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 Clustering algorithms are supposed to implement the Algorithm-Interface.
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.gdbscan	Generalized DBSCAN Generalized DBSCAN is an abstraction of the original DBSCAN idea, that allows the use of arbitrary "neighborhood" and "core point" predicates.
de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan.parallel	Parallel versions of Generalized DBSCAN.
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical	Hierarchical agglomerative clustering (HAC).
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.birch	BIRCH clustering.
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.extraction	Extraction of partitional clusterings from hierarchical results.
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.linkage	Linkages for hierarchical clustering.
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.itemsetmining.associationrules	Association rule mining.
de.lmu.ifi.dbs.elki.algorithm.itemsetmining.associationrules.interest	Association rule interestingness measures.
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.intrinsic	Outlier detection algorithms based on intrinsic dimensionality.
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 Methods that detect outliers in subspaces (projections) of the data set.
de.lmu.ifi.dbs.elki.algorithm.outlier.svm	Support-Vector-Machines for outlier detection.
de.lmu.ifi.dbs.elki.algorithm.projection	Data projections (see also preprocessing filters for basic projections).
de.lmu.ifi.dbs.elki.algorithm.statistics	Statistical analysis algorithms.
de.lmu.ifi.dbs.elki.algorithm.timeseries	Algorithms for change point detection in time series.
de.lmu.ifi.dbs.elki.application	Base classes for standalone applications.
de.lmu.ifi.dbs.elki.application.experiments	Packaged experiments to make them easy to reproduce.
de.lmu.ifi.dbs.elki.application.greedyensemble	Greedy ensembles for outlier detection.
de.lmu.ifi.dbs.elki.data.projection.random	Random projection families
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, F-divergence, χ²-divergence, etc.
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 Note that some regular distance functions (e.g., Euclidean) are also used on 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.evaluation.scores	Evaluation of rankings and scorings
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.preprocessed.preference	Indexes storing preference vectors
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.metrical.mtreevariants.strategies.split.distribution	Entry distsribution 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.geodesy	Functions for computing on the sphere / earth.
de.lmu.ifi.dbs.elki.math.geometry	Algorithms from computational geometry
de.lmu.ifi.dbs.elki.math.linearalgebra	The linear algebra package provides classes and computational methods for operations on matrices and vectors.
de.lmu.ifi.dbs.elki.math.linearalgebra.pca	Principal Component Analysis (PCA) and Eigenvector processing
de.lmu.ifi.dbs.elki.math.spacefillingcurves	Space filling curves
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.datastructures.unionfind	Union-find data structures.
de.lmu.ifi.dbs.elki.utilities.random	Random number generation.
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.parallel3d	3DPC: 3D parallel coordinate plot visualization for ELKI.
de.lmu.ifi.dbs.elki.visualization.parallel3d.layout	Layouting algorithms for 3D parallel coordinate plots.
de.lmu.ifi.dbs.elki.visualization.projector	Projectors are responsible for finding appropriate projections for data relations
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	Tutorials on implementing outlier detection methods in ELKI.

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	GriDBSCAN<V extends NumberVector> Using Grid for Accelerating Density-Based 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 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> 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.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 4C core point predicate.
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.
class	SimilarityNeighborPredicate<O> The DBSCAN neighbor predicate for a SimilarityFunction, using all neighbors with a minimum similarity.

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

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

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

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	AnderbergHierarchicalClustering<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	HDBSCANLinearMemory<O> Linear memory implementation of HDBSCAN clustering.
class	MiniMaxAnderberg<O> This is a modification of the classic MiniMax algorithm for hierarchical clustering using a nearest-neighbor heuristic for acceleration.
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 de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.birch

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

Modifier and Type	Class and Description
class	AverageInterclusterDistance Average intercluster distance.
class	AverageIntraclusterDistance Average intracluster distance.
class	CentroidEuclideanDistance Centroid Euclidean distance.
class	CentroidManhattanDistance Centroid Manhattan Distance Reference: Data Clustering for Very Large Datasets Plus Applications T.
class	DiameterCriterion Average Radius (R) criterion.
class	RadiusCriterion Average Radius (R) criterion.
class	VarianceIncreaseDistance Variance increase distance.

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	ClustersWithNoiseExtraction Extraction of a given number of clusters with a minimum size, and noise.
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.hierarchical.linkage

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

Modifier and Type	Class and 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 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	CLARANS<V> CLARANS: a method for clustering objects for spatial data mining is inspired by PAM (partitioning around medoids, KMedoidsPAM) and CLARA and also based on sampling.
class	FastCLARA<V> Clustering Large Applications (CLARA) with the KMedoidsFastPAM 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	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	KMeansCompare<V extends NumberVector> Compare-Means: Accelerated k-means by exploiting the triangle inequality and pairwise distances of means to prune candidate means.
class	KMeansElkan<V extends NumberVector> Elkan's fast k-means by exploiting the triangle inequality.
class	KMeansExponion<V extends NumberVector> Newlings's exponion k-means algorithm, exploiting the triangle inequality.
class	KMeansHamerly<V extends NumberVector> Hamerly's fast k-means by exploiting the triangle inequality.
class	KMeansMacQueen<V extends NumberVector> The original k-means algorithm, using MacQueen style incremental updates; making this effectively an "online" (streaming) algorithm.
class	KMeansMinusMinus<V extends NumberVector> k-means--: A Unified Approach to Clustering and Outlier Detection.
class	KMeansSimplifiedElkan<V extends NumberVector> Simplified version of Elkan's k-means by exploiting the triangle inequality.
class	KMeansSort<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	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	KMedoidsFastPAM<V> FastPAM: An improved version of PAM, that is usually O(k) times faster.
class	KMedoidsFastPAM1<V> FastPAM1: A version of PAM that is O(k) times faster, i.e., now in O((n-k)²).
class	KMedoidsPAMReynolds<V> The Partitioning Around Medoids (PAM) algorithm with some additional optimizations proposed by Reynolds et al.
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	FirstKInitialMeans<O> Initialize K-means by using the first k objects as initial means.
class	KMeansPlusPlusInitialMeans<O> K-Means++ initialization for k-means.
class	LABInitialMeans<O> Linear approximative BUILD (LAB) initialization for FastPAM (and k-means).
class	ParkInitialMeans<O> Initialization method proposed by Park and Jun.
class	RandomNormalGeneratedInitialMeans Initialize k-means by generating random vectors (normal distributed with \(N(\mu,\sigma)\) in each dimension).
class	RandomUniformGeneratedInitialMeans Initialize k-means by generating random vectors (uniform, within the value range of the data set).
class	SampleKMeansInitialization<V extends NumberVector> Initialize k-means by running k-means on a sample of the data set only.

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	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.

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<V extends NumberVector> DeliClu: Density-Based Hierarchical Clustering A hierarchical algorithm to find density-connected sets in a database, closely related to OPTICS but exploiting the structure of a R-tree for acceleration.
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 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> DOC is a sampling based subspace clustering algorithm.
class	FastDOC<V extends NumberVector> The heuristic variant of the DOC algorithm, FastDOC Reference: C.
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.itemsetmining.associationrules

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

Modifier and Type	Class and Description
class	AssociationRuleGeneration Association rule generation from frequent itemsets This algorithm calls a specified frequent itemset algorithm and calculates all association rules, having a interest value between then the specified boundaries form the obtained frequent itemsets Reference: M.

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

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

Modifier and Type	Class and 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.

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 Reference: Hans-Peter Kriegel, Peer Kröger, Erich Schubert, Arthur Zimek Outlier Detection in Arbitrarily Oriented Subspaces Proc.
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<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> 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 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	CBLOF<O extends NumberVector> Cluster-based local outlier factor (CBLOF).
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	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 de.lmu.ifi.dbs.elki.algorithm.outlier.distance with annotations of type Reference

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

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.intrinsic

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

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

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.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.
class	VarianceOfVolume<O extends SpatialComparable> Variance of Volume for outlier detection.

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<P> Spatial outlier detection based on random walks.
class	CTLuScatterplotOutlier<N> Scatterplot-outlier is a spatial outlier detection method that performs a linear regression of object attributes and their neighbors average value.
class	CTLuZTestOutlier<N> Detect outliers by comparing their attribute value to the mean and standard deviation of their neighborhood.
class	SLOM<N,O> SLOM: a new measure for local spatial outliers Reference: S.
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 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.projection

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

Modifier and Type	Class and Description
class	BarnesHutTSNE<O> tSNE 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 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.algorithm.timeseries

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

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

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 java.lang.String	AbstractApplication.REFERENCE Information for citation and version.

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

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

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

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.data.projection.random

Classes in de.lmu.ifi.dbs.elki.data.projection.random 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	RandomSubsetProjectionFamily Random projection family based on selecting random features.
class	SimplifiedRandomHyperplaneProjectionFamily Random hyperplane projection family.

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	CanberraDistanceFunction Canberra distance function, a variation of Manhattan distance.
class	ClarkDistanceFunction Clark distance function for vector spaces.
class	MahalanobisDistanceFunction Mahalanobis quadratic form distance for feature vectors.

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

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

Modifier and Type	Class and Description
class	DimensionSelectingLatLngDistanceFunction Distance function for 2D vectors in Latitude, Longitude form.
class	LatLngDistanceFunction Distance function for 2D vectors in Latitude, Longitude form.
class	LngLatDistanceFunction Distance function for 2D vectors in Longitude, Latitude form.

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., Manhattan distance on the cumulative density function.

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 χ² distance function, symmetric version.
class	KullbackLeiblerDivergenceAsymmetricDistanceFunction Kullback-Leibler divergence, also known as relative entropy, information deviation, or just KL-distance (albeit asymmetric).
class	KullbackLeiblerDivergenceReverseAsymmetricDistanceFunction Kullback-Leibler divergence, also known as relative entropy, information deviation or just KL-distance (albeit asymmetric).
class	SqrtJensenShannonDivergenceDistanceFunction The square root of Jensen-Shannon divergence is a metric.
class	TriangularDiscriminationDistanceFunction Triangular Discrimination has relatively tight upper and lower bounds to the Jensen-Shannon divergence, but is much less expensive.
class	TriangularDistanceFunction 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 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 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	PairCounting.adjustedRandIndex() Computes the adjusted Rand index (ARI).
double	SetMatchingPurity.f1Measure() Get the set matching F1-Measure M.
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	PairCounting.jaccard() Computes the Jaccard index P.
long	PairCounting.mirkin() Computes the Mirkin index, aka Equivalence Mismatch Distance.
double	Entropy.normalizedVariationOfInformation() Get the normalized variation of information (normalized, 0 = equal) NVI = 1 - NMI_Joint X.
double	SetMatchingPurity.purity() Get the set matchings purity (first:second clustering) (normalized, 1 = equal) Y.
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	EvaluateDBCV<O> Compute the Density-Based Clustering Validation Index.
class	EvaluatePBMIndex Compute the PBM index of a clustering Reference: M.
class	EvaluateSilhouette<O> Compute the silhouette of a data set.
class	EvaluateVarianceRatioCriteria<O> Compute the Variance Ratio Criteria of a data set, also known as Calinski-Harabasz index.

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	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 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.evaluation.scores

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

Modifier and Type	Class and Description
class	DCGEvaluation Discounted Cumulative Gain.
class	NDCGEvaluation Normalized Discounted Cumulative Gain.

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	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	RandomProjectedNeighborsAndDensities<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	NNDescent<O> NN-desent (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 de.lmu.ifi.dbs.elki.index.preprocessed.preference

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

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

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<N extends AbstractMTreeNode<?,N,E>,E extends MTreeEntry> Minimum enlargement insert - 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<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 de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.split.distribution

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

Modifier and Type	Class and 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 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: R.

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

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

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

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.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 Complexity: 5 trigonometric functions, 1-2 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.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

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

Modifier and Type	Method and Description
static double	VMath.mahalanobisDistance(double[][] B, double[] a, double[] c) Matrix multiplication, (a-c)T * B * (a-c) Note: it may (or may not) be more efficient to materialize (a-c), then use transposeTimesTimes(a_minus_c, B, a_minus_c) instead.

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

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.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	HoeffdingsDDependenceMeasure Calculate Hoeffding's D as a measure of dependence.
class	HSMDependenceMeasure Compute the "interestingness" of dimension connections using the 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.

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.
class	SkewGeneralizedNormalDistribution Generalized normal distribution by adding a skew term, similar to lognormal distributions.

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

Modifier and Type	Method and 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 Based on first half of algorithm AS 91 Reference: D.
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	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 Reference: D.
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 stirlerr(n) = ln(n!)
private static double	PoissonDistribution.stirlingError(int n) Calculates the Stirling 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	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 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	WinsorizingEstimator<D extends Distribution> Winsorizing 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	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	RVEstimator Regularly Varying Functions estimator of the intrinsic dimensionality Reference: L.

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

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.

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.datastructures.unionfind

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

Modifier and Type	Class and 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 de.lmu.ifi.dbs.elki.utilities.random

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

Modifier and Type	Class and 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 de.lmu.ifi.dbs.elki.utilities.random with annotations of type Reference

Modifier and Type	Method and Description
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.
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	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	FastNonThreadsafeRandom.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	FastNonThreadsafeRandom.nextIntRefined(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 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	HeDESNormalizationOutlierScaling Normalization used by HeDES Reference: H.
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) Useful for example for scaling ABOD, but see MinusLogStandardDeviationScaling and MinusLogGammaScaling for more advanced scalings for this algorithm.
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 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].

Uses of Reference in de.lmu.ifi.dbs.elki.visualization.parallel3d

Classes in de.lmu.ifi.dbs.elki.visualization.parallel3d with annotations of type Reference

Modifier and Type	Class and 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 de.lmu.ifi.dbs.elki.visualization.parallel3d.layout

Classes in de.lmu.ifi.dbs.elki.visualization.parallel3d.layout with annotations of type Reference

Modifier and Type	Class and 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 de.lmu.ifi.dbs.elki.visualization.projector

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

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

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.