLinkageMethod Ward's method clustering method.
class	WeightedAverageLinkageMethod Weighted average linkage clustering method.

Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical with annotations of type Reference

Modifier and Type	Field and Description
static Void	AGNES.ADDITIONAL_REFERENCE Additional historical reference for single-linkage.

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

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

Modifier and Type	Class and Description
class	HDBSCANHierarchyExtraction Extraction of simplified cluster hierarchies, as proposed in HDBSCAN.
class	SimplifiedHierarchyExtraction Extraction of simplified cluster hierarchies, as proposed in HDBSCAN.

Uses of Reference in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans

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

Modifier and Type	Class and Description
class	CLARA<V> Clustering Large Applications (CLARA) is a clustering method for large data sets based on PAM, partitioning around medoids (KMedoidsPAM) based on sampling.
class	KMeansBisecting<V extends NumberVector,M extends MeanModel> The bisecting k-means algorithm works by starting with an initial partitioning into two clusters, then repeated splitting of the largest cluster to get additional clusters.
class	KMeansElkan<V extends NumberVector> Elkan's fast k-means by exploiting the triangle inequality.
class	KMeansHamerly<V extends NumberVector> Hamerly's fast k-means by exploiting the triangle inequality.
class	KMeansLloyd<V extends NumberVector> The standard k-means algorithm, using Lloyd-style bulk iterations.
class	KMeansMacQueen<V extends NumberVector> The original k-means algorithm, using MacQueen style incremental updates; making this effectively an "online" (streaming) algorithm.
class	KMediansLloyd<V extends NumberVector> k-medians clustering algorithm, but using Lloyd-style bulk iterations instead of the more complicated approach suggested by Kaufman and Rousseeuw (see KMedoidsPAM instead).
class	KMedoidsPAM<V> The original PAM algorithm or k-medoids clustering, as proposed by Kaufman and Rousseeuw in "Partitioning Around Medoids".
class	XMeans<V extends NumberVector,M extends MeanModel> X-means: Extending K-means with Efficient Estimation on the Number of Clusters.

Uses of Reference in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization

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

Modifier and Type	Class and Description
class	KMeansPlusPlusInitialMeans<O> K-Means++ initialization for k-means.
class	PAMInitialMeans<O> PAM initialization for k-means (and of course, PAM).
class	RandomlyChosenInitialMeans<O> Initialize K-means by randomly choosing k existing elements as cluster centers.

Uses of Reference in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.quality

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

Modifier and Type	Class and Description
class	AbstractKMeansQualityMeasure<O extends NumberVector> Base class for evaluating clusterings by information criteria (such as AIC or BIC).
class	AkaikeInformationCriterion Akaike Information Criterion (AIC).
class	BayesianInformationCriterion Bayesian Information Criterion (BIC), also known as Schwarz criterion (SBC, SBIC) for the use with evaluating k-means results.
class	BayesianInformationCriterionZhao Different version of the BIC criterion.

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.quality with annotations of type Reference

Modifier and Type	Method and Description
static <V extends NumberVector> double	AbstractKMeansQualityMeasure.logLikelihood(Relation<V> relation, Clustering<? extends MeanModel> clustering, NumberVectorDistanceFunction<? super V> distanceFunction) Computes log likelihood of an entire clustering.
static <V extends NumberVector> double	AbstractKMeansQualityMeasure.logLikelihoodAlternate(Relation<V> relation, Clustering<? extends MeanModel> clustering, NumberVectorDistanceFunction<? super V> distanceFunction) Computes log likelihood of an entire clustering.

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

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

Modifier and Type	Class and Description
class	AbstractOPTICS<O> The OPTICS algorithm for density-based hierarchical clustering.
class	DeLiClu<NV extends NumberVector> DeliClu: Density-Based Hierarchical Clustering, a hierarchical algorithm to find density-connected sets in a database.
class	FastOPTICS<V extends NumberVector> FastOPTICS algorithm (Fast approximation of OPTICS) Note that this is not FOPTICS as in "Fuzzy OPTICS"!
class	OPTICSHeap<O> The OPTICS algorithm for density-based hierarchical clustering.
class	OPTICSList<O> The OPTICS algorithm for density-based hierarchical clustering.

Uses of Reference in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace

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

Modifier and Type	Class and Description
class	CLIQUE<V extends NumberVector> Implementation of the CLIQUE algorithm, a grid-based algorithm to identify dense clusters in subspaces of maximum dimensionality.
class	DiSH<V extends NumberVector> Algorithm for detecting subspace hierarchies.
class	DOC<V extends NumberVector> The DOC algorithm, and it's heuristic variant, FastDOC.
class	HiSC<V extends NumberVector> Implementation of the HiSC algorithm, an algorithm for detecting hierarchies of subspace clusters.
class	P3C<V extends NumberVector> P3C: A Robust Projected Clustering Algorithm.
class	PreDeCon<V extends NumberVector> PreDeCon computes clusters of subspace preference weighted connected points.
class	PROCLUS<V extends NumberVector> The PROCLUS algorithm, an algorithm to find subspace clusters in high dimensional spaces.
class	SUBCLU<V extends NumberVector> Implementation of the SUBCLU algorithm, an algorithm to detect arbitrarily shaped and positioned clusters in subspaces.

Uses of Reference in de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain

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

Modifier and Type	Class and Description
class	CenterOfMassMetaClustering<C extends Clustering<?>> Center-of-mass meta clustering reduces uncertain objects to their center of mass, then runs a vector-oriented clustering algorithm on this data set.
class	CKMeans Run k-means on the centers of each uncertain object.
class	FDBSCAN FDBSCAN is an adaption of DBSCAN for fuzzy (uncertain) objects.
class	FDBSCANNeighborPredicate Density-based Clustering of Applications with Noise and Fuzzy objects (FDBSCAN) is an Algorithm to find sets in a fuzzy database that are density-connected with minimum probability.
class	RepresentativeUncertainClustering Representative clustering of uncertain data.
class	UKMeans Uncertain K-Means clustering, using the average deviation from the center.

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

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

Modifier and Type	Class and Description
class	APRIORI The APRIORI algorithm for Mining Association Rules.
class	Eclat Eclat is a depth-first discovery algorithm for mining frequent itemsets.
class	FPGrowth FP-Growth is an algorithm for mining the frequent itemsets by using a compressed representation of the database called FPGrowth.FPTree.

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

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

Modifier and Type	Class and Description
class	COP<V extends NumberVector> Correlation outlier probability: Outlier Detection in Arbitrarily Oriented Subspaces Hans-Peter Kriegel, Peer Kröger, Erich Schubert, Arthur Zimek Outlier Detection in Arbitrarily Oriented Subspaces in: Proc.
class	DWOF<O> Algorithm to compute dynamic-window outlier factors in a database based on a specified parameter DWOF.Parameterizer.K_ID (-dwof.k).
class	GaussianUniformMixture<V extends NumberVector> Outlier detection algorithm using a mixture model approach.
class	OPTICSOF<O> Optics-OF outlier detection algorithm, an algorithm to find Local Outliers in a database based on ideas from OPTICSTypeAlgorithm clustering.
class	SimpleCOP<V extends NumberVector> Algorithm to compute local correlation outlier probability.

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

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

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

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

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

Modifier and Type	Class and Description
class	SilhouetteOutlierDetection<O> Outlier detection by using the Silhouette Coefficients.

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

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

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

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

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

Modifier and Type	Class and Description
class	ParallelKNNOutlier<O> Parallel implementation of KNN Outlier detection.
class	ParallelKNNWeightOutlier<O> Parallel implementation of KNN Weight Outlier detection.

Uses of Reference in de.lmu.ifi.dbs.elki.algorithm.outlier.lof

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

Modifier and Type	Class and Description
class	ALOCI<O extends NumberVector> Fast Outlier Detection Using the "approximate Local Correlation Integral".
class	COF<O> Connectivity-based outlier factor (COF).
class	FlexibleLOF<O> Flexible variant of the "Local Outlier Factor" algorithm.
class	INFLO<O> Influence Outliers using Symmetric Relationship (INFLO) using two-way search, is an outlier detection method based on LOF; but also using the reverse kNN.
class	KDEOS<O> Generalized Outlier Detection with Flexible Kernel Density Estimates.
class	LDF<O extends NumberVector> Outlier Detection with Kernel Density Functions.
class	LDOF<O> Computes the LDOF (Local Distance-Based Outlier Factor) for all objects of a Database.
class	LOCI<O> Fast Outlier Detection Using the "Local Correlation Integral".
class	LOF<O> Algorithm to compute density-based local outlier factors in a database based on a specified parameter LOF.Parameterizer.K_ID (-lof.k).
class	LoOP<O> LoOP: Local Outlier Probabilities Distance/density based algorithm similar to LOF to detect outliers, but with statistical methods to achieve better result stability.
class	SimplifiedLOF<O> A simplified version of the original LOF algorithm, which does not use the reachability distance, yielding less stable results on inliers.

Uses of Reference in de.lmu.ifi.dbs.elki.algorithm.outlier.lof.parallel

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

Modifier and Type	Class and Description
class	ParallelLOF<O> Parallel implementation of Local Outlier Factor using processors.
class	ParallelSimplifiedLOF<O> Parallel implementation of Simplified-LOF Outlier detection using processors.

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

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

Modifier and Type	Class and Description
class	FeatureBagging A simple ensemble method called "Feature bagging" for outlier detection.
class	HiCS<V extends NumberVector> Algorithm to compute High Contrast Subspaces for Density-Based Outlier Ranking.

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

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

Modifier and Type	Class and Description
class	CTLuGLSBackwardSearchAlgorithm<V extends NumberVector> GLS-Backward Search is a statistical approach to detecting spatial outliers.
class	CTLuMeanMultipleAttributes<N,O extends NumberVector> Mean Approach is used to discover spatial outliers with multiple attributes.
class	CTLuMedianAlgorithm<N> Median Algorithm of C.
class	CTLuMedianMultipleAttributes<N,O extends NumberVector> Median Approach is used to discover spatial outliers with multiple attributes.
class	CTLuMoranScatterplotOutlier<N> Moran scatterplot outliers, based on the standardized deviation from the local and global means.
class	CTLuRandomWalkEC<N> Spatial outlier detection based on random walks.
class	CTLuScatterplotOutlier<N> Scatterplot-outlier is a spatial outlier detection method that performs a linear regression of object attributes and their neighbors average value.
class	CTLuZTestOutlier<N> Detect outliers by comparing their attribute value to the mean and standard deviation of their neighborhood.
class	SLOM<N,O> SLOM: a new measure for local spatial outliers Reference: Sanjay Chawla and Pei Sun SLOM: a new measure for local spatial outliers in Knowledge and Information Systems 9(4), 412-429, 2006 This implementation works around some corner cases in SLOM, in particular when an object has none or a single neighbor only (albeit the results will still not be too useful then), which will result in divisions by zero.
class	SOF<N,O> The Spatial Outlier Factor (SOF) is a spatial LOF variation.
class	TrimmedMeanApproach<N> A Trimmed Mean Approach to Finding Spatial Outliers.

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

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

Modifier and Type	Class and Description
class	AbstractAggarwalYuOutlier<V extends NumberVector> Abstract base class for the sparse-grid-cell based outlier detection of Aggarwal and Yu.
class	AggarwalYuEvolutionary<V extends NumberVector> Evolutionary variant (EAFOD) of the high-dimensional outlier detection algorithm by Aggarwal and Yu.
class	AggarwalYuNaive<V extends NumberVector> BruteForce variant of the high-dimensional outlier detection algorithm by Aggarwal and Yu.
class	OutRankS1 OutRank: ranking outliers in high dimensional data.
class	OUTRES<V extends NumberVector> Adaptive outlierness for subspace outlier ranking (OUTRES).
class	SOD<V extends NumberVector> Subspace Outlier Degree.

Uses of Reference in de.lmu.ifi.dbs.elki.algorithm.outlier.svm

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

Modifier and Type	Class and Description
class	LibSVMOneClassOutlierDetection<V extends NumberVector> Outlier-detection using one-class support vector machines.

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

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

Modifier and Type	Class and Description
class	HopkinsStatisticClusteringTendency The Hopkins Statistic of Clustering Tendency measures the probability that a data set is generated by a uniform data distribution.

Uses of Reference in de.lmu.ifi.dbs.elki.application

Fields in de.lmu.ifi.dbs.elki.application with annotations of type Reference

Modifier and Type	Field and Description
static String	AbstractApplication.REFERENCE Information for citation and version.

Uses of Reference in de.lmu.ifi.dbs.elki.application.greedyensemble

Classes in de.lmu.ifi.dbs.elki.application.greedyensemble with annotations of type Reference

Modifier and Type	Class and Description
class	ComputeKNNOutlierScores<O extends NumberVector> Application that runs a series of kNN-based algorithms on a data set, for building an ensemble in a second step.
class	GreedyEnsembleExperiment Class to load an outlier detection summary file, as produced by ComputeKNNOutlierScores, and compute a naive ensemble for it.
class	VisualizePairwiseGainMatrix Class to load an outlier detection summary file, as produced by ComputeKNNOutlierScores, and compute a matrix with the pairwise gains.

Uses of Reference in de.lmu.ifi.dbs.elki.application.internal

Methods in de.lmu.ifi.dbs.elki.application.internal that return types with arguments of type Reference

Modifier and Type	Method and Description
private static List<Pair<Reference,TreeSet<Object>>>	DocumentReferences.sortedReferences()

Methods in de.lmu.ifi.dbs.elki.application.internal with parameters of type Reference

Modifier and Type	Method and Description
private static void	DocumentReferences.addReference(Object cls, Reference ref, List<Pair<Reference,TreeSet<Object>>> refs, Map<Reference,TreeSet<Object>> map)

Method parameters in de.lmu.ifi.dbs.elki.application.internal with type arguments of type Reference

Modifier and Type	Method and Description
private static void	DocumentReferences.addReference(Object cls, Reference ref, List<Pair<Reference,TreeSet<Object>>> refs, Map<Reference,TreeSet<Object>> map)
private static void	DocumentReferences.addReference(Object cls, Reference ref, List<Pair<Reference,TreeSet<Object>>> refs, Map<Reference,TreeSet<Object>> map)
private static Document	DocumentReferences.documentReferences(List<Pair<Reference,TreeSet<Object>>> refs)
private static void	DocumentReferences.documentReferencesWiki(List<Pair<Reference,TreeSet<Object>>> refs, PrintStream refstreamW)
private static void	DocumentReferences.inspectClass(Class<?> cls, List<Pair<Reference,TreeSet<Object>>> refs, Map<Reference,TreeSet<Object>> map)
private static void	DocumentReferences.inspectClass(Class<?> cls, List<Pair<Reference,TreeSet<Object>>> refs, Map<Reference,TreeSet<Object>> map)
private static void	DocumentReferences.inspectPackage(Package p, List<Pair<Reference,TreeSet<Object>>> refs, Map<Reference,TreeSet<Object>> map)
private static void	DocumentReferences.inspectPackage(Package p, List<Pair<Reference,TreeSet<Object>>> refs, Map<Reference,TreeSet<Object>> map)

Uses of Reference in de.lmu.ifi.dbs.elki.data.uncertain

Classes in de.lmu.ifi.dbs.elki.data.uncertain with annotations of type Reference

Modifier and Type	Class and Description
class	UnweightedDiscreteUncertainObject Unweighted implementation of discrete uncertain objects.
class	WeightedDiscreteUncertainObject Weighted version of discrete uncertain objects.

Uses of Reference in de.lmu.ifi.dbs.elki.database.ids.integer

Classes in de.lmu.ifi.dbs.elki.database.ids.integer with annotations of type Reference

Modifier and Type	Class and Description
(package private) class	IntegerDBIDArrayQuickSort Class to sort an integer DBID array, using a modified quicksort.

Uses of Reference in de.lmu.ifi.dbs.elki.datasource.filter.transform

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

Modifier and Type	Class and Description
class	LinearDiscriminantAnalysisFilter<V extends NumberVector> Linear Discriminant Analysis (LDA) / Fisher's linear discriminant.
class	PerturbationFilter<V extends NumberVector> A filter to perturb the values by adding micro-noise.

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

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

Modifier and Type	Class and Description
class	BrayCurtisDistanceFunction Bray-Curtis distance function / Sørensen–Dice coefficient for continuous spaces.
class	CanberraDistanceFunction Canberra distance function, a variation of Manhattan distance.
class	ClarkDistanceFunction Clark distance function for vector spaces.
class	Kulczynski1DistanceFunction Kulczynski similarity 1, in distance form.
class	LorentzianDistanceFunction Lorentzian distance function for vector spaces.

Methods in de.lmu.ifi.dbs.elki.distance.distancefunction with annotations of type Reference

Modifier and Type	Method and Description
(package private) static void	BrayCurtisDistanceFunction.secondReference() Dummy method, just to attach a second reference.
(package private) static void	BrayCurtisDistanceFunction.thirdReference() Dummy method, just to attach a third reference.

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

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

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

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

Methods in de.lmu.ifi.dbs.elki.distance.distancefunction.geo with annotations of type Reference

Modifier and Type	Method and Description
double	LngLatDistanceFunction.minDist(SpatialComparable mbr1, SpatialComparable mbr2)
double	LatLngDistanceFunction.minDist(SpatialComparable mbr1, SpatialComparable mbr2)
double	DimensionSelectingLatLngDistanceFunction.minDist(SpatialComparable mbr1, SpatialComparable mbr2)

Uses of Reference in de.lmu.ifi.dbs.elki.distance.distancefunction.histogram

Classes in de.lmu.ifi.dbs.elki.distance.distancefunction.histogram with annotations of type Reference

Modifier and Type	Class and Description
class	HistogramMatchDistanceFunction Distance function based on histogram matching, i.e.

Uses of Reference in de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic

Classes in de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic with annotations of type Reference

Modifier and Type	Class and Description
class	ChiSquaredDistanceFunction Chi-Squared distance function, symmetric version.
class	HellingerDistanceFunction Hellinger kernel / Hellinger distance are used with SIFT vectors, and also known as Bhattacharyya distance / coefficient.
class	JeffreyDivergenceDistanceFunction Jeffrey Divergence Distance for NumberVectors.
class	KullbackLeiblerDivergenceAsymmetricDistanceFunction Kullback-Leibler (asymmetric!)
class	KullbackLeiblerDivergenceReverseAsymmetricDistanceFunction Kullback-Leibler (asymmetric!)
class	SqrtJensenShannonDivergenceDistanceFunction The square root of Jensen-Shannon divergence is metric.

Uses of Reference in de.lmu.ifi.dbs.elki.distance.distancefunction.set

Classes in de.lmu.ifi.dbs.elki.distance.distancefunction.set with annotations of type Reference

Modifier and Type	Class and Description
class	HammingDistanceFunction Computes the Hamming distance of arbitrary vectors - i.e. counting, on how many places they differ.
class	JaccardSimilarityDistanceFunction<O extends FeatureVector<?>> A flexible extension of Jaccard similarity to non-binary vectors.

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

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

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

Uses of Reference in de.lmu.ifi.dbs.elki.distance.distancefunction.timeseries

Classes in de.lmu.ifi.dbs.elki.distance.distancefunction.timeseries with annotations of type Reference

Modifier and Type	Class and Description
class	DerivativeDTWDistanceFunction Derivative Dynamic Time Warping distance for numerical vectors.
class	DTWDistanceFunction Dynamic Time Warping distance (DTW) for numerical vectors.
class	EDRDistanceFunction Edit Distance on Real Sequence distance for numerical vectors.
class	ERPDistanceFunction Edit Distance With Real Penalty distance for numerical vectors.
class	LCSSDistanceFunction Longest Common Subsequence distance for numerical vectors.

Uses of Reference in de.lmu.ifi.dbs.elki.distance.similarityfunction

Classes in de.lmu.ifi.dbs.elki.distance.similarityfunction with annotations of type Reference

Modifier and Type	Class and Description
class	Kulczynski1SimilarityFunction Kulczynski similarity 1.
class	Kulczynski2SimilarityFunction Kulczynski similarity 2.

Uses of Reference in de.lmu.ifi.dbs.elki.distance.similarityfunction.cluster

Classes in de.lmu.ifi.dbs.elki.distance.similarityfunction.cluster with annotations of type Reference

Modifier and Type	Class and Description
class	ClusteringAdjustedRandIndexSimilarityFunction Measure the similarity of clusters via the Adjusted Rand Index.
class	ClusteringBCubedF1SimilarityFunction Measure the similarity of clusters via the BCubed F1 Index.
class	ClusteringFowlkesMallowsSimilarityFunction Measure the similarity of clusters via the Fowlkes-Mallows Index.
class	ClusteringRandIndexSimilarityFunction Measure the similarity of clusters via the Rand Index.
class	ClusterJaccardSimilarityFunction Measure the similarity of clusters via the Jaccard coefficient.

Uses of Reference in de.lmu.ifi.dbs.elki.evaluation.clustering

Classes in de.lmu.ifi.dbs.elki.evaluation.clustering with annotations of type Reference

Modifier and Type	Class and Description
class	BCubed BCubed measures.
class	EditDistance Edit distance measures.
class	Entropy Entropy based measures.
class	SetMatchingPurity Set matching purity measures.

Methods in de.lmu.ifi.dbs.elki.evaluation.clustering with annotations of type Reference

Modifier and Type	Method and Description
double	SetMatchingPurity.f1Measure() Get the set matching F1-Measure Steinbach, M. and Karypis, G. and Kumar, V.
double	SetMatchingPurity.fMeasureFirst() Get the Van Rijsbergen’s F measure (asymmetric) for first clustering E.
double	SetMatchingPurity.fMeasureSecond() Get the Van Rijsbergen’s F measure (asymmetric) for second clustering E.
double	PairCounting.fowlkesMallows() Computes the pair-counting Fowlkes-mallows (flat only, non-hierarchical!)
double	Entropy.normalizedVariationOfInformation() Get the normalized variation of information (normalized, 0 = equal) NVI = 1 - NMI_Joint Nguyen, X.
double	SetMatchingPurity.purity() Get the set matchings purity (first:second clustering) (normalized, 1 = equal)
double	PairCounting.randIndex() Computes the Rand index (RI).

Uses of Reference in de.lmu.ifi.dbs.elki.evaluation.clustering.internal

Classes in de.lmu.ifi.dbs.elki.evaluation.clustering.internal with annotations of type Reference

Modifier and Type	Class and Description
class	EvaluateCIndex<O> Compute the C-index of a data set.
class	EvaluateConcordantPairs<O> Compute the Gamma Criterion of a data set.
class	EvaluateDaviesBouldin Compute the Davies-Bouldin index of a data set.
class	EvaluatePBMIndex Compute the PBM of a data set Reference: M.
class	EvaluateSilhouette<O> Compute the silhouette of a data set.
class	EvaluateVarianceRatioCriteria<O> Compute the Variance Ratio Criteria of a data set.

Methods in de.lmu.ifi.dbs.elki.evaluation.clustering.internal with annotations of type Reference

Modifier and Type	Method and Description
double	EvaluateConcordantPairs.computeTau(long c, long d, double m, long wd, long bd) Compute the Tau correlation measure

Uses of Reference in de.lmu.ifi.dbs.elki.evaluation.clustering.pairsegments

Classes in de.lmu.ifi.dbs.elki.evaluation.clustering.pairsegments with annotations of type Reference

Modifier and Type	Class and Description
class	Segments Creates segments of two or more clusterings.

Uses of Reference in de.lmu.ifi.dbs.elki.evaluation.outlier

Classes in de.lmu.ifi.dbs.elki.evaluation.outlier with annotations of type Reference

Modifier and Type	Class and Description
class	OutlierSmROCCurve Smooth ROC curves are a variation of classic ROC curves that takes the scores into account.

Uses of Reference in de.lmu.ifi.dbs.elki.index.idistance

Classes in de.lmu.ifi.dbs.elki.index.idistance with annotations of type Reference

Modifier and Type	Class and Description
class	InMemoryIDistanceIndex<O> In-memory iDistance index, a metric indexing method using a reference point embedding.

Fields in de.lmu.ifi.dbs.elki.index.idistance with annotations of type Reference

Modifier and Type	Field and Description
static Void	InMemoryIDistanceIndex.SECOND_REFERENCE Second reference, for documentation generation.

Uses of Reference in de.lmu.ifi.dbs.elki.index.lsh.hashfamilies

Classes in de.lmu.ifi.dbs.elki.index.lsh.hashfamilies with annotations of type Reference

Modifier and Type	Class and Description
class	CosineHashFunctionFamily Hash function family to use with Cosine distance.
class	EuclideanHashFunctionFamily 2-stable hash function family for Euclidean distances.
class	ManhattanHashFunctionFamily 2-stable hash function family for Euclidean distances.

Uses of Reference in de.lmu.ifi.dbs.elki.index.lsh.hashfunctions

Classes in de.lmu.ifi.dbs.elki.index.lsh.hashfunctions with annotations of type Reference

Modifier and Type	Class and Description
class	CosineLocalitySensitiveHashFunction Random projection family to use with sparse vectors.
class	MultipleProjectionsLocalitySensitiveHashFunction LSH hash function for vector space data.

Uses of Reference in de.lmu.ifi.dbs.elki.index.preprocessed.fastoptics

Classes in de.lmu.ifi.dbs.elki.index.preprocessed.fastoptics with annotations of type Reference

Modifier and Type	Class and Description
class	RandomProjectedNeighborssAndDensities<V extends NumberVector> Random Projections used for computing neighbors and density estimates.

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

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

Modifier and Type	Class and Description
class	NaiveProjectedKNNPreprocessor<O extends NumberVector> Compute the approximate k nearest neighbors using 1 dimensional projections.
class	RandomSampleKNNPreprocessor<O> Class that computed the kNN only on a random sample.
class	SpacefillingKNNPreprocessor<O extends NumberVector> Compute the nearest neighbors approximatively using space filling curves.
class	SpacefillingMaterializeKNNPreprocessor<O extends NumberVector> Compute the nearest neighbors approximatively using space filling curves.

Uses of Reference in de.lmu.ifi.dbs.elki.index.projected

Classes in de.lmu.ifi.dbs.elki.index.projected with annotations of type Reference

Modifier and Type	Class and Description
class	PINN<O extends NumberVector> Projection-Indexed nearest-neighbors (PINN) is an index to retrieve the nearest neighbors in high dimensional spaces by using a random projection based index.

Uses of Reference in de.lmu.ifi.dbs.elki.index.tree.metrical.covertree

Classes in de.lmu.ifi.dbs.elki.index.tree.metrical.covertree with annotations of type Reference

Modifier and Type	Class and Description
class	CoverTree<O> Cover tree data structure (in-memory).

Uses of Reference in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mtree

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

Modifier and Type	Class and Description
class	MTree<O> MTree is a metrical index structure based on the concepts of the M-Tree.

Uses of Reference in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.insert

Classes in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.insert with annotations of type Reference

Modifier and Type	Class and Description
class	MinimumEnlargementInsert<O,N extends AbstractMTreeNode<O,N,E>,E extends MTreeEntry> Default insertion strategy for the M-tree.

Uses of Reference in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.split

Classes in de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.split with annotations of type Reference

Modifier and Type	Class and Description
class	MLBDistSplit<O,N extends AbstractMTreeNode<O,N,E>,E extends MTreeEntry> Encapsulates the required methods for a split of a node in an M-Tree.
class	MMRadSplit<O,N extends AbstractMTreeNode<O,N,E>,E extends MTreeEntry> Encapsulates the required methods for a split of a node in an M-Tree.
class	MRadSplit<O,N extends AbstractMTreeNode<O,N,E>,E extends MTreeEntry> Encapsulates the required methods for a split of a node in an M-Tree.
class	RandomSplit<O,N extends AbstractMTreeNode<O,N,E>,E extends MTreeEntry> Encapsulates the required methods for a split of a node in an M-Tree.

Uses of Reference in de.lmu.ifi.dbs.elki.index.tree.spatial.kd

Classes in de.lmu.ifi.dbs.elki.index.tree.spatial.kd with annotations of type Reference

Modifier and Type	Class and Description
class	MinimalisticMemoryKDTree<O extends NumberVector> Simple implementation of a static in-memory K-D-tree.
class	SmallMemoryKDTree<O extends NumberVector> Simple implementation of a static in-memory K-D-tree.

Uses of Reference in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.query

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

Modifier and Type	Class and Description
class	EuclideanRStarTreeKNNQuery<O extends NumberVector> Instance of a KNN query for a particular spatial index.
class	EuclideanRStarTreeRangeQuery<O extends NumberVector> Instance of a range query for a particular spatial index.
class	RStarTreeKNNQuery<O extends SpatialComparable> Instance of a KNN query for a particular spatial index.
class	RStarTreeRangeQuery<O extends SpatialComparable> Instance of a range query for a particular spatial index.

Uses of Reference in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rstar

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

Modifier and Type	Class and Description
class	RStarTree RStarTree is a spatial index structure based on the concepts of the R*-Tree.

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

Classes in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.bulk with annotations of type Reference

Modifier and Type	Class and Description
class	OneDimSortBulkSplit Simple bulk loading strategy by sorting the data along the first dimension.
class	SortTileRecursiveBulkSplit Sort-Tile-Recursive aims at tiling the data space with a grid-like structure for partitioning the dataset into the required number of buckets.
class	SpatialSortBulkSplit Bulk loading by spatially sorting the objects, then partitioning the sorted list appropriately.

Uses of Reference in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.insert

Classes in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.insert with annotations of type Reference

Modifier and Type	Class and Description
class	ApproximativeLeastOverlapInsertionStrategy The choose subtree method proposed by the R*-Tree with slightly better performance for large leaf sizes (linear approximation).
class	CombinedInsertionStrategy Use two different insertion strategies for directory and leaf nodes.
class	LeastEnlargementInsertionStrategy The default R-Tree insertion strategy: find rectangle with least volume enlargement.
class	LeastEnlargementWithAreaInsertionStrategy A slight modification of the default R-Tree insertion strategy: find rectangle with least volume enlargement, but choose least area on ties.
class	LeastOverlapInsertionStrategy The choose subtree method proposed by the R*-Tree for leaf nodes.

Uses of Reference in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.overflow

Classes in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.overflow with annotations of type Reference

Modifier and Type	Class and Description
class	LimitedReinsertOverflowTreatment Limited reinsertions, as proposed by the R*-Tree: For each real insert, allow reinsertions to happen only once per level.

Uses of Reference in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.reinsert

Classes in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.reinsert with annotations of type Reference

Modifier and Type	Class and Description
class	CloseReinsert Reinsert objects on page overflow, starting with close objects first (even when they will likely be inserted into the same page again!)
class	FarReinsert Reinsert objects on page overflow, starting with farther objects first (even when they will likely be inserted into the same page again!)

Uses of Reference in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.split

Classes in de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.split with annotations of type Reference

Modifier and Type	Class and Description
class	AngTanLinearSplit Line-time complexity split proposed by Ang and Tan.
class	GreeneSplit Quadratic-time complexity split as used by Diane Greene for the R-Tree.
class	RTreeLinearSplit Linear-time complexity greedy split as used by the original R-Tree.
class	RTreeQuadraticSplit Quadratic-time complexity greedy split as used by the original R-Tree.
class	TopologicalSplitter Encapsulates the required parameters for a topological split of a R*-Tree.

Uses of Reference in de.lmu.ifi.dbs.elki.index.vafile

Classes in de.lmu.ifi.dbs.elki.index.vafile with annotations of type Reference

Modifier and Type	Class and Description
class	DAFile Dimension approximation file, a one-dimensional part of the PartialVAFile.
class	PartialVAFile<V extends NumberVector> PartialVAFile.
class	VAFile<V extends NumberVector> Vector-approximation file (VAFile) Reference: Weber, R. and Blott, S.

Uses of Reference in de.lmu.ifi.dbs.elki.math

Classes in de.lmu.ifi.dbs.elki.math with annotations of type Reference

Modifier and Type	Class and Description
class	Mean Compute the mean using a numerically stable online algorithm.
class	MeanVariance Do some simple statistics (mean, variance) using a numerically stable online algorithm.
class	StatisticalMoments Track various statistical moments, including mean, variance, skewness and kurtosis.

Methods in de.lmu.ifi.dbs.elki.math with annotations of type Reference

Modifier and Type	Method and Description
void	MeanVariance.put(double val, double weight) Add data with a given weight.

Uses of Reference in de.lmu.ifi.dbs.elki.math.dimensionsimilarity

Classes in de.lmu.ifi.dbs.elki.math.dimensionsimilarity with annotations of type Reference

Modifier and Type	Class and Description
class	HiCSDimensionSimilarity Use the statistical tests as used by HiCS to arrange dimensions.
class	HSMDimensionSimilarity Compute the similarity of dimensions by using a hough transformation.
class	MCEDimensionSimilarity Compute dimension similarity by using a nested means discretization.
class	SlopeDimensionSimilarity Arrange dimensions based on the entropy of the slope spectrum.
class	SlopeInversionDimensionSimilarity Arrange dimensions based on the entropy of the slope spectrum.
class	SURFINGDimensionSimilarity Compute the similarity of dimensions using the SURFING score.

Methods in de.lmu.ifi.dbs.elki.math.dimensionsimilarity with annotations of type Reference

Modifier and Type	Method and Description
void	SURFINGDimensionSimilarity.computeDimensionSimilarites(Relation<? extends NumberVector> relation, DBIDs subset, DimensionSimilarityMatrix matrix)

Uses of Reference in de.lmu.ifi.dbs.elki.math.geodesy

Classes in de.lmu.ifi.dbs.elki.math.geodesy with annotations of type Reference

Modifier and Type	Class and Description
class	SphereUtil Class with utility functions for distance computations on the sphere.

Methods in de.lmu.ifi.dbs.elki.math.geodesy with annotations of type Reference

Modifier and Type	Method and Description
static double	SphereUtil.ellipsoidVincentyFormulaDeg(double f, double lat1, double lon1, double lat2, double lon2) Compute the approximate great-circle distance of two points.
static double	SphereUtil.ellipsoidVincentyFormulaRad(double f, double lat1, double lon1, double lat2, double lon2) Compute the approximate great-circle distance of two points.
static double	SphereUtil.haversineFormulaDeg(double lat1, double lon1, double lat2, double lon2) Compute the approximate great-circle distance of two points using the Haversine formula Complexity: 5 trigonometric functions, 2 sqrt.
static double	SphereUtil.haversineFormulaRad(double lat1, double lon1, double lat2, double lon2) Compute the approximate great-circle distance of two points using the Haversine formula Complexity: 5 trigonometric functions, 2 sqrt.
static double	SphereUtil.latlngMinDistDeg(double plat, double plng, double rminlat, double rminlng, double rmaxlat, double rmaxlng) Point to rectangle minimum distance.
static double	SphereUtil.latlngMinDistRad(double plat, double plng, double rminlat, double rminlng, double rmaxlat, double rmaxlng) Point to rectangle minimum distance.
static double	SphereUtil.latlngMinDistRadFull(double plat, double plng, double rminlat, double rminlng, double rmaxlat, double rmaxlng) Point to rectangle minimum distance.
static double	SphereUtil.sphericalVincentyFormulaDeg(double lat1, double lon1, double lat2, double lon2) Compute the approximate great-circle distance of two points.
static double	SphereUtil.sphericalVincentyFormulaRad(double lat1, double lon1, double lat2, double lon2) Compute the approximate great-circle distance of two points.

Uses of Reference in de.lmu.ifi.dbs.elki.math.geometry

Classes in de.lmu.ifi.dbs.elki.math.geometry with annotations of type Reference

Modifier and Type	Class and Description
class	GrahamScanConvexHull2D Classes to compute the convex hull of a set of points in 2D, using the classic Grahams scan.
class	PrimsMinimumSpanningTree Prim's algorithm for finding the minimum spanning tree.
class	SweepHullDelaunay2D Compute the Convex Hull and/or Delaunay Triangulation, using the sweep-hull approach of David Sinclair.

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

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

Modifier and Type	Class and Description
class	PCAFilteredAutotuningRunner Performs a self-tuning local PCA based on the covariance matrices of given objects.
class	RANSACCovarianceMatrixBuilder RANSAC based approach to a more robust covariance matrix computation.
class	WeightedCovarianceMatrixBuilder CovarianceMatrixBuilder with weights.

Methods in de.lmu.ifi.dbs.elki.math.linearalgebra.pca with annotations of type Reference

Modifier and Type	Method and Description
Matrix	RANSACCovarianceMatrixBuilder.processIds(DBIDs ids, Relation<? extends NumberVector> relation)

Uses of Reference in de.lmu.ifi.dbs.elki.math.linearalgebra.randomprojections

Classes in de.lmu.ifi.dbs.elki.math.linearalgebra.randomprojections with annotations of type Reference

Modifier and Type	Class and Description
class	AchlioptasRandomProjectionFamily Random projections as suggested by Dimitris Achlioptas.
class	CauchyRandomProjectionFamily Random projections using Cauchy distributions (1-stable).
class	GaussianRandomProjectionFamily Random projections using Cauchy distributions (1-stable).
class	RandomHyperplaneProjectionFamily Random hyperplane projection family.
class	RandomSubsetProjectionFamily Random projection family based on selecting random features.

Uses of Reference in de.lmu.ifi.dbs.elki.math.random

Classes in de.lmu.ifi.dbs.elki.math.random with annotations of type Reference

Modifier and Type	Class and Description
class	XorShift1024NonThreadsafeRandom Replacement for Java's Random class, using a different random number generation strategy.
class	XorShift64NonThreadsafeRandom Replacement for Java's Random class, using a different random number generation strategy.

Uses of Reference in de.lmu.ifi.dbs.elki.math.spacefillingcurves

Classes in de.lmu.ifi.dbs.elki.math.spacefillingcurves with annotations of type Reference

Modifier and Type	Class and Description
class	BinarySplitSpatialSorter Spatially sort the data set by repetitive binary splitting, circulating through the dimensions.
class	HilbertSpatialSorter Sort object along the Hilbert Space Filling curve by mapping them to their Hilbert numbers and sorting them.
class	PeanoSpatialSorter Bulk-load an R-tree index by presorting the objects with their position on the Peano curve.

Uses of Reference in de.lmu.ifi.dbs.elki.math.statistics

Classes in de.lmu.ifi.dbs.elki.math.statistics with annotations of type Reference

Modifier and Type	Class and Description
class	ProbabilityWeightedMoments Estimate the L-Moments of a sample.

Uses of Reference in de.lmu.ifi.dbs.elki.math.statistics.dependence

Classes in de.lmu.ifi.dbs.elki.math.statistics.dependence with annotations of type Reference

Modifier and Type	Class and Description
class	DistanceCorrelationDependenceMeasure Distance correlation.
class	HiCSDependenceMeasure Use the statistical tests as used by HiCS to measure dependence of variables.
class	HoeffdingsDDependenceMeasure Calculate Hoeffding's D as a measure of dependence.
class	HSMDependenceMeasure Compute the similarity of dimensions by using a hough transformation.
class	MCEDependenceMeasure Compute a mutual information based dependence measure using a nested means discretization, originally proposed for ordering axes in parallel coordinate plots.
class	SlopeDependenceMeasure Arrange dimensions based on the entropy of the slope spectrum.
class	SlopeInversionDependenceMeasure Arrange dimensions based on the entropy of the slope spectrum.
class	SURFINGDependenceMeasure Compute the similarity of dimensions using the SURFING score.

Methods in de.lmu.ifi.dbs.elki.math.statistics.dependence with annotations of type Reference

Modifier and Type	Method and Description
<A,B> double	SURFINGDependenceMeasure.dependence(NumberArrayAdapter<?,A> adapter1, A data1, NumberArrayAdapter<?,B> adapter2, B data2)

Uses of Reference in de.lmu.ifi.dbs.elki.math.statistics.distribution

Classes in de.lmu.ifi.dbs.elki.math.statistics.distribution with annotations of type Reference

Modifier and Type	Class and Description
class	HaltonUniformDistribution Halton sequences are a pseudo-uniform distribution.

Methods in de.lmu.ifi.dbs.elki.math.statistics.distribution with annotations of type Reference

Modifier and Type	Method and Description
protected static double	GammaDistribution.chisquaredProbitApproximation(double p, double nu, double g) Approximate probit for chi squared distribution Based on first half of algorithm AS 91 Reference: Algorithm AS 91: The percentage points of the $\chi^2$ distribution D.J.
private static double	PoissonDistribution.devianceTerm(double x, double np) Evaluate the deviance term of the saddle point approximation.
static double	GammaDistribution.digamma(double x) Compute the Psi / Digamma function Reference: J.
static double	PoissonDistribution.pmf(double x, int n, double p) Poisson probability mass function (PMF) for integer values.
static double	ChiSquaredDistribution.quantile(double x, double dof) Return the quantile function for this distribution Reference: Algorithm AS 91: The percentage points of the $\chi$^2 distribution D.J.
static double	GammaDistribution.quantile(double p, double k, double theta) Compute probit (inverse cdf) for Gamma distributions.
private static double	PoissonDistribution.stirlingError(double n) Calculates the Striling Error stirlerr(n) = ln(n!)
private static double	PoissonDistribution.stirlingError(int n) Calculates the Striling Error stirlerr(n) = ln(n!)

Uses of Reference in de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator

Classes in de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator with annotations of type Reference

Modifier and Type	Class and Description
class	CauchyMADEstimator Estimate Cauchy distribution parameters using Median and MAD.
class	EMGOlivierNorbergEstimator Naive distribution estimation using mean and sample variance.
class	ExponentialLMMEstimator Estimate the parameters of a Gamma Distribution, using the methods of L-Moments (LMM).
class	ExponentialMADEstimator Estimate Exponential distribution parameters using Median and MAD.
class	ExponentialMedianEstimator Estimate Exponential distribution parameters using Median and MAD.
class	GammaChoiWetteEstimator Estimate distribution parameters using the method by Choi and Wette.
class	GammaLMMEstimator Estimate the parameters of a Gamma Distribution, using the methods of L-Moments (LMM).
class	GammaMADEstimator Robust parameter estimation for the Gamma distribution.
class	GammaMOMEstimator Simple parameter estimation for the Gamma distribution.
class	GeneralizedExtremeValueLMMEstimator Estimate the parameters of a Generalized Extreme Value Distribution, using the methods of L-Moments (LMM).
class	GeneralizedLogisticAlternateLMMEstimator Estimate the parameters of a Generalized Logistic Distribution, using the methods of L-Moments (LMM).
class	GeneralizedParetoLMMEstimator Estimate the parameters of a Generalized Pareto Distribution (GPD), using the methods of L-Moments (LMM).
class	GumbelLMMEstimator Estimate the parameters of a Gumbel Distribution, using the methods of L-Moments (LMM).
class	GumbelMADEstimator Parameter estimation via median and median absolute deviation from median (MAD).
class	LaplaceMADEstimator Estimate Laplace distribution parameters using Median and MAD.
class	LaplaceMLEEstimator Estimate Laplace distribution parameters using Median and mean deviation from median.
class	LogGammaChoiWetteEstimator Estimate distribution parameters using the method by Choi and Wette.
class	LogisticLMMEstimator Estimate the parameters of a Logistic Distribution, using the methods of L-Moments (LMM).
class	LogisticMADEstimator Estimate Logistic distribution parameters using Median and MAD.
class	LogLogisticMADEstimator Estimate Logistic distribution parameters using Median and MAD.
class	LogNormalBilkovaLMMEstimator Alternate estimate the parameters of a log Gamma Distribution, using the methods of L-Moments (LMM) for the Generalized Normal Distribution.
class	LogNormalLMMEstimator Estimate the parameters of a log Normal Distribution, using the methods of L-Moments (LMM) for the Generalized Normal Distribution.
class	LogNormalLogMADEstimator Estimator using Medians.
class	NormalLMMEstimator Estimate the parameters of a normal distribution using the method of L-Moments (LMM).
class	NormalMADEstimator Estimator using Medians.
class	RayleighMADEstimator Estimate the parameters of a RayleighDistribution using the MAD.
class	SkewGNormalLMMEstimator Estimate the parameters of a skew Normal Distribution (Hoskin's Generalized Normal Distribution), using the methods of L-Moments (LMM).
class	UniformMADEstimator Estimate Uniform distribution parameters using Median and MAD.
class	WeibullLogMADEstimator Parameter estimation via median and median absolute deviation from median (MAD).

Uses of Reference in de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.meta

Classes in de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.meta with annotations of type Reference

Modifier and Type	Class and Description
class	WinsorisingEstimator<D extends Distribution> Winsorising or Georgization estimator.

Uses of Reference in de.lmu.ifi.dbs.elki.math.statistics.intrinsicdimensionality

Classes in de.lmu.ifi.dbs.elki.math.statistics.intrinsicdimensionality with annotations of type Reference

Modifier and Type	Class and Description
class	AggregatedHillEstimator Estimator using the weighted average of multiple hill estimators.
class	GEDEstimator Generalized Expansion Dimension for estimating the intrinsic dimensionality.
class	HillEstimator Hill estimator of the intrinsic dimensionality (maximum likelihood estimator for ID).
class	MOMEstimator Methods of moments estimator, using the first moment (i.e. average).
class	ZipfEstimator Zipf estimator (qq-estimator) of the intrinsic dimensionality.

Uses of Reference in de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions

Fields in de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions with annotations of type Reference

Modifier and Type	Field and Description
static double	BiweightKernelDensityFunction.CANONICAL_BANDWIDTH Canonical bandwidth: 35^(1/5)
static double	TriweightKernelDensityFunction.CANONICAL_BANDWIDTH Canonical bandwidth: (9450/143)^(1/5)
static double	UniformKernelDensityFunction.CANONICAL_BANDWIDTH Canonical bandwidth: (9/2)^(1/5)
static double	EpanechnikovKernelDensityFunction.CANONICAL_BANDWIDTH Canonical bandwidth: 15^(1/5)
static double	GaussianKernelDensityFunction.CANONICAL_BANDWIDTH Canonical bandwidth: (1./(4*pi))^(1/10)

Methods in de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions with annotations of type Reference

Modifier and Type	Method and Description
double	KernelDensityFunction.canonicalBandwidth() Get the canonical bandwidth for this kernel.

Uses of Reference in de.lmu.ifi.dbs.elki.math.statistics.tests

Classes in de.lmu.ifi.dbs.elki.math.statistics.tests with annotations of type Reference

Modifier and Type	Class and Description
class	AndersonDarlingTest Perform Anderson-Darling test for a Gaussian distribution.
class	StandardizedTwoSampleAndersonDarlingTest Perform a two-sample Anderson-Darling rank test, and standardize the statistic according to Scholz and Stephens.

Fields in de.lmu.ifi.dbs.elki.math.statistics.tests with annotations of type Reference

Modifier and Type	Field and Description
static Void	StandardizedTwoSampleAndersonDarlingTest.ADDITIONAL_REFERENCE_1 Additional reference -- Darling's note on this equation
static Void	StandardizedTwoSampleAndersonDarlingTest.ADDITIONAL_REFERENCE_2 More detailed discussion by Pettitt

Methods in de.lmu.ifi.dbs.elki.math.statistics.tests with annotations of type Reference

Modifier and Type	Method and Description
static double	AndersonDarlingTest.removeBiasNormalDistribution(double A2, int n) Remove bias from the Anderson-Darling statistic if the mean and standard deviation were estimated from the data, and a normal distribution was assumed.

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

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

Modifier and Type	Class and Description
class	KMLOutputHandler Class to handle KML output.

Uses of Reference in de.lmu.ifi.dbs.elki.utilities.datastructures.arrays

Classes in de.lmu.ifi.dbs.elki.utilities.datastructures.arrays with annotations of type Reference

Modifier and Type	Class and Description
class	IntegerArrayQuickSort Class to sort an int array, using a modified quicksort.

Uses of Reference in de.lmu.ifi.dbs.elki.utilities.documentation

Methods in de.lmu.ifi.dbs.elki.utilities.documentation that return Reference

Modifier and Type	Method and Description
static Reference	DocumentationUtil.getReference(Class<?> c) Get the reference annotation of a class, or null.

Uses of Reference in de.lmu.ifi.dbs.elki.utilities.scaling.outlier

Classes in de.lmu.ifi.dbs.elki.utilities.scaling.outlier with annotations of type Reference

Modifier and Type	Class and Description
class	COPOutlierScaling CDF based outlier score scaling.
class	HeDESNormalizationOutlierScaling Normalization used by HeDES
class	MinusLogGammaScaling Scaling that can map arbitrary values to a probability in the range of [0:1], by assuming a Gamma distribution on the data and evaluating the Gamma CDF.
class	MinusLogStandardDeviationScaling Scaling that can map arbitrary values to a probability in the range of [0:1].
class	MixtureModelOutlierScalingFunction Tries to fit a mixture model (exponential for inliers and gaussian for outliers) to the outlier score distribution.
class	MultiplicativeInverseScaling Scaling function to invert values basically by computing 1/x, but in a variation that maps the values to the [0:1] interval and avoiding division by 0.
class	OutlierGammaScaling Scaling that can map arbitrary values to a probability in the range of [0:1] by assuming a Gamma distribution on the values.
class	OutlierMinusLogScaling Scaling function to invert values by computing -1 * Math.log(x) Useful for example for scaling ABOD, but see MinusLogStandardDeviationScaling and MinusLogGammaScaling for more advanced scalings for this algorithm.
class	SigmoidOutlierScalingFunction Tries to fit a sigmoid to the outlier scores and use it to convert the values to probability estimates in the range of 0.0 to 1.0 Reference: J.
class	SqrtStandardDeviationScaling Scaling that can map arbitrary values to a probability in the range of [0:1].
class	StandardDeviationScaling Scaling that can map arbitrary values to a probability in the range of [0:1].

Methods in de.lmu.ifi.dbs.elki.utilities.scaling.outlier with annotations of type Reference

Modifier and Type	Method and Description
static void	COPOutlierScaling.secondReference() Secondary reference.

Uses of Reference in de.lmu.ifi.dbs.elki.visualization.visualizers.pairsegments

Classes in de.lmu.ifi.dbs.elki.visualization.visualizers.pairsegments with annotations of type Reference

Modifier and Type	Class and Description
class	CircleSegmentsVisualizer Visualizer to draw circle segments of clusterings and enable interactive selection of segments.

Uses of Reference in de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.density

Methods in de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.density with annotations of type Reference

Modifier and Type	Method and Description
private double[]	DensityEstimationOverlay.Instance.initializeBandwidth(double[][] data)

Uses of Reference in de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.outlier

Classes in de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.outlier with annotations of type Reference

Modifier and Type	Class and Description
class	BubbleVisualization Generates a SVG-Element containing bubbles.
class	COPVectorVisualization Visualize error vectors as produced by COP.

Uses of Reference in tutorial.clustering

Classes in tutorial.clustering with annotations of type Reference

Modifier and Type	Class and Description
class	NaiveAgglomerativeHierarchicalClustering3<O> This tutorial will step you through implementing a well known clustering algorithm, agglomerative hierarchical clustering, in multiple steps.
class	NaiveAgglomerativeHierarchicalClustering4<O> This tutorial will step you through implementing a well known clustering algorithm, agglomerative hierarchical clustering, in multiple steps.

Uses of Reference in tutorial.outlier

Classes in tutorial.outlier with annotations of type Reference

Modifier and Type	Class and Description
class	ODIN<O> Outlier detection based on the in-degree of the kNN graph.