## Uses of Classde.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.
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
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
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.bulk
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>
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>
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
• ### 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.