Uses of Class
elki.utilities.documentation.Reference

Packages that use Reference

Package	Description
elki.algorithm	Miscellaneous algorithms.
elki.algorithm.statistics	Statistical analysis algorithms.
elki.application	Base classes for standalone applications.
elki.application.experiments	Packaged experiments to make them easy to reproduce.
elki.application.greedyensemble	Greedy ensembles for outlier detection.
elki.clustering	Clustering algorithms.
elki.clustering.affinitypropagation	Affinity Propagation (AP) clustering.
elki.clustering.biclustering	Biclustering algorithms.
elki.clustering.correlation	Correlation clustering algorithms.
elki.clustering.dbscan	DBSCAN and its generalizations.
elki.clustering.dbscan.parallel	Parallel versions of Generalized DBSCAN.
elki.clustering.dbscan.predicates	Neighbor and core predicated for Generalized DBSCAN.
elki.clustering.em	Expectation-Maximization clustering algorithm for Gaussian Mixture Modeling (GMM).
elki.clustering.em.models
elki.clustering.hierarchical	Hierarchical agglomerative clustering (HAC).
elki.clustering.hierarchical.birch	BIRCH clustering.
elki.clustering.hierarchical.extraction	Extraction of partitional clusterings from hierarchical results.
elki.clustering.hierarchical.linkage	Linkages for hierarchical clustering.
elki.clustering.kmeans	K-means clustering and variations.
elki.clustering.kmeans.initialization	Initialization strategies for k-means.
elki.clustering.kmeans.initialization.betula	Initialization methods for BIRCH-based k-means and EM clustering.
elki.clustering.kmeans.quality	Quality measures for k-Means results.
elki.clustering.kmeans.spherical	Spherical k-means clustering and variations.
elki.clustering.kmedoids	K-medoids clustering (PAM).
elki.clustering.kmedoids.initialization
elki.clustering.optics	OPTICS family of clustering algorithms.
elki.clustering.silhouette	Silhouette clustering algorithms.
elki.clustering.subspace	Axis-parallel subspace clustering algorithms.
elki.clustering.uncertain	Clustering algorithms for uncertain data.
elki.data.projection.random	Random projection families.
elki.database.ids.integer	Integer-based DBID implementation -- do not use directly - always use DBIDUtil.
elki.datasource.filter.transform	Data space transformations.
elki.distance	Distance functions for use within ELKI.
elki.distance.colorhistogram	Distance functions for color histograms.
elki.distance.geo	Geographic (earth) distance functions.
elki.distance.histogram	Distance functions for one-dimensional histograms.
elki.distance.probabilistic	Distance from probability theory, mostly divergences such as K-L-divergence, J-divergence, F-divergence, χ²-divergence, etc.
elki.distance.set	Distance functions for binary and set type data.
elki.distance.strings	Distance functions for strings.
elki.distance.timeseries	Distance functions designed for time series.
elki.evaluation.clustering	Evaluation of clustering results.
elki.evaluation.clustering.internal	Internal evaluation measures for clusterings.
elki.evaluation.clustering.pairsegments	Pair-segment analysis of multiple clusterings.
elki.evaluation.outlier	Evaluate an outlier score using a misclassification based cost model.
elki.evaluation.scores	Evaluation of rankings and scorings.
elki.index.laesa	Linear Approximating and Eliminating Search Algorithm (LAESA).
elki.index.lsh.hashfamilies	Hash function families for LSH.
elki.index.lsh.hashfunctions	Hash functions for LSH.
elki.index.preprocessed.fastoptics	Preprocessed index used by the FastOPTICS algorithm.
elki.index.preprocessed.knn	Indexes providing KNN and rKNN data.
elki.index.projected	Projected indexes for data.
elki.index.tree.betula.distance	Distance functions for BETULA and BIRCH.
elki.index.tree.metrical.covertree	Cover-tree variations.
elki.index.tree.metrical.mtreevariants.mtree	MTree.
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	Splitting strategies of nodes in an M-tree (and variants).
elki.index.tree.metrical.mtreevariants.strategies.split.distribution	Entry distribution strategies of nodes in an M-tree (and variants).
elki.index.tree.metrical.vptree
elki.index.tree.spatial.kd	K-d-tree and variants.
elki.index.tree.spatial.kd.split
elki.index.tree.spatial.rstarvariants.query	Queries on the R-Tree family of indexes: kNN and range queries.
elki.index.tree.spatial.rstarvariants.rstar	RStarTree.
elki.index.tree.spatial.rstarvariants.strategies.bulk	Packages for bulk-loading R*-trees.
elki.index.tree.spatial.rstarvariants.strategies.insert	Insertion strategies for R-trees.
elki.index.tree.spatial.rstarvariants.strategies.overflow	Overflow treatment strategies for R-trees.
elki.index.tree.spatial.rstarvariants.strategies.reinsert	Reinsertion strategies for R-trees.
elki.index.tree.spatial.rstarvariants.strategies.split	Splitting strategies for R-trees.
elki.index.vafile	Vector Approximation File.
elki.itemsetmining	Algorithms for frequent itemset mining such as APRIORI.
elki.itemsetmining.associationrules	Association rule mining.
elki.itemsetmining.associationrules.interest	Association rule interestingness measures.
elki.math	Mathematical operations and utilities used throughout the framework.
elki.math.geodesy	Functions for computing on the sphere / earth.
elki.math.geometry	Algorithms from computational geometry.
elki.math.linearalgebra	The linear algebra package provides classes and computational methods for operations on matrices and vectors.
elki.math.linearalgebra.pca	Principal Component Analysis (PCA) and eigenvector processing.
elki.math.spacefillingcurves	Space filling curves.
elki.math.statistics.dependence	Statistical measures of dependence, such as correlation.
elki.math.statistics.dependence.mcde	Tests tailored to be used with MCDEDependence.
elki.math.statistics.distribution	Standard distributions, with random generation functionalities.
elki.math.statistics.distribution.estimator	Estimators for statistical distributions.
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	Methods for estimating the intrinsic dimensionality.
elki.math.statistics.kernelfunctions	Kernel functions from statistics.
elki.math.statistics.tests	Statistical tests.
elki.outlier	Outlier detection algorithms.
elki.outlier.anglebased	Angle-based outlier detection algorithms.
elki.outlier.clustering	Clustering based outlier detection.
elki.outlier.density	Density-based outlier detection algorithms.
elki.outlier.distance	Distance-based outlier detection algorithms, such as DBOutlier and kNN.
elki.outlier.distance.parallel	Parallel implementations of distance-based outlier detectors.
elki.outlier.intrinsic	Outlier detection algorithms based on intrinsic dimensionality.
elki.outlier.lof	LOF family of outlier detection algorithms.
elki.outlier.lof.parallel	Parallelized variants of LOF.
elki.outlier.meta	Meta outlier detection algorithms: external scores, score rescaling.
elki.outlier.spatial	Spatial outlier detection algorithms.
elki.outlier.subspace	Subspace outlier detection methods.
elki.outlier.svm	Support-Vector-Machines for outlier detection.
elki.projection	Data projections (see also preprocessing filters for basic projections).
elki.result	Result types, representation and handling.
elki.similarity	Similarity functions.
elki.similarity.cluster	Similarity measures for comparing clusters.
elki.timeseries	Algorithms for change point detection in time series.
elki.utilities.datastructures	Basic memory structures such as heaps and object hierarchies.
elki.utilities.datastructures.arrays	Utilities for arrays: advanced sorting for primitive arrays.
elki.utilities.datastructures.unionfind	Union-find data structures.
elki.utilities.documentation	Documentation utilities: Annotations for Title, Description, Reference.
elki.utilities.random	Random number generation.
elki.utilities.scaling.outlier	Scaling of outlier scores, that require a statistical analysis of the occurring values.
elki.visualization.parallel3d	3DPC: 3D parallel coordinate plot visualization for ELKI.
elki.visualization.parallel3d.layout	Layouting algorithms for 3D parallel coordinate plots.
elki.visualization.projector	Projectors are responsible for finding appropriate projections for data relations.
elki.visualization.visualizers.pairsegments	Visualizers for inspecting cluster differences using pair counting segments.
elki.visualization.visualizers.scatterplot.density	Visualizers for data set density in a scatterplot projection.
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	Tutorials on implementing outlier detection methods in ELKI.

Packages with annotations of type Reference

Package	Description
elki.outlier.lof.parallel	Parallelized variants of LOF.

Uses of Reference in elki.algorithm

Classes in elki.algorithm with annotations of type Reference

Modifier and Type	Class	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 elki.algorithm.statistics

Classes in elki.algorithm.statistics with annotations of type Reference

Modifier and Type	Class	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 elki.application

Fields in elki.application with annotations of type Reference

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

Uses of Reference in elki.application.experiments

Classes in elki.application.experiments with annotations of type Reference

Modifier and Type	Class	Description
class	VisualizeGeodesicDistances	Visualization function for Cross-track, Along-track, and minimum distance function.

Uses of Reference in elki.application.greedyensemble

Classes in elki.application.greedyensemble with annotations of type Reference

Modifier and Type	Class	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 elki.clustering

Classes in elki.clustering with annotations of type Reference

Modifier and Type	Class	Description
class	BetulaLeafPreClustering	BETULA-based clustering algorithm that simply treats the leafs of the CFTree as clusters.
class	CanopyPreClustering<O>	Canopy pre-clustering is a simple preprocessing step for clustering.
class	CFSFDP<O>	Clustering by fast search and find of density peaks (CFSFDP) is a density-based clustering method similar to mean-shift clustering.
class	Leader<O>	Leader clustering algorithm.
class	NaiveMeanShiftClustering<V extends NumberVector>	Mean-shift based clustering algorithm.
class	SNNClustering<O>	Shared nearest neighbor clustering.

Uses of Reference in elki.clustering.affinitypropagation

Classes in elki.clustering.affinitypropagation with annotations of type Reference

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

Uses of Reference in elki.clustering.biclustering

Classes in elki.clustering.biclustering with annotations of type Reference

Modifier and Type	Class	Description
class	ChengAndChurch	Cheng and Church biclustering.

Uses of Reference in elki.clustering.correlation

Classes in elki.clustering.correlation with annotations of type Reference

Modifier and Type	Class	Description
class	CASH	The CASH algorithm is a subspace clustering algorithm based on the Hough transform.
class	COPAC	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	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	4C identifies local subgroups of data objects sharing a uniform correlation.
class	HiCO	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	ORCLUS: Arbitrarily ORiented projected CLUSter generation.

Uses of Reference in elki.clustering.dbscan

Classes in elki.clustering.dbscan with annotations of type Reference

Modifier and Type	Class	Description
class	GeneralizedDBSCAN	Generalized DBSCAN, density-based clustering with noise.
class	GriDBSCAN<V extends NumberVector>	Using Grid for Accelerating Density-Based Clustering.
class	LSDBC<O extends NumberVector>	Locally Scaled Density Based Clustering.

Uses of Reference in elki.clustering.dbscan.parallel

Classes in elki.clustering.dbscan.parallel with annotations of type Reference

Modifier and Type	Class	Description
class	ParallelGeneralizedDBSCAN	Parallel version of DBSCAN clustering.

Uses of Reference in elki.clustering.dbscan.predicates

Classes in elki.clustering.dbscan.predicates with annotations of type Reference

Modifier and Type	Class	Description
class	COPACNeighborPredicate	COPAC neighborhood predicate.
class	EpsilonNeighborPredicate<O>	The default DBSCAN and OPTICS neighbor predicate, using an epsilon-neighborhood.
class	ERiCNeighborPredicate	ERiC neighborhood predicate.
class	FourCCorePredicate	The 4C core point predicate.
class	FourCNeighborPredicate	4C identifies local subgroups of data objects sharing a uniform correlation.
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	Neighborhood predicate used by PreDeCon.
class	SimilarityNeighborPredicate<O>	The DBSCAN neighbor predicate for a Similarity, using all neighbors with a minimum similarity.

Uses of Reference in elki.clustering.em

Classes in elki.clustering.em with annotations of type Reference

Modifier and Type	Class	Description
class	BetulaGMM	Clustering by expectation maximization (EM-Algorithm), also known as Gaussian Mixture Modeling (GMM), with optional MAP regularization.
class	BetulaGMMWeighted	Clustering by expectation maximization (EM-Algorithm), also known as Gaussian Mixture Modeling (GMM), with optional MAP regularization.
class	KDTreeEM	Clustering by expectation maximization (EM-Algorithm), also known as Gaussian Mixture Modeling (GMM), calculated on a kd-tree.

Uses of Reference in elki.clustering.em.models

Classes in elki.clustering.em.models with annotations of type Reference

Modifier and Type	Interface	Description
interface	BetulaClusterModel	Models usable in Betula EM clustering.
interface	BetulaClusterModelFactory<M extends BetulaClusterModel>	Factory for initializing the EM models.
class	BetulaDiagonalGaussianModelFactory	Factory for EM with multivariate gaussian models using diagonal matrixes.
class	BetulaMultivariateGaussianModelFactory	Factory for EM with multivariate gaussian models using diagonal matrixes.
class	BetulaSphericalGaussianModelFactory	Factory for EM with multivariate gaussian models using a single variance.

Uses of Reference in elki.clustering.hierarchical

Classes in elki.clustering.hierarchical with annotations of type Reference

Modifier and Type	Class	Description
class	AbstractHDBSCAN<O>	Abstract base class for HDBSCAN variations.
class	Anderberg<O>	This is a modification of the classic AGNES algorithm for hierarchical clustering using a nearest-neighbor heuristic for acceleration.
class	CLINK<O>	CLINK algorithm for complete linkage.
class	HACAM<O>	Hierarchical Agglomerative Clustering Around Medoids (HACAM) is a hierarchical clustering method that merges the clusters with the smallest distance to the medoid of the union.
class	HDBSCANLinearMemory<O>	Linear memory implementation of HDBSCAN clustering.
class	LinearMemoryNNChain<O extends NumberVector>	NNchain clustering algorithm with linear memory, for particular linkages (that can be aggregated) and numerical vector data only.
class	MiniMaxAnderberg<O>	This is a modification of the classic MiniMax algorithm for hierarchical clustering using a nearest-neighbor heuristic for acceleration.
class	OPTICSToHierarchical	Convert a OPTICS ClusterOrder to a hierarchical clustering.
class	SLINK<O>	Implementation of the efficient Single-Link Algorithm SLINK of R.
class	SLINKHDBSCANLinearMemory<O>	Linear memory implementation of HDBSCAN clustering based on SLINK.

Uses of Reference in elki.clustering.hierarchical.birch

Classes in elki.clustering.hierarchical.birch with annotations of type Reference

Modifier and Type	Class	Description
class	AverageInterclusterDistance	Average intercluster distance.
class	AverageIntraclusterDistance	Average intracluster distance.
class	CentroidEuclideanDistance	Centroid Euclidean distance.
class	CentroidManhattanDistance	Centroid Manhattan Distance
class	DiameterCriterion	Average Radius (R) criterion.
class	RadiusCriterion	Average Radius (R) criterion.
class	VarianceIncreaseDistance	Variance increase distance.

Uses of Reference in elki.clustering.hierarchical.extraction

Classes in elki.clustering.hierarchical.extraction with annotations of type Reference

Modifier and Type	Class	Description
class	ClustersWithNoiseExtraction	Extraction of a given number of clusters with a minimum size, and noise.
class	SimplifiedHierarchyExtraction	Extraction of simplified cluster hierarchies, as proposed in HDBSCAN.

Uses of Reference in elki.clustering.hierarchical.linkage

Classes in elki.clustering.hierarchical.linkage with annotations of type Reference

Modifier and Type	Class	Description
class	CentroidLinkage	Centroid linkage — Unweighted Pair-Group Method using Centroids (UPGMC).
class	FlexibleBetaLinkage	Flexible-beta linkage as proposed by Lance and Williams.
class	GroupAverageLinkage	Group-average linkage clustering method (UPGMA).
interface	Linkage	Abstract interface for implementing a new linkage method into hierarchical clustering.
class	MedianLinkage	Median-linkage — weighted pair group method using centroids (WPGMC).
class	SingleLinkage	Single-linkage ("minimum") clustering method.
class	WeightedAverageLinkage	Weighted average linkage clustering method (WPGMA).

Uses of Reference in elki.clustering.kmeans

Classes in elki.clustering.kmeans with annotations of type Reference

Modifier and Type	Class	Description
class	BetulaLloydKMeans	BIRCH/BETULA-based clustering algorithm that simply treats the leafs of the CFTree as clusters.
class	BisectingKMeans<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	CompareMeans<V extends NumberVector>	Compare-Means: Accelerated k-means by exploiting the triangle inequality and pairwise distances of means to prune candidate means.
class	ElkanKMeans<V extends NumberVector>	Elkan's fast k-means by exploiting the triangle inequality.
class	ExponionKMeans<V extends NumberVector>	Newlings's Exponion k-means algorithm, exploiting the triangle inequality.
class	GMeans<V extends NumberVector,M extends MeanModel>	G-Means extends K-Means and estimates the number of centers with Anderson Darling Test. Implemented as specialization of XMeans.
class	HamerlyKMeans<V extends NumberVector>	Hamerly's fast k-means by exploiting the triangle inequality.
class	HartiganWongKMeans<V extends NumberVector>	Hartigan and Wong k-means clustering.
class	KMeansMinusMinus<V extends NumberVector>	k-means--: A Unified Approach to Clustering and Outlier Detection.
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 PAM instead).
class	MacQueenKMeans<V extends NumberVector>	The original k-means algorithm, using MacQueen style incremental updates; making this effectively an "online" (streaming) algorithm.
class	ShallotKMeans<V extends NumberVector>	Borgelt's Shallot k-means algorithm, exploiting the triangle inequality.
class	SimplifiedElkanKMeans<V extends NumberVector>	Simplified version of Elkan's k-means by exploiting the triangle inequality.
class	SortMeans<V extends NumberVector>	Sort-Means: Accelerated k-means by exploiting the triangle inequality and pairwise distances of means to prune candidate means (with sorting).
class	XMeans<V extends NumberVector,M extends MeanModel>	X-means: Extending K-means with Efficient Estimation on the Number of Clusters.
class	YinYangKMeans<V extends NumberVector>	Yin-Yang k-Means Clustering.

Uses of Reference in elki.clustering.kmeans.initialization

Classes in elki.clustering.kmeans.initialization with annotations of type Reference

Modifier and Type	Class	Description
class	AFKMC2	AFK-MC² initialization
class	FirstK<O>	Initialize K-means by using the first k objects as initial means.
class	KMC2	K-MC² initialization
class	KMeansPlusPlus<O>	K-Means++ initialization for k-means.
class	RandomNormalGenerated	Initialize k-means by generating random vectors (normal distributed with \(N(\mu,\sigma)\) in each dimension).
class	RandomUniformGenerated	Initialize k-means by generating random vectors (uniform, within the value range of the data set).
class	SampleKMeans<V extends NumberVector>	Initialize k-means by running k-means on a sample of the data set only.
class	SphericalAFKMC2	Spherical K-Means++ initialization with markov chains.
class	SphericalKMeansPlusPlus<O>	Spherical K-Means++ initialization for k-means.

Uses of Reference in elki.clustering.kmeans.initialization.betula

Classes in elki.clustering.kmeans.initialization.betula with annotations of type Reference

Modifier and Type	Class	Description
class	CFKPlusPlusLeaves	K-Means++-like initialization for BETULA k-means, treating the leaf clustering features as a flat list, and called "leaves" in the publication.
class	CFKPlusPlusTree	Initialize K-means by following tree paths weighted by their variance contribution.
class	CFKPlusPlusTrunk	Trunk strategy for initializing k-means with BETULA: only the nodes up to a particular level are considered for k-means++ style initialization.
class	CFRandomlyChosen	Initialize K-means by randomly choosing k existing elements as initial cluster centers for Clustering Features.
class	CFWeightedRandomlyChosen	Initialize K-means by randomly choosing k existing elements as initial cluster centers for Clustering Features.
class	InterclusterWeight	Initialization via n2 * D2²(cf1, cf2), which supposedly is closes to the idea of k-means++ initialization.
class	SquaredEuclideanWeight	Use the squared Euclidean distance only for distance measurement.
class	VarianceWeight	Variance-based weighting scheme for k-means clustering with BETULA.

Uses of Reference in elki.clustering.kmeans.quality

Classes in elki.clustering.kmeans.quality with annotations of type Reference

Modifier and Type	Class	Description
class	AkaikeInformationCriterionXMeans	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	BayesianInformationCriterionXMeans	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 elki.clustering.kmeans.quality with annotations of type Reference

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

Uses of Reference in elki.clustering.kmeans.spherical

Classes in elki.clustering.kmeans.spherical with annotations of type Reference

Modifier and Type	Class	Description
class	EuclideanSphericalElkanKMeans<V extends NumberVector>	Elkan's fast k-means by exploiting the triangle inequality in the corresponding Euclidean space.
class	EuclideanSphericalHamerlyKMeans<V extends NumberVector>	A spherical k-Means algorithm based on Hamerly's fast k-means by exploiting the triangle inequality in the corresponding Euclidean space.
class	EuclideanSphericalSimplifiedElkanKMeans<V extends NumberVector>	A spherical k-Means algorithm based on Hamerly's fast k-means by exploiting the triangle inequality in the corresponding Euclidean space.
class	SphericalKMeans<V extends NumberVector>	The standard spherical k-means algorithm.

Uses of Reference in elki.clustering.kmedoids

Classes in elki.clustering.kmedoids with annotations of type Reference

Modifier and Type	Class	Description
class	CLARANS<O>	CLARANS: a method for clustering objects for spatial data mining is inspired by PAM (partitioning around medoids, PAM) and CLARA and also based on sampling.
class	FastCLARA<V>	Clustering Large Applications (CLARA) with the FastPAM improvements, to increase scalability in the number of clusters.
class	FastCLARANS<V>	A faster variation of CLARANS, that can explore O(k) as many swaps at a similar cost by considering all medoids for each candidate non-medoid.
class	FasterCLARA<O>	Clustering Large Applications (CLARA) with the FastPAM improvements, to increase scalability in the number of clusters.
class	FasterPAM<O>	Variation of FastPAM that eagerly performs any swap that yields an improvement during an iteration.
class	FastPAM<O>	FastPAM: An improved version of PAM, that is usually O(k) times faster.
class	FastPAM1<O>	FastPAM1: A version of PAM that is O(k) times faster, i.e., now in O((n-k)²).
class	ReynoldsPAM<O>	The Partitioning Around Medoids (PAM) algorithm with some additional optimizations proposed by Reynolds et al.

Uses of Reference in elki.clustering.kmedoids.initialization

Classes in elki.clustering.kmedoids.initialization with annotations of type Reference

Modifier and Type	Class	Description
class	GreedyG<O>	Initialization method for k-medoids that combines the Greedy (PAM BUILD) with "alternate" refinement steps.
class	LAB<O>	Linear approximative BUILD (LAB) initialization for FastPAM (and k-means).
class	ParkJun<O>	Initialization method proposed by Park and Jun.

Uses of Reference in elki.clustering.optics

Classes in elki.clustering.optics with annotations of type Reference

Modifier and Type	Class	Description
class	AbstractOPTICS<O>	The OPTICS algorithm for density-based hierarchical clustering.
class	DeLiClu<V extends NumberVector>	DeliClu: Density-Based Hierarchical Clustering
class	FastOPTICS<V extends NumberVector>	FastOPTICS algorithm (Fast approximation of 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 elki.clustering.silhouette

Classes in elki.clustering.silhouette with annotations of type Reference

Modifier and Type	Class	Description
class	FasterMSC<O>	Fast and Eager Medoid Silhouette Clustering.
class	FastMSC<O>	Fast Medoid Silhouette Clustering.

Uses of Reference in elki.clustering.subspace

Classes in elki.clustering.subspace with annotations of type Reference

Modifier and Type	Class	Description
class	CLIQUE	Implementation of the CLIQUE algorithm, a grid-based algorithm to identify dense clusters in subspaces of maximum dimensionality.
class	DiSH	Algorithm for detecting subspace hierarchies.
class	DOC	DOC is a sampling based subspace clustering algorithm.
class	FastDOC	The heuristic variant of the DOC algorithm, FastDOC
class	HiSC	Implementation of the HiSC algorithm, an algorithm for detecting hierarchies of subspace clusters.
class	P3C	P3C: A Robust Projected Clustering Algorithm.
class	PreDeCon	PreDeCon computes clusters of subspace preference weighted connected points.
class	PROCLUS	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 elki.clustering.uncertain

Classes in elki.clustering.uncertain with annotations of type Reference

Modifier and Type	Class	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 elki.data.projection.random

Classes in elki.data.projection.random with annotations of type Reference

Modifier and Type	Class	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	RandomSubsetProjectionFamily	Random projection family based on selecting random features.
class	SimplifiedRandomHyperplaneProjectionFamily	Random hyperplane projection family.

Uses of Reference in elki.database.ids.integer

Classes in elki.database.ids.integer with annotations of type Reference

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

Uses of Reference in elki.datasource.filter.transform

Classes in elki.datasource.filter.transform with annotations of type Reference

Modifier and Type	Class	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 elki.distance

Classes in elki.distance with annotations of type Reference

Modifier and Type	Class	Description
class	CanberraDistance	Canberra distance function, a variation of Manhattan distance.
class	ClarkDistance	Clark distance function for vector spaces.
class	MahalanobisDistance	Mahalanobis quadratic form distance for feature vectors.

Uses of Reference in elki.distance.colorhistogram

Classes in elki.distance.colorhistogram with annotations of type Reference

Modifier and Type	Class	Description
class	HistogramIntersectionDistance	Intersection distance for color histograms.
class	HSBHistogramQuadraticDistance	Distance function for HSB color histograms based on a quadratic form and color similarity.
class	RGBHistogramQuadraticDistance	Distance function for RGB color histograms based on a quadratic form and color similarity.

Uses of Reference in elki.distance.geo

Classes in elki.distance.geo with annotations of type Reference

Modifier and Type	Class	Description
class	DimensionSelectingLatLngDistance	Distance function for 2D vectors in Latitude, Longitude form.
class	LatLngDistance	Distance function for 2D vectors in Latitude, Longitude form.
class	LngLatDistance	Distance function for 2D vectors in Longitude, Latitude form.

Uses of Reference in elki.distance.histogram

Classes in elki.distance.histogram with annotations of type Reference

Modifier and Type	Class	Description
class	HistogramMatchDistance	Distance function based on histogram matching, i.e., Manhattan distance on the cumulative density function.

Uses of Reference in elki.distance.probabilistic

Classes in elki.distance.probabilistic with annotations of type Reference

Modifier and Type	Class	Description
class	ChiSquaredDistance	χ² distance function, symmetric version.
class	KullbackLeiblerDivergenceAsymmetricDistance	Kullback-Leibler divergence, also known as relative entropy, information deviation, or just KL-distance (albeit asymmetric).
class	KullbackLeiblerDivergenceReverseAsymmetricDistance	Kullback-Leibler divergence, also known as relative entropy, information deviation or just KL-distance (albeit asymmetric).
class	SqrtJensenShannonDivergenceDistance	The square root of Jensen-Shannon divergence is a metric.
class	TriangularDiscriminationDistance	Triangular Discrimination has relatively tight upper and lower bounds to the Jensen-Shannon divergence, but is much less expensive.
class	TriangularDistance	Triangular Distance has relatively tight upper and lower bounds to the (square root of the) Jensen-Shannon divergence, but is much less expensive.

Uses of Reference in elki.distance.set

Classes in elki.distance.set with annotations of type Reference

Modifier and Type	Class	Description
class	HammingDistance	Computes the Hamming distance of arbitrary vectors - i.e. counting, on how many places they differ.
class	JaccardSimilarityDistance	A flexible extension of Jaccard similarity to non-binary vectors.

Uses of Reference in elki.distance.strings

Classes in elki.distance.strings with annotations of type Reference

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

Uses of Reference in elki.distance.timeseries

Classes in elki.distance.timeseries with annotations of type Reference

Modifier and Type	Class	Description
class	DerivativeDTWDistance	Derivative Dynamic Time Warping distance for numerical vectors.
class	DTWDistance	Dynamic Time Warping distance (DTW) for numerical vectors.
class	EDRDistance	Edit Distance on Real Sequence distance for numerical vectors.
class	ERPDistance	Edit Distance With Real Penalty distance for numerical vectors.
class	LCSSDistance	Longest Common Subsequence distance for numerical vectors.

Uses of Reference in elki.evaluation.clustering

Classes in elki.evaluation.clustering with annotations of type Reference

Modifier and Type	Class	Description
class	EditDistance	Edit distance measures.
class	MaximumMatchingAccuracy	Calculates the accuracy of a clustering based on the maximum set matching found by the Hungarian algorithm.
class	PairSetsIndex	The Pair Sets Index calculates an index based on the maximum matching of relative cluster sizes by the Hungarian algorithm.
class	SetMatchingPurity	Set matching purity measures.

Methods in elki.evaluation.clustering with annotations of type Reference

Modifier and Type	Method	Description
double	PairCounting.adjustedRandIndex()	Computes the adjusted Rand index (ARI).
double	SetMatchingPurity.f1Measure()	Get the set matching F1-Measure
double	SetMatchingPurity.fMeasureFirst()	Get the Van Rijsbergen’s F measure (asymmetric) for first clustering
double	SetMatchingPurity.fMeasureSecond()	Get the Van Rijsbergen’s F measure (asymmetric) for second clustering
double	PairCounting.fowlkesMallows()	Computes the pair-counting Fowlkes-mallows (flat only, non-hierarchical!)
double	Entropy.geometricNMI()	Get the geometric mean normalized mutual information (using the square root).
double	PairCounting.jaccard()	Computes the Jaccard index
double	Entropy.jointNMI()	Get the joint-normalized mutual information.
double	Entropy.maxNMI()	Get the max-normalized mutual information.
double	Entropy.minNMI()	Get the min-normalized mutual information.
long	PairCounting.mirkin()	Computes the Mirkin index, aka Equivalence Mismatch Distance.
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 elki.evaluation.clustering.internal

Classes in elki.evaluation.clustering.internal with annotations of type Reference

Modifier and Type	Class	Description
class	CIndex<O>	Compute the C-index of a data set.
class	ConcordantPairsGammaTau	Compute the Gamma Criterion of a data set.
class	DaviesBouldinIndex	Compute the Davies-Bouldin index of a data set.
class	DBCV<O>	Compute the Density-Based Clustering Validation Index.
class	PBMIndex	Compute the PBM index of a clustering
class	Silhouette<O>	Compute the silhouette of a data set.
class	VarianceRatioCriterion	Compute the Variance Ratio Criterion of a data set, also known as Calinski-Harabasz index.

Methods in elki.evaluation.clustering.internal with annotations of type Reference

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

Uses of Reference in elki.evaluation.clustering.pairsegments

Classes in elki.evaluation.clustering.pairsegments with annotations of type Reference

Modifier and Type	Class	Description
class	ClusterPairSegmentAnalysis	Evaluate clustering results by building segments for their pairs: shared pairs and differences.
class	Segments	Creates segments of two or more clusterings.

Uses of Reference in elki.evaluation.outlier

Classes in elki.evaluation.outlier with annotations of type Reference

Modifier and Type	Class	Description
class	OutlierPrecisionRecallCurve	Compute a curve containing the precision values for an outlier detection method.
class	OutlierPrecisionRecallGainCurve	Compute a curve containing the precision gain and revall gain values for an outlier detection method.
class	OutlierSmROCCurve	Smooth ROC curves are a variation of classic ROC curves that takes the scores into account.

Uses of Reference in elki.evaluation.scores

Classes in elki.evaluation.scores with annotations of type Reference

Modifier and Type	Class	Description
class	DCGEvaluation	Discounted Cumulative Gain.
class	NDCGEvaluation	Normalized Discounted Cumulative Gain.
class	PRGCEvaluation	Compute the area under the precision-recall-gain curve

Uses of Reference in elki.index.laesa

Classes in elki.index.laesa with annotations of type Reference

Modifier and Type	Class	Description
class	LAESA<O>	Linear Approximating and Eliminating Search Algorithm

Uses of Reference in elki.index.lsh.hashfamilies

Classes in elki.index.lsh.hashfamilies with annotations of type Reference

Modifier and Type	Class	Description
class	EuclideanHashFunctionFamily	2-stable hash function family for Euclidean distances.
class	ManhattanHashFunctionFamily	2-stable hash function family for Euclidean distances.

Uses of Reference in elki.index.lsh.hashfunctions

Classes in elki.index.lsh.hashfunctions with annotations of type Reference

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

Uses of Reference in elki.index.preprocessed.fastoptics

Classes in elki.index.preprocessed.fastoptics with annotations of type Reference

Modifier and Type	Class	Description
class	RandomProjectedNeighborsAndDensities	Random Projections used for computing neighbors and density estimates.

Uses of Reference in elki.index.preprocessed.knn

Classes in elki.index.preprocessed.knn with annotations of type Reference

Modifier and Type	Class	Description
class	NaiveProjectedKNNPreprocessor<O extends NumberVector>	Compute the approximate k nearest neighbors using 1 dimensional projections.
class	NNDescent<O>	NN-descent (also known as KNNGraph) is an approximate nearest neighbor search algorithm beginning with a random sample, then iteratively refining this sample until.
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 elki.index.projected

Classes in elki.index.projected with annotations of type Reference

Modifier and Type	Class	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 elki.index.tree.betula.distance

Classes in elki.index.tree.betula.distance with annotations of type Reference

Modifier and Type	Class	Description
class	BIRCHAverageInterclusterDistance	Average intercluster distance.
class	BIRCHAverageIntraclusterDistance	Average intracluster distance.
class	BIRCHRadiusDistance	Average Radius (R) criterion.
class	BIRCHVarianceIncreaseDistance	Variance increase distance.

Uses of Reference in elki.index.tree.metrical.covertree

Classes in elki.index.tree.metrical.covertree with annotations of type Reference

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

Uses of Reference in elki.index.tree.metrical.mtreevariants.mtree

Classes in elki.index.tree.metrical.mtreevariants.mtree with annotations of type Reference

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

Uses of Reference in elki.index.tree.metrical.mtreevariants.strategies.insert

Classes in elki.index.tree.metrical.mtreevariants.strategies.insert with annotations of type Reference

Modifier and Type	Class	Description
class	MinimumEnlargementInsert<N extends AbstractMTreeNode<?,N,E>,E extends MTreeEntry>	Minimum enlargement insert - default insertion strategy for the M-tree.

Uses of Reference in elki.index.tree.metrical.mtreevariants.strategies.split

Classes in elki.index.tree.metrical.mtreevariants.strategies.split with annotations of type Reference

Modifier and Type	Class	Description
class	MLBDistSplit<E extends MTreeEntry,N extends AbstractMTreeNode<?,N,E>>	Encapsulates the required methods for a split of a node in an M-Tree.
class	MMRadSplit<E extends MTreeEntry,N extends AbstractMTreeNode<?,N,E>>	Encapsulates the required methods for a split of a node in an M-Tree.
class	MRadSplit<E extends MTreeEntry,N extends AbstractMTreeNode<?,N,E>>	Encapsulates the required methods for a split of a node in an M-Tree.
class	MSTSplit<E extends MTreeEntry,N extends AbstractMTreeNode<?,N,E>>	Splitting algorithm using the minimum spanning tree (MST), as proposed by the Slim-Tree variant.
class	RandomSplit<E extends MTreeEntry,N extends AbstractMTreeNode<?,N,E>>	Encapsulates the required methods for a split of a node in an M-Tree.

Uses of Reference in elki.index.tree.metrical.mtreevariants.strategies.split.distribution

Classes in elki.index.tree.metrical.mtreevariants.strategies.split.distribution with annotations of type Reference

Modifier and Type	Class	Description
class	BalancedDistribution	Balanced entry distribution strategy of the M-tree.
class	GeneralizedHyperplaneDistribution	Generalized hyperplane entry distribution strategy of the M-tree.

Uses of Reference in elki.index.tree.metrical.vptree

Classes in elki.index.tree.metrical.vptree with annotations of type Reference

Modifier and Type	Class	Description
class	GNAT<O>	Geometric Near-neighbor Access Tree (GNAT), also known as Multi Vantage Point Tree or MVP-Tree.
class	VPTree<O>	Vantage Point Tree with no additional information

Uses of Reference in elki.index.tree.spatial.kd

Classes in elki.index.tree.spatial.kd with annotations of type Reference

Modifier and Type	Class	Description
class	MemoryKDTree<O extends NumberVector>	Implementation of a static in-memory K-D-tree.
class	MemoryKDTree.KDTreeKNNSearcher	kNN query for the k-d-tree.
class	MemoryKDTree.KDTreeRangeSearcher	Range query for the k-d-tree.
class	MinimalisticMemoryKDTree<O extends NumberVector>	Simple implementation of a static in-memory K-D-tree.
class	MinimalisticMemoryKDTree.KDTreeKNNSearcher	kNN query for the k-d-tree.
class	MinimalisticMemoryKDTree.KDTreeRangeSearcher	Range query for the k-d-tree.
class	SmallMemoryKDTree<O extends NumberVector>	Simple implementation of a static in-memory K-D-tree.
class	SmallMemoryKDTree.KDTreeKNNSearcher	kNN query for the k-d-tree.
class	SmallMemoryKDTree.KDTreeRangeSearcher	Range query for the k-d-tree.

Uses of Reference in elki.index.tree.spatial.kd.split

Classes in elki.index.tree.spatial.kd.split with annotations of type Reference

Modifier and Type	Class	Description
class	MeanVarianceSplit	Split on the median of the axis with the largest variance.
class	MedianVarianceSplit	Split on the median of the axis with the largest variance.

Uses of Reference in elki.index.tree.spatial.rstarvariants.query

Classes in elki.index.tree.spatial.rstarvariants.query with annotations of type Reference

Modifier and Type	Class	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	RStarTreeKNNSearcher<O extends SpatialComparable>	Instance of a KNN query for a particular spatial index.
class	RStarTreeRangeSearcher<O extends SpatialComparable>	Instance of a range query for a particular spatial index.

Uses of Reference in elki.index.tree.spatial.rstarvariants.rstar

Classes in elki.index.tree.spatial.rstarvariants.rstar with annotations of type Reference

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

Uses of Reference in elki.index.tree.spatial.rstarvariants.strategies.bulk

Classes in elki.index.tree.spatial.rstarvariants.strategies.bulk with annotations of type Reference

Modifier and Type	Class	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 elki.index.tree.spatial.rstarvariants.strategies.insert

Classes in elki.index.tree.spatial.rstarvariants.strategies.insert with annotations of type Reference

Modifier and Type	Class	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 elki.index.tree.spatial.rstarvariants.strategies.overflow

Classes in elki.index.tree.spatial.rstarvariants.strategies.overflow with annotations of type Reference

Modifier and Type	Class	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 elki.index.tree.spatial.rstarvariants.strategies.reinsert

Classes in elki.index.tree.spatial.rstarvariants.strategies.reinsert with annotations of type Reference

Modifier and Type	Class	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 elki.index.tree.spatial.rstarvariants.strategies.split

Classes in elki.index.tree.spatial.rstarvariants.strategies.split with annotations of type Reference

Modifier and Type	Class	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 elki.index.vafile

Classes in elki.index.vafile with annotations of type Reference

Modifier and Type	Class	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)

Uses of Reference in elki.itemsetmining

Classes in elki.itemsetmining with annotations of type Reference

Modifier and Type	Class	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 elki.itemsetmining.associationrules

Classes in elki.itemsetmining.associationrules with annotations of type Reference

Modifier and Type	Class	Description
class	AssociationRuleGeneration	Association rule generation from frequent itemsets

Uses of Reference in elki.itemsetmining.associationrules.interest

Classes in elki.itemsetmining.associationrules.interest with annotations of type Reference

Modifier and Type	Class	Description
class	AddedValue	Added value (AV) interestingness measure: \( \text{confidence}(X \rightarrow Y) - \text{support}(Y) = P(Y|X)-P(Y) \).
class	CertaintyFactor	Certainty factor (CF; Loevinger) interestingness measure. \( \tfrac{\text{confidence}(X \rightarrow Y) - \text{support}(Y)}{\text{support}(\neg Y)} \).
class	Confidence	Confidence interestingness measure, \( \tfrac{\text{support}(X \cup Y)}{\text{support}(X)} = \tfrac{P(X \cap Y)}{P(X)}=P(Y|X) \).
class	Conviction	Conviction interestingness measure: \(\frac{P(X) P(\neg Y)}{P(X\cap\neg Y)}\).
class	Cosine	Cosine interestingness measure, \(\tfrac{\text{support}(A\cup B)}{\sqrt{\text{support}(A)\text{support}(B)}} =\tfrac{P(A\cap B)}{\sqrt{P(A)P(B)}}\).
class	JMeasure	J-Measure interestingness measure.
class	Klosgen	Klösgen interestingness measure.
class	Leverage	Leverage interestingness measure.
class	Lift	Lift interestingness measure.
class	SebagSchonauer	Sebag Schonauer interestingness measure.

Uses of Reference in elki.math

Methods in elki.math with annotations of type Reference

Modifier and Type	Method	Description
static double	Mean.highPrecision(double... data)	Static helper function, with extra precision

Uses of Reference in elki.math.geodesy

Classes in elki.math.geodesy with annotations of type Reference

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

Methods in elki.math.geodesy with annotations of type Reference

Modifier and Type	Method	Description
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.haversineFormulaRad(double lat1, double lon1, double lat2, double lon2)	Compute the approximate great-circle distance of two points using the Haversine formula
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.sphericalVincentyFormulaRad(double lat1, double lon1, double lat2, double lon2)	Compute the approximate great-circle distance of two points.

Uses of Reference in elki.math.geometry

Classes in elki.math.geometry with annotations of type Reference

Modifier and Type	Class	Description
class	AlphaShape	Compute the alpha-shape of a point set, using Delaunay triangulation.
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 elki.math.linearalgebra

Methods in elki.math.linearalgebra with annotations of type Reference

Modifier and Type	Method	Description
static double	VMath.mahalanobisDistance(double[][] B, double[] a, double[] c)	Matrix multiplication, (a-c)T * B * (a-c)

Uses of Reference in elki.math.linearalgebra.pca

Classes in elki.math.linearalgebra.pca with annotations of type Reference

Modifier and Type	Class	Description
class	AutotuningPCA	Performs a self-tuning local PCA based on the covariance matrices of given objects.
class	WeightedCovarianceMatrixBuilder	CovarianceMatrixBuilder with weights.

Uses of Reference in elki.math.spacefillingcurves

Classes in elki.math.spacefillingcurves with annotations of type Reference

Modifier and Type	Class	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 elki.math.statistics.dependence

Classes in elki.math.statistics.dependence with annotations of type Reference

Modifier and Type	Class	Description
class	DCor	Distance correlation.
class	HoeffdingsD	Calculate Hoeffding's D as a measure of dependence.
class	HoughSpaceMeasure	HSM: Compute the "interestingness" of dimension connections using the Hough transformation.
class	MaximumConditionalEntropy	Compute a mutual information based dependence measure using a nested means discretization, originally proposed for ordering axes in parallel coordinate plots.
class	MCDEDependence	Implementation of bivariate Monte Carlo Density Estimation as described in
class	SlopeDependence	Arrange dimensions based on the entropy of the slope spectrum.
class	SlopeInversionDependence	Arrange dimensions based on the entropy of the slope spectrum.

Uses of Reference in elki.math.statistics.dependence.mcde

Classes in elki.math.statistics.dependence.mcde with annotations of type Reference

Modifier and Type	Class	Description
class	MWPTest	Implementation of Mann-Whitney U test returning the p-value (not the test statistic, thus MWP) for MCDEDependence.

Uses of Reference in elki.math.statistics.distribution

Classes in elki.math.statistics.distribution with annotations of type Reference

Modifier and Type	Class	Description
class	HaltonUniformDistribution	Halton sequences are a pseudo-uniform distribution.
class	SkewGeneralizedNormalDistribution	Generalized normal distribution by adding a skew term, similar to lognormal distributions.

Methods in elki.math.statistics.distribution with annotations of type Reference

Modifier and Type	Method	Description
static double	NormalDistribution.cdf(double x, double mu, double sigma)	Cumulative probability density function (CDF) of a normal distribution.
protected static double	GammaDistribution.chisquaredProbitApproximation(double p, double nu, double g)	Approximate probit for chi squared distribution
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
static double	NormalDistribution.erfc(double x)	Complementary error function for Gaussian distributions = Normal distributions.
static double	NormalDistribution.erfcinv(double y)	Inverse error function.
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
static double	GammaDistribution.quantile(double p, double k, double theta)	Compute probit (inverse cdf) for Gamma distributions.
static double	NormalDistribution.standardNormalCDF(double x)	Cumulative probability density function (CDF) of a normal distribution.
private static double	PoissonDistribution.stirlingError(double n)	Calculates the Stirling Error
private static double	PoissonDistribution.stirlingError(int n)	Calculates the Stirling Error

Uses of Reference in elki.math.statistics.distribution.estimator

Classes in elki.math.statistics.distribution.estimator with annotations of type Reference

Modifier and Type	Class	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	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	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 elki.math.statistics.distribution.estimator.meta

Classes in elki.math.statistics.distribution.estimator.meta with annotations of type Reference

Modifier and Type	Class	Description
class	WinsorizingEstimator<D extends Distribution>	Winsorizing or Georgization estimator.

Uses of Reference in elki.math.statistics.intrinsicdimensionality

Classes in elki.math.statistics.intrinsicdimensionality with annotations of type Reference

Modifier and Type	Class	Description
class	ABIDEstimator	Angle based intrinsic dimensionality (ABID) estimator.
class	AggregatedHillEstimator	Estimator using the weighted average of multiple hill estimators.
class	ALIDEstimator	ALID estimator of the intrinsic dimensionality (maximum likelihood estimator for ID using auxiliary distances).
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	RABIDEstimator	Raw angle based intrinsic dimensionality (RABID) estimator.
class	RVEstimator	Regularly Varying Functions estimator of the intrinsic dimensionality
class	TightLIDEstimator	TightLID Estimator (TLE) of the intrinsic dimensionality (maximum likelihood estimator for ID using auxiliary distances).

Uses of Reference in elki.math.statistics.kernelfunctions

Fields in elki.math.statistics.kernelfunctions with annotations of type Reference

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

Methods in elki.math.statistics.kernelfunctions with annotations of type Reference

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

Uses of Reference in elki.math.statistics.tests

Classes in elki.math.statistics.tests with annotations of type Reference

Modifier and Type	Class	Description
class	AndersonDarlingTest	Perform Anderson-Darling test for a Gaussian distribution.

Methods in elki.math.statistics.tests with annotations of type Reference

Modifier and Type	Method	Description
static double	AndersonDarlingTest.pValueCase0(double A2, int n)	Calculates the p-value for an Anderson Darling statistic in the case where both center and variance are known.
static double	AndersonDarlingTest.pValueCase3(double A2)	Calculates the p-value for an Anderson Darling statistic in the case where both center and variance are unknown.
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.
static double	AndersonDarlingTest.removeBiasNormalDistributionDAgostino(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 elki.outlier

Classes in elki.outlier with annotations of type Reference

Modifier and Type	Class	Description
class	COP<V extends NumberVector>	Correlation outlier probability: Outlier Detection in Arbitrarily Oriented Subspaces
class	DWOF<O>	Algorithm to compute dynamic-window outlier factors in a database based on a specified parameter k, which specifies the number of the neighbors to be considered during the calculation of the DWOF score.
class	GaussianUniformMixture	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 elki.outlier.anglebased

Classes in elki.outlier.anglebased with annotations of type Reference

Modifier and Type	Class	Description
class	ABOD<V extends NumberVector>	Angle-Based Outlier Detection / Angle-Based Outlier Factor.
class	FastABOD<V extends NumberVector>	Fast-ABOD (approximateABOF) version of Angle-Based Outlier Detection / Angle-Based Outlier Factor.
class	LBABOD<V extends NumberVector>	LB-ABOD (lower-bound) version of Angle-Based Outlier Detection / Angle-Based Outlier Factor.

Uses of Reference in elki.outlier.clustering

Classes in elki.outlier.clustering with annotations of type Reference

Modifier and Type	Class	Description
class	CBLOF<O extends NumberVector>	Cluster-based local outlier factor (CBLOF).
class	GLOSH	Global-Local Outlier Scores from Hierarchies.
class	KMeansMinusMinusOutlierDetection	k-means--: A Unified Approach to Clustering and Outlier Detection.
class	SilhouetteOutlierDetection<O>	Outlier detection by using the Silhouette Coefficients.

Uses of Reference in elki.outlier.density

Classes in elki.outlier.density with annotations of type Reference

Modifier and Type	Class	Description
class	HySortOD	Hypercube-Based Outlier Detection.
class	IsolationForest	Isolation-Based Anomaly Detection.

Uses of Reference in elki.outlier.distance

Classes in elki.outlier.distance with annotations of type Reference

Modifier and Type	Class	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
class	KNNDD<O>	Nearest Neighbor Data Description.
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	LocalIsolationCoefficient<O>	The Local Isolation Coefficient is the sum of the kNN distance and the average distance 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.
class	SOS<O>	Stochastic Outlier Selection.

Methods in elki.outlier.distance with annotations of type Reference

Modifier and Type	Method	Description
protected static double	SOS.estimateInitialBeta(DBIDRef ignore, DoubleDBIDListIter it, double perplexity)	Estimate beta from the distances in a row.

Uses of Reference in elki.outlier.distance.parallel

Classes in elki.outlier.distance.parallel with annotations of type Reference

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

Uses of Reference in elki.outlier.intrinsic

Classes in elki.outlier.intrinsic with annotations of type Reference

Modifier and Type	Class	Description
class	IDOS<O>	Intrinsic Dimensional Outlier Detection in High-Dimensional Data.
class	ISOS<O>	Intrinsic Stochastic Outlier Selection.
class	LID<O>	Use intrinsic dimensionality for outlier detection.

Uses of Reference in elki.outlier.lof

Classes in elki.outlier.lof with annotations of type Reference

Modifier and Type	Class	Description
class	ALOCI<V 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.k.
class	LoOP<O>	LoOP: Local Outlier Probabilities
class	SimplifiedLOF<O>	A simplified version of the original LOF algorithm, which does not use the reachability distance, yielding less stable results on inliers.
class	VarianceOfVolume<O extends SpatialComparable>	Variance of Volume for outlier detection.

Uses of Reference in elki.outlier.lof.parallel

Classes in elki.outlier.lof.parallel with annotations of type Reference

Modifier and Type	Class	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 elki.outlier.meta

Classes in elki.outlier.meta with annotations of type Reference

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

Uses of Reference in elki.outlier.spatial

Classes in elki.outlier.spatial with annotations of type Reference

Modifier and Type	Class	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<O>	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
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 elki.outlier.subspace

Classes in elki.outlier.subspace with annotations of type Reference

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

Uses of Reference in elki.outlier.svm

Classes in elki.outlier.svm with annotations of type Reference

Modifier and Type	Class	Description
class	LibSVMOneClassOutlierDetection<V extends NumberVector>	Outlier-detection using one-class support vector machines.
class	OCSVM<V>	Outlier-detection using one-class support vector machines.
class	SVDD<V>	Support Vector Data Description for outlier detection.

Uses of Reference in elki.projection

Classes in elki.projection with annotations of type Reference

Modifier and Type	Class	Description
class	BarnesHutTSNE<O>	t-SNE using Barnes-Hut-Approximation.
class	GaussianAffinityMatrixBuilder<O>	Compute the affinity matrix for SNE and tSNE using a Gaussian distribution with a constant sigma.
class	IntrinsicNearestNeighborAffinityMatrixBuilder<O>	Build sparse affinity matrix using the nearest neighbors only, adjusting for intrinsic dimensionality.
class	NearestNeighborAffinityMatrixBuilder<O>	Build sparse affinity matrix using the nearest neighbors only.
class	PerplexityAffinityMatrixBuilder<O>	Compute the affinity matrix for SNE and tSNE.
class	SNE<O>	Stochastic Neighbor Embedding is a projection technique designed for visualization that tries to preserve the nearest neighbor structure.
class	TSNE<O>	t-Stochastic Neighbor Embedding is a projection technique designed for visualization that tries to preserve the nearest neighbor structure.

Uses of Reference in elki.result

Classes in elki.result with annotations of type Reference

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

Uses of Reference in elki.similarity

Classes in elki.similarity with annotations of type Reference

Modifier and Type	Class	Description
class	Kulczynski1Similarity	Kulczynski similarity 1.
class	Kulczynski2Similarity	Kulczynski similarity 2.

Uses of Reference in elki.similarity.cluster

Classes in elki.similarity.cluster with annotations of type Reference

Modifier and Type	Class	Description
class	ClusteringAdjustedRandIndexSimilarity	Measure the similarity of clusters via the Adjusted Rand Index.
class	ClusteringBCubedF1Similarity	Measure the similarity of clusters via the BCubed F1 Index.
class	ClusteringFowlkesMallowsSimilarity	Measure the similarity of clusters via the Fowlkes-Mallows Index.
class	ClusteringRandIndexSimilarity	Measure the similarity of clusters via the Rand Index.
class	ClusterJaccardSimilarity	Measure the similarity of clusters via the Jaccard coefficient.

Uses of Reference in elki.timeseries

Classes in elki.timeseries with annotations of type Reference

Modifier and Type	Class	Description
class	SigniTrendChangeDetection	Signi-Trend detection algorithm applies to a single time-series.

Uses of Reference in elki.utilities.datastructures

Classes in elki.utilities.datastructures with annotations of type Reference

Modifier and Type	Class	Description
class	KuhnMunkresStern	A version of Kuhn-Munkres inspired by the implementation of Kevin L.
class	KuhnMunkresWong	Kuhn-Munkres optimal matching (aka the Hungarian algorithm), supposedly in a modern variant.

Uses of Reference in elki.utilities.datastructures.arrays

Classes in elki.utilities.datastructures.arrays with annotations of type Reference

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

Uses of Reference in elki.utilities.datastructures.unionfind

Classes in elki.utilities.datastructures.unionfind with annotations of type Reference

Modifier and Type	Class	Description
class	WeightedQuickUnionInteger	Union-find algorithm for primitive integers, with optimizations.
class	WeightedQuickUnionRangeDBIDs	Union-find algorithm for DBIDRange only, with optimizations.
class	WeightedQuickUnionStaticDBIDs	Union-find algorithm for StaticDBIDs, with optimizations.

Uses of Reference in elki.utilities.documentation

Methods in elki.utilities.documentation that return Reference

Modifier and Type	Method	Description
Reference[]	value()	References of the class / field / method.

Uses of Reference in elki.utilities.random

Classes in elki.utilities.random with annotations of type Reference

Modifier and Type	Class	Description
class	Xoroshiro128NonThreadsafeRandom	Replacement for Java's Random class, using a different random number generation strategy.
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.

Methods in elki.utilities.random with annotations of type Reference

Modifier and Type	Method	Description
int	Xoroshiro128NonThreadsafeRandom.nextInt(int n)	Returns a pseudorandom, uniformly distributed int value between 0 (inclusive) and the specified value (exclusive), drawn from this random number generator's sequence.
int	XorShift1024NonThreadsafeRandom.nextInt(int n)	Returns a pseudorandom, uniformly distributed int value between 0 (inclusive) and the specified value (exclusive), drawn from this random number generator's sequence.
int	XorShift64NonThreadsafeRandom.nextInt(int n)	Returns a pseudorandom, uniformly distributed int value between 0 (inclusive) and the specified value (exclusive), drawn from this random number generator's sequence.

Uses of Reference in elki.utilities.scaling.outlier

Classes in elki.utilities.scaling.outlier with annotations of type Reference

Modifier and Type	Class	Description
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	MixtureModelOutlierScaling	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 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 -log(x)
class	SigmoidOutlierScaling	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
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].

Uses of Reference in elki.visualization.parallel3d

Classes in elki.visualization.parallel3d with annotations of type Reference

Modifier and Type	Class	Description
class	OpenGL3DParallelCoordinates<O extends NumberVector>	Simple JOGL2 based parallel coordinates visualization.
class	Parallel3DRenderer<O extends NumberVector>	Renderer for 3D parallel plots.

Uses of Reference in elki.visualization.parallel3d.layout

Classes in elki.visualization.parallel3d.layout with annotations of type Reference

Modifier and Type	Class	Description
class	CompactCircularMSTLayout3DPC	Simple circular layout based on the minimum spanning tree.
class	MultidimensionalScalingMSTLayout3DPC	Layout the axes by multi-dimensional scaling.
class	SimpleCircularMSTLayout3DPC	Simple circular layout based on the minimum spanning tree.

Uses of Reference in elki.visualization.projector

Classes in elki.visualization.projector with annotations of type Reference

Modifier and Type	Class	Description
class	ParallelPlotProjector<V extends SpatialComparable>	ParallelPlotProjector is responsible for producing a parallel axes visualization.

Uses of Reference in elki.visualization.visualizers.pairsegments

Classes in elki.visualization.visualizers.pairsegments with annotations of type Reference

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

Uses of Reference in elki.visualization.visualizers.scatterplot.density

Methods in elki.visualization.visualizers.scatterplot.density with annotations of type Reference

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

Uses of Reference in elki.visualization.visualizers.scatterplot.outlier

Classes in elki.visualization.visualizers.scatterplot.outlier with annotations of type Reference

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