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

Packages that use Reference

Package	Description
de.lmu.ifi.dbs.elki.algorithm	Algorithms suitable as a task for the KDDTask main routine.
de.lmu.ifi.dbs.elki.algorithm.clustering	Clustering algorithms.
de.lmu.ifi.dbs.elki.algorithm.clustering.correlation	Correlation clustering algorithms
de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan	Generalized DBSCAN.
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans	K-means clustering and variations.
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.outlier	Outlier detection algorithms
de.lmu.ifi.dbs.elki.algorithm.outlier.meta	Meta outlier detection algorithms: external scores, score rescaling.
de.lmu.ifi.dbs.elki.algorithm.outlier.spatial	Spatial outlier detection algorithms
de.lmu.ifi.dbs.elki.algorithm.outlier.subspace	Subspace outlier detection methods.
de.lmu.ifi.dbs.elki.application.greedyensemble	Greedy ensembles for outlier detection.
de.lmu.ifi.dbs.elki.application.internal	Internal utilities for development.
de.lmu.ifi.dbs.elki.application.visualization	Visualization applications in ELKI.
de.lmu.ifi.dbs.elki.database.ids.integer	Integer-based DBID implementation -- do not use directly - always use DBIDUtil.
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.timeseries	Distance functions designed for time series.
de.lmu.ifi.dbs.elki.evaluation.clustering	Evaluation of clustering results.
de.lmu.ifi.dbs.elki.evaluation.clustering.pairsegments	Pair-segment analysis of multiple clusterings.
de.lmu.ifi.dbs.elki.evaluation.outlier	Evaluate an outlier score using a misclassification based cost model.
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mtree	MTree
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.query	Queries on the R-Tree family of indexes: kNN and range queries.
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rstar	RStarTree
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.bulk	Packages for bulk-loading R*-Trees.
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.insert	Insertion strategies for R-Trees
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.overflow	Overflow treatment strategies for R-Trees
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.reinsert	Reinsertion strategies for R-Trees
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.split	Splitting strategies for R-Trees
de.lmu.ifi.dbs.elki.index.vafile	Vector Approximation File
de.lmu.ifi.dbs.elki.math	Mathematical operations and utilities used throughout the framework.
de.lmu.ifi.dbs.elki.math.dimensionsimilarity	Functions to compute the similarity of dimensions (or the interestingness of the combination).
de.lmu.ifi.dbs.elki.math.geometry	Algorithms from computational geometry.
de.lmu.ifi.dbs.elki.math.linearalgebra.pca	Principal Component Analysis (PCA) and Eigenvector processing.
de.lmu.ifi.dbs.elki.math.spacefillingcurves	Space filling curves.
de.lmu.ifi.dbs.elki.math.statistics.distribution	Standard distributions, with random generation functionalities.
de.lmu.ifi.dbs.elki.result	Result types, representation and handling
de.lmu.ifi.dbs.elki.utilities.datastructures.arrays	Utilities for arrays: advanced sorting for primitvie arrays.
de.lmu.ifi.dbs.elki.utilities.documentation	Documentation utilities: Annotations for Title, Description, Reference
de.lmu.ifi.dbs.elki.utilities.scaling.outlier	Scaling of Outlier scores, that require a statistical analysis of the occurring values
de.lmu.ifi.dbs.elki.visualization.visualizers.pairsegments	Visualizers for inspecting cluster differences using pair counting segments.
de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.density	Visualizers for data set density in a scatterplot projection.
de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.outlier	Visualizers for outlier scores based on 2D projections.

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	APRIORI Provides the APRIORI algorithm for Mining Association Rules.
class	DependencyDerivator<V extends NumberVector<?>,D extends Distance<D>> 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	DBSCAN<O,D extends Distance<D>> DBSCAN provides the DBSCAN algorithm, an algorithm to find density-connected sets in a database.
class	DeLiClu<NV extends NumberVector<?>,D extends Distance<D>> DeLiClu provides the DeLiClu algorithm, a hierarchical algorithm to find density-connected sets in a database.
class	EM<V extends NumberVector<?>> Provides the EM algorithm (clustering by expectation maximization).
class	NaiveMeanShiftClustering<V extends NumberVector<?>,D extends NumberDistance<D,?>> Mean-shift based clustering algorithm.
class	OPTICS<O,D extends Distance<D>> OPTICS provides the OPTICS algorithm.
class	SLINK<O,D extends Distance<D>> Implementation of the efficient Single-Link Algorithm SLINK of R.
class	SNNClustering<O> Shared nearest neighbor clustering.

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<?>> Provides the CASH algorithm, an subspace clustering algorithm based on the Hough transform.
class	COPAC<V extends NumberVector<?>,D extends Distance<D>> Provides the COPAC algorithm, 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 provides the ORCLUS algorithm, an algorithm to find clusters in high dimensional spaces.

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	EpsilonNeighborPredicate<O,D extends Distance<D>> The default DBSCAN and OPTICS neighbor predicate, using an epsilon-neighborhood.
class	GeneralizedDBSCAN Generalized DBSCAN, density-based clustering with noise.
class	MinPtsCorePredicate The DBSCAN default core point predicate -- having at least MinPtsCorePredicate.minpts neighbors.

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	KMeansLloyd<V extends NumberVector<?>,D extends Distance<D>> Provides the k-means algorithm, using Lloyd-style bulk iterations.
class	KMeansMacQueen<V extends NumberVector<?>,D extends Distance<D>> Provides the k-means algorithm, using MacQueen style incremental updates.
class	KMeansPlusPlusInitialMeans<V,D extends NumberDistance<D,?>> K-Means++ initialization for k-means.
class	KMediansLloyd<V extends NumberVector<?>,D extends Distance<D>> Provides the k-medians clustering algorithm, using Lloyd-style bulk iterations.
class	KMedoidsPAM<V,D extends NumberDistance<D,?>> Provides the k-medoids clustering algorithm, using the "Partitioning Around Medoids" approach.
class	PAMInitialMeans<V,D extends NumberDistance<D,?>> PAM initialization for k-means (and of course, PAM).

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

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

Modifier and Type	Class and Description
class	CLIQUE<V extends NumberVector<?>> Implementation of the CLIQUE algorithm, a grid-based algorithm to identify dense clusters in subspaces of maximum dimensionality.
class	DiSH<V extends NumberVector<?>> Algorithm for detecting subspace hierarchies.
class	HiSC<V extends NumberVector<?>> Implementation of the HiSC algorithm, an algorithm for detecting hierarchies of subspace clusters.
class	PreDeCon<V extends NumberVector<?>> PreDeCon computes clusters of subspace preference weighted connected points.
class	PROCLUS<V extends NumberVector<?>> Provides 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.outlier

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

Modifier and Type	Class and Description
class	ABOD<V extends NumberVector<?>> Angle-Based Outlier Detection Outlier detection using variance analysis on angles, especially for high dimensional data sets.
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<?>> EAFOD provides the evolutionary outlier detection algorithm, an algorithm to detect outliers for high dimensional data.
class	AggarwalYuNaive<V extends NumberVector<?>> BruteForce provides a naive brute force algorithm in which all k-subsets of dimensions are examined and calculates the sparsity coefficient to find outliers.
class	ALOCI<O extends NumberVector<?>,D extends NumberDistance<D,?>> Fast Outlier Detection Using the "approximate Local Correlation Integral".
class	COP<V extends NumberVector<?>,D extends NumberDistance<D,?>> Correlation outlier probability: Outlier Detection in Arbitrarily Oriented Subspaces Hans-Peter Kriegel, Peer Kröger, Erich Schubert, Arthur Zimek Outlier Detection in Arbitrarily Oriented Subspaces in: Proc.
class	DBOutlierDetection<O,D extends Distance<D>> Simple distanced based outlier detection algorithm.
class	DBOutlierScore<O,D extends Distance<D>> Compute percentage of neighbors in the given neighborhood with size d.
class	GaussianUniformMixture<V extends NumberVector<?>> Outlier detection algorithm using a mixture model approach.
class	HilOut<O extends NumberVector<?>> Fast Outlier Detection in High Dimensional Spaces Outlier Detection using Hilbert space filling curves Reference: F.
class	INFLO<O,D extends NumberDistance<D,?>> INFLO provides the Mining Algorithms (Two-way Search Method) for Influence Outliers using Symmetric Relationship Reference: Jin, W., Tung, A., Han, J., and Wang, W. 2006 Ranking outliers using symmetric neighborhood relationship In Proc.
class	KNNOutlier<O,D extends NumberDistance<D,?>> Outlier Detection based on the distance of an object to its k nearest neighbor.
class	KNNWeightOutlier<O,D extends NumberDistance<D,?>> Outlier Detection based on the accumulated distances of a point to its k nearest neighbors.
class	LDF<O extends NumberVector<?>,D extends NumberDistance<D,?>> Outlier Detection with Kernel Density Functions.
class	LDOF<O,D extends NumberDistance<D,?>> Computes the LDOF (Local Distance-Based Outlier Factor) for all objects of a Database.
class	LOCI<O,D extends NumberDistance<D,?>> Fast Outlier Detection Using the "Local Correlation Integral".
class	LOF<O,D extends NumberDistance<D,?>> Algorithm to compute density-based local outlier factors in a database based on a specified parameter LOF.K_ID (-lof.k).
class	LoOP<O,D extends NumberDistance<D,?>> LoOP: Local Outlier Probabilities Distance/density based algorithm similar to LOF to detect outliers, but with statistical methods to achieve better result stability.
class	OPTICSOF<O,D extends NumberDistance<D,?>> OPTICSOF provides the Optics-of algorithm, an algorithm to find Local Outliers in a database.
class	ReferenceBasedOutlierDetection<V extends NumberVector<?>,D extends NumberDistance<D,?>> provides the Reference-Based Outlier Detection algorithm, an algorithm that computes kNN distances approximately, using reference points.
class	SimpleCOP<V extends NumberVector<?>,D extends NumberDistance<D,?>> Algorithm to compute local correlation outlier probability.

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<?>,D extends NumberDistance<D,?>> GLS-Backward Search is a statistical approach to detecting spatial outliers.
class	CTLuMeanMultipleAttributes<N,O extends NumberVector<?>> Mean Approach is used to discover spatial outliers with multiple attributes.
class	CTLuMedianAlgorithm<N> Median Algorithm of C.
class	CTLuMedianMultipleAttributes<N,O extends NumberVector<?>> Median Approach is used to discover spatial outliers with multiple attributes.
class	CTLuMoranScatterplotOutlier<N> Moran scatterplot outliers, based on the standardized deviation from the local and global means.
class	CTLuRandomWalkEC<N,D extends NumberDistance<D,?>> 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,D extends NumberDistance<D,?>> SLOM: a new measure for local spatial outliers Reference: Sanjay Chawla and Pei Sun SLOM: a new measure for local spatial outliers in Knowledge and Information Systems 9(4), 412-429, 2006 This implementation works around some corner cases in SLOM, in particular when an object has none or a single neighbor only (albeit the results will still not be too useful then), which will result in divisions by zero.
class	SOF<N,O,D extends NumberDistance<D,?>> 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	OutRankS1 OutRank: ranking outliers in high dimensional data.
class	OUTRES<V extends NumberVector<?>> Adaptive outlierness for subspace outlier ranking (OUTRES).
class	SOD<V extends NumberVector<?>,D extends NumberDistance<D,?>> Subspace Outlier Degree.

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

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

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

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

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

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

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

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

Modifier and Type	Class and Description
class	KNNExplorer<O extends NumberVector<?>,D extends NumberDistance<D,?>> User application to explore the k Nearest Neighbors for a given data set and distance function.

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.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	JeffreyDivergenceDistanceFunction Provides the Jeffrey Divergence Distance for FeatureVectors.

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.timeseries

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

Modifier and Type	Class and Description
class	DTWDistanceFunction Provides the Dynamic Time Warping distance for FeatureVectors.
class	EDRDistanceFunction Provides the Edit Distance on Real Sequence distance for FeatureVectors.
class	ERPDistanceFunction Provides the Edit Distance With Real Penalty distance for FeatureVectors.
class	LCSSDistanceFunction Provides the Longest Common Subsequence distance for FeatureVectors.

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 Pantel, P. and Lin, D.
class	Entropy Entropy based measures References: Meilă, M.
class	SetMatchingPurity Set matching purity measures References: Zhao, Y. and Karypis, G.

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

Modifier and Type	Method and Description
double	SetMatchingPurity.f1Measure() Get the set matching F1-Measure Steinbach, M. and Karypis, G. and Kumar, V. and others A comparison of document clustering techniques KDD workshop on text mining, 2000
double	PairCounting.fowlkesMallows() Computes the pair-counting Fowlkes-mallows (flat only, non-hierarchical!)
double	Entropy.normalizedVariationOfInformation() Get the normalized variation of information (normalized, 0 = equal) NVI = 1 - NMI_Joint Vinh, N.X. and Epps, J. and Bailey, J.
double	SetMatchingPurity.purity() Get the set matchings purity (first:second clustering) (normalized, 1 = equal)
double	PairCounting.randIndex() Computes the Rand index (RI).

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

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

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

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

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

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

Uses of Reference in de.lmu.ifi.dbs.elki.index.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,D extends Distance<D>> 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.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	DoubleDistanceRStarTreeKNNQuery<O extends SpatialComparable> Instance of a KNN query for a particular spatial index.
class	DoubleDistanceRStarTreeRangeQuery<O extends SpatialComparable> Instance of a range query for a particular spatial index.
class	GenericRStarTreeKNNQuery<O extends SpatialComparable,D extends Distance<D>> Instance of a KNN query for a particular spatial index.
class	GenericRStarTreeRangeQuery<O extends SpatialComparable,D extends Distance<D>> Instance of a range query for a particular spatial index.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Modifier and Type	Class and Description
class	GeoUtil Class with utility functions for geographic computations.
class	Mean Compute the mean using a numerically stable online algorithm.
class	MeanVariance Do some simple statistics (mean, variance) using a numerically stable online algorithm.

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

Modifier and Type	Method and Description
static double	GeoUtil.haversineFormulaDeg(double lat1, double lon1, double lat2, double lon2) Compute the approximate on-earth-surface distance of two points using the Haversine formula Complexity: 5 trigonometric functions, 2 sqrt.
static double	GeoUtil.haversineFormulaRad(double lat1, double lon1, double lat2, double lon2) Compute the approximate on-earth-surface distance of two points using the Haversine formula Complexity: 5 trigonometric functions, 2 sqrt.
static double	GeoUtil.sphericalVincentyFormulaDeg(double lat1, double lon1, double lat2, double lon2) Compute the approximate on-earth-surface distance of two points.
static double	GeoUtil.sphericalVincentyFormulaRad(double lat1, double lon1, double lat2, double lon2) Compute the approximate on-earth-surface distance of two points.

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

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

Modifier and Type	Class and Description
class	HSMDimensionSimilarity FIXME: This needs serious TESTING before release.
class	MCEDimensionSimilarity Compute dimension similarity by using a nested means discretization.
class	SURFINGDimensionSimilarity Compute the similarity of dimensions using the SURFING score.

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

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

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

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

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

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

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

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

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.distribution

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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