Uses of Class
elki.utilities.documentation.Reference
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Packages that use Reference Package Description elki.algorithm Miscellaneous algorithms.elki.algorithm.statistics Statistical analysis algorithms.elki.application Base classes for standalone applications.elki.application.experiments Packaged experiments to make them easy to reproduce.elki.application.greedyensemble Greedy ensembles for outlier detection.elki.clustering Clustering algorithms.elki.clustering.affinitypropagation Affinity Propagation (AP) clustering.elki.clustering.biclustering Biclustering algorithms.elki.clustering.correlation Correlation clustering algorithms.elki.clustering.dbscan DBSCAN and its generalizations.elki.clustering.dbscan.parallel Parallel versions of Generalized DBSCAN.elki.clustering.dbscan.predicates Neighbor and core predicated for Generalized DBSCAN.elki.clustering.em Expectation-Maximization clustering algorithm for Gaussian Mixture Modeling (GMM).elki.clustering.em.models elki.clustering.hierarchical Hierarchical agglomerative clustering (HAC).elki.clustering.hierarchical.birch BIRCH clustering.elki.clustering.hierarchical.extraction Extraction of partitional clusterings from hierarchical results.elki.clustering.hierarchical.linkage Linkages for hierarchical clustering.elki.clustering.kmeans K-means clustering and variations.elki.clustering.kmeans.initialization Initialization strategies for k-means.elki.clustering.kmeans.initialization.betula Initialization methods for BIRCH-based k-means and EM clustering.elki.clustering.kmeans.quality Quality measures for k-Means results.elki.clustering.kmeans.spherical Spherical k-means clustering and variations.elki.clustering.kmedoids K-medoids clustering (PAM).elki.clustering.kmedoids.initialization elki.clustering.optics OPTICS family of clustering algorithms.elki.clustering.silhouette Silhouette clustering algorithms.elki.clustering.subspace Axis-parallel subspace clustering algorithms.elki.clustering.uncertain Clustering algorithms for uncertain data.elki.data.projection.random Random projection families.elki.database.ids.integer Integer-based DBID implementation -- do not use directly - always useDBIDUtil
.elki.datasource.filter.transform Data space transformations.elki.distance Distance functions for use within ELKI.elki.distance.colorhistogram Distance functions for color histograms.elki.distance.geo Geographic (earth) distance functions.elki.distance.histogram Distance functions for one-dimensional histograms.elki.distance.probabilistic Distance from probability theory, mostly divergences such as K-L-divergence, J-divergence, F-divergence, χ²-divergence, etc.elki.distance.set Distance functions for binary and set type data.elki.distance.strings Distance functions for strings.elki.distance.timeseries Distance functions designed for time series.elki.evaluation.clustering Evaluation of clustering results.elki.evaluation.clustering.internal Internal evaluation measures for clusterings.elki.evaluation.clustering.pairsegments Pair-segment analysis of multiple clusterings.elki.evaluation.outlier Evaluate an outlier score using a misclassification based cost model.elki.evaluation.scores Evaluation of rankings and scorings.elki.index.laesa Linear Approximating and Eliminating Search Algorithm (LAESA).elki.index.lsh.hashfamilies Hash function families for LSH.elki.index.lsh.hashfunctions Hash functions for LSH.elki.index.preprocessed.fastoptics Preprocessed index used by the FastOPTICS algorithm.elki.index.preprocessed.knn Indexes providing KNN and rKNN data.elki.index.projected Projected indexes for data.elki.index.tree.betula.distance Distance functions for BETULA and BIRCH.elki.index.tree.metrical.covertree Cover-tree variations.elki.index.tree.metrical.mtreevariants.mtree elki.index.tree.metrical.mtreevariants.strategies.insert Insertion (choose path) strategies of nodes in an M-tree (and variants).elki.index.tree.metrical.mtreevariants.strategies.split Splitting strategies of nodes in an M-tree (and variants).elki.index.tree.metrical.mtreevariants.strategies.split.distribution Entry distribution strategies of nodes in an M-tree (and variants).elki.index.tree.metrical.vptree elki.index.tree.spatial.kd K-d-tree and variants.elki.index.tree.spatial.kd.split elki.index.tree.spatial.rstarvariants.query Queries on the R-Tree family of indexes: kNN and range queries.elki.index.tree.spatial.rstarvariants.rstar elki.index.tree.spatial.rstarvariants.strategies.bulk Packages for bulk-loading R*-trees.elki.index.tree.spatial.rstarvariants.strategies.insert Insertion strategies for R-trees.elki.index.tree.spatial.rstarvariants.strategies.overflow Overflow treatment strategies for R-trees.elki.index.tree.spatial.rstarvariants.strategies.reinsert Reinsertion strategies for R-trees.elki.index.tree.spatial.rstarvariants.strategies.split Splitting strategies for R-trees.elki.index.vafile Vector Approximation File.elki.itemsetmining Algorithms for frequent itemset mining such as APRIORI.elki.itemsetmining.associationrules Association rule mining.elki.itemsetmining.associationrules.interest Association rule interestingness measures.elki.math Mathematical operations and utilities used throughout the framework.elki.math.geodesy Functions for computing on the sphere / earth.elki.math.geometry Algorithms from computational geometry.elki.math.linearalgebra The linear algebra package provides classes and computational methods for operations on matrices and vectors.elki.math.linearalgebra.pca Principal Component Analysis (PCA) and eigenvector processing.elki.math.spacefillingcurves Space filling curves.elki.math.statistics.dependence Statistical measures of dependence, such as correlation.elki.math.statistics.dependence.mcde Tests tailored to be used withMCDEDependence
.elki.math.statistics.distribution Standard distributions, with random generation functionalities.elki.math.statistics.distribution.estimator Estimators for statistical distributions.elki.math.statistics.distribution.estimator.meta Meta estimators: estimators that do not actually estimate themselves, but instead use other estimators, e.g., on a trimmed data set, or as an ensemble.elki.math.statistics.intrinsicdimensionality Methods for estimating the intrinsic dimensionality.elki.math.statistics.kernelfunctions Kernel functions from statistics.elki.math.statistics.tests Statistical tests.elki.outlier Outlier detection algorithms.elki.outlier.anglebased Angle-based outlier detection algorithms.elki.outlier.clustering Clustering based outlier detection.elki.outlier.density Density-based outlier detection algorithms.elki.outlier.distance Distance-based outlier detection algorithms, such as DBOutlier and kNN.elki.outlier.distance.parallel Parallel implementations of distance-based outlier detectors.elki.outlier.intrinsic Outlier detection algorithms based on intrinsic dimensionality.elki.outlier.lof LOF family of outlier detection algorithms.elki.outlier.lof.parallel Parallelized variants of LOF.elki.outlier.meta Meta outlier detection algorithms: external scores, score rescaling.elki.outlier.spatial Spatial outlier detection algorithms.elki.outlier.subspace Subspace outlier detection methods.elki.outlier.svm Support-Vector-Machines for outlier detection.elki.projection Data projections (see also preprocessing filters for basic projections).elki.result Result types, representation and handling.elki.similarity Similarity functions.elki.similarity.cluster Similarity measures for comparing clusters.elki.timeseries Algorithms for change point detection in time series.elki.utilities.datastructures Basic memory structures such as heaps and object hierarchies.elki.utilities.datastructures.arrays Utilities for arrays: advanced sorting for primitive arrays.elki.utilities.datastructures.unionfind Union-find data structures.elki.utilities.documentation Documentation utilities: Annotations for Title, Description, Reference.elki.utilities.random Random number generation.elki.utilities.scaling.outlier Scaling of outlier scores, that require a statistical analysis of the occurring values.elki.visualization.parallel3d 3DPC: 3D parallel coordinate plot visualization for ELKI.elki.visualization.parallel3d.layout Layouting algorithms for 3D parallel coordinate plots.elki.visualization.projector Projectors are responsible for finding appropriate projections for data relations.elki.visualization.visualizers.pairsegments Visualizers for inspecting cluster differences using pair counting segments.elki.visualization.visualizers.scatterplot.density Visualizers for data set density in a scatterplot projection.elki.visualization.visualizers.scatterplot.outlier Visualizers for outlier scores based on 2D projections.tutorial.clustering Classes from the tutorial on implementing a custom k-means variation.tutorial.outlier Tutorials on implementing outlier detection methods in ELKI. -
Packages with annotations of type Reference Package Description elki.outlier.lof.parallel Parallelized variants of LOF. -
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Uses of Reference in elki.algorithm
Classes in elki.algorithm with annotations of type Reference Modifier and Type Class Description class
DependencyDerivator<V extends NumberVector>
Dependency derivator computes quantitatively linear dependencies among attributes of a given dataset based on a linear correlation PCA. -
Uses of Reference in elki.algorithm.statistics
Classes in elki.algorithm.statistics with annotations of type Reference Modifier and Type Class Description class
HopkinsStatisticClusteringTendency
The Hopkins Statistic of Clustering Tendency measures the probability that a data set is generated by a uniform data distribution. -
Uses of Reference in elki.application
Fields in elki.application with annotations of type Reference Modifier and Type Field Description static java.lang.String
AbstractApplication. REFERENCE
Information for citation and version. -
Uses of Reference in elki.application.experiments
Classes in elki.application.experiments with annotations of type Reference Modifier and Type Class Description class
VisualizeGeodesicDistances
Visualization function for Cross-track, Along-track, and minimum distance function. -
Uses of Reference in elki.application.greedyensemble
Classes in elki.application.greedyensemble with annotations of type Reference Modifier and Type Class Description class
ComputeKNNOutlierScores<O extends NumberVector>
Application that runs a series of kNN-based algorithms on a data set, for building an ensemble in a second step.class
GreedyEnsembleExperiment
Class to load an outlier detection summary file, as produced byComputeKNNOutlierScores
, and compute a naive ensemble for it.class
VisualizePairwiseGainMatrix
Class to load an outlier detection summary file, as produced byComputeKNNOutlierScores
, and compute a matrix with the pairwise gains. -
Uses of Reference in elki.clustering
Classes in elki.clustering with annotations of type Reference Modifier and Type Class Description class
BetulaLeafPreClustering
BETULA-based clustering algorithm that simply treats the leafs of the CFTree as clusters.class
CanopyPreClustering<O>
Canopy pre-clustering is a simple preprocessing step for clustering.class
CFSFDP<O>
Clustering by fast search and find of density peaks (CFSFDP) is a density-based clustering method similar to mean-shift clustering.class
Leader<O>
Leader clustering algorithm.class
NaiveMeanShiftClustering<V extends NumberVector>
Mean-shift based clustering algorithm.class
SNNClustering<O>
Shared nearest neighbor clustering. -
Uses of Reference in elki.clustering.affinitypropagation
Classes in elki.clustering.affinitypropagation with annotations of type Reference Modifier and Type Class Description class
AffinityPropagation<O>
Cluster analysis by affinity propagation. -
Uses of Reference in elki.clustering.biclustering
Classes in elki.clustering.biclustering with annotations of type Reference Modifier and Type Class Description class
ChengAndChurch
Cheng and Church biclustering. -
Uses of Reference in elki.clustering.correlation
Classes in elki.clustering.correlation with annotations of type Reference Modifier and Type Class Description class
CASH
The CASH algorithm is a subspace clustering algorithm based on the Hough transform.class
COPAC
COPAC is an algorithm to partition a database according to the correlation dimension of its objects and to then perform an arbitrary clustering algorithm over the partitions.class
ERiC
Performs correlation clustering on the data partitioned according to local correlation dimensionality and builds a hierarchy of correlation clusters that allows multiple inheritance from the clustering result.class
FourC
4C identifies local subgroups of data objects sharing a uniform correlation.class
HiCO
Implementation of the HiCO algorithm, an algorithm for detecting hierarchies of correlation clusters.class
LMCLUS
Linear manifold clustering in high dimensional spaces by stochastic search.class
ORCLUS
ORCLUS: Arbitrarily ORiented projected CLUSter generation. -
Uses of Reference in elki.clustering.dbscan
Classes in elki.clustering.dbscan with annotations of type Reference Modifier and Type Class Description class
GeneralizedDBSCAN
Generalized DBSCAN, density-based clustering with noise.class
GriDBSCAN<V extends NumberVector>
Using Grid for Accelerating Density-Based Clustering.class
LSDBC<O extends NumberVector>
Locally Scaled Density Based Clustering. -
Uses of Reference in elki.clustering.dbscan.parallel
Classes in elki.clustering.dbscan.parallel with annotations of type Reference Modifier and Type Class Description class
ParallelGeneralizedDBSCAN
Parallel version of DBSCAN clustering. -
Uses of Reference in elki.clustering.dbscan.predicates
Classes in elki.clustering.dbscan.predicates with annotations of type Reference Modifier and Type Class Description class
COPACNeighborPredicate
COPAC neighborhood predicate.class
EpsilonNeighborPredicate<O>
The default DBSCAN and OPTICS neighbor predicate, using an epsilon-neighborhood.class
ERiCNeighborPredicate
ERiC neighborhood predicate.class
FourCCorePredicate
The 4C core point predicate.class
FourCNeighborPredicate
4C identifies local subgroups of data objects sharing a uniform correlation.class
MinPtsCorePredicate
The DBSCAN default core point predicate -- having at leastMinPtsCorePredicate.minpts
neighbors.class
PreDeConCorePredicate
The PreDeCon core point predicate -- having at least minpts. neighbors, and a maximum preference dimensionality of lambda.class
PreDeConNeighborPredicate
Neighborhood predicate used by PreDeCon.class
SimilarityNeighborPredicate<O>
The DBSCAN neighbor predicate for aSimilarity
, using all neighbors with a minimum similarity. -
Uses of Reference in elki.clustering.em
Classes in elki.clustering.em with annotations of type Reference Modifier and Type Class Description class
BetulaGMM
Clustering by expectation maximization (EM-Algorithm), also known as Gaussian Mixture Modeling (GMM), with optional MAP regularization.class
BetulaGMMWeighted
Clustering by expectation maximization (EM-Algorithm), also known as Gaussian Mixture Modeling (GMM), with optional MAP regularization.class
KDTreeEM
Clustering by expectation maximization (EM-Algorithm), also known as Gaussian Mixture Modeling (GMM), calculated on a kd-tree. -
Uses of Reference in elki.clustering.em.models
Classes in elki.clustering.em.models with annotations of type Reference Modifier and Type Interface Description interface
BetulaClusterModel
Models usable in Betula EM clustering.interface
BetulaClusterModelFactory<M extends BetulaClusterModel>
Factory for initializing the EM models.class
BetulaDiagonalGaussianModelFactory
Factory for EM with multivariate gaussian models using diagonal matrixes.class
BetulaMultivariateGaussianModelFactory
Factory for EM with multivariate gaussian models using diagonal matrixes.class
BetulaSphericalGaussianModelFactory
Factory for EM with multivariate gaussian models using a single variance. -
Uses of Reference in elki.clustering.hierarchical
Classes in elki.clustering.hierarchical with annotations of type Reference Modifier and Type Class Description class
AbstractHDBSCAN<O>
Abstract base class for HDBSCAN variations.class
Anderberg<O>
This is a modification of the classic AGNES algorithm for hierarchical clustering using a nearest-neighbor heuristic for acceleration.class
CLINK<O>
CLINK algorithm for complete linkage.class
HACAM<O>
Hierarchical Agglomerative Clustering Around Medoids (HACAM) is a hierarchical clustering method that merges the clusters with the smallest distance to the medoid of the union.class
HDBSCANLinearMemory<O>
Linear memory implementation of HDBSCAN clustering.class
LinearMemoryNNChain<O extends NumberVector>
NNchain clustering algorithm with linear memory, for particular linkages (that can be aggregated) and numerical vector data only.class
MiniMaxAnderberg<O>
This is a modification of the classic MiniMax algorithm for hierarchical clustering using a nearest-neighbor heuristic for acceleration.class
OPTICSToHierarchical
Convert a OPTICS ClusterOrder to a hierarchical clustering.class
SLINK<O>
Implementation of the efficient Single-Link Algorithm SLINK of R.class
SLINKHDBSCANLinearMemory<O>
Linear memory implementation of HDBSCAN clustering based on SLINK. -
Uses of Reference in elki.clustering.hierarchical.birch
Classes in elki.clustering.hierarchical.birch with annotations of type Reference Modifier and Type Class Description class
AverageInterclusterDistance
Average intercluster distance.class
AverageIntraclusterDistance
Average intracluster distance.class
CentroidEuclideanDistance
Centroid Euclidean distance.class
CentroidManhattanDistance
Centroid Manhattan Distanceclass
DiameterCriterion
Average Radius (R) criterion.class
RadiusCriterion
Average Radius (R) criterion.class
VarianceIncreaseDistance
Variance increase distance. -
Uses of Reference in elki.clustering.hierarchical.extraction
Classes in elki.clustering.hierarchical.extraction with annotations of type Reference Modifier and Type Class Description class
ClustersWithNoiseExtraction
Extraction of a given number of clusters with a minimum size, and noise.class
SimplifiedHierarchyExtraction
Extraction of simplified cluster hierarchies, as proposed in HDBSCAN. -
Uses of Reference in elki.clustering.hierarchical.linkage
Classes in elki.clustering.hierarchical.linkage with annotations of type Reference Modifier and Type Class Description class
CentroidLinkage
Centroid linkage — Unweighted Pair-Group Method using Centroids (UPGMC).class
FlexibleBetaLinkage
Flexible-beta linkage as proposed by Lance and Williams.class
GroupAverageLinkage
Group-average linkage clustering method (UPGMA).interface
Linkage
Abstract interface for implementing a new linkage method into hierarchical clustering.class
MedianLinkage
Median-linkage — weighted pair group method using centroids (WPGMC).class
SingleLinkage
Single-linkage ("minimum") clustering method.class
WeightedAverageLinkage
Weighted average linkage clustering method (WPGMA). -
Uses of Reference in elki.clustering.kmeans
Classes in elki.clustering.kmeans with annotations of type Reference Modifier and Type Class Description class
BetulaLloydKMeans
BIRCH/BETULA-based clustering algorithm that simply treats the leafs of the CFTree as clusters.class
BisectingKMeans<V extends NumberVector,M extends MeanModel>
The bisecting k-means algorithm works by starting with an initial partitioning into two clusters, then repeated splitting of the largest cluster to get additional clusters.class
CompareMeans<V extends NumberVector>
Compare-Means: Accelerated k-means by exploiting the triangle inequality and pairwise distances of means to prune candidate means.class
ElkanKMeans<V extends NumberVector>
Elkan's fast k-means by exploiting the triangle inequality.class
ExponionKMeans<V extends NumberVector>
Newlings's Exponion k-means algorithm, exploiting the triangle inequality.class
GMeans<V extends NumberVector,M extends MeanModel>
G-Means extends K-Means and estimates the number of centers with Anderson Darling Test.
Implemented as specialization of XMeans.class
HamerlyKMeans<V extends NumberVector>
Hamerly's fast k-means by exploiting the triangle inequality.class
HartiganWongKMeans<V extends NumberVector>
Hartigan and Wong k-means clustering.class
KMeansMinusMinus<V extends NumberVector>
k-means--: A Unified Approach to Clustering and Outlier Detection.class
KMediansLloyd<V extends NumberVector>
k-medians clustering algorithm, but using Lloyd-style bulk iterations instead of the more complicated approach suggested by Kaufman and Rousseeuw (seePAM
instead).class
MacQueenKMeans<V extends NumberVector>
The original k-means algorithm, using MacQueen style incremental updates; making this effectively an "online" (streaming) algorithm.class
ShallotKMeans<V extends NumberVector>
Borgelt's Shallot k-means algorithm, exploiting the triangle inequality.class
SimplifiedElkanKMeans<V extends NumberVector>
Simplified version of Elkan's k-means by exploiting the triangle inequality.class
SortMeans<V extends NumberVector>
Sort-Means: Accelerated k-means by exploiting the triangle inequality and pairwise distances of means to prune candidate means (with sorting).class
XMeans<V extends NumberVector,M extends MeanModel>
X-means: Extending K-means with Efficient Estimation on the Number of Clusters.class
YinYangKMeans<V extends NumberVector>
Yin-Yang k-Means Clustering. -
Uses of Reference in elki.clustering.kmeans.initialization
Classes in elki.clustering.kmeans.initialization with annotations of type Reference Modifier and Type Class Description class
AFKMC2
AFK-MC² initializationclass
FirstK<O>
Initialize K-means by using the first k objects as initial means.class
KMC2
K-MC² initializationclass
KMeansPlusPlus<O>
K-Means++ initialization for k-means.class
RandomNormalGenerated
Initialize k-means by generating random vectors (normal distributed with \(N(\mu,\sigma)\) in each dimension).class
RandomUniformGenerated
Initialize k-means by generating random vectors (uniform, within the value range of the data set).class
SampleKMeans<V extends NumberVector>
Initialize k-means by running k-means on a sample of the data set only.class
SphericalAFKMC2
Spherical K-Means++ initialization with markov chains.class
SphericalKMeansPlusPlus<O>
Spherical K-Means++ initialization for k-means. -
Uses of Reference in elki.clustering.kmeans.initialization.betula
Classes in elki.clustering.kmeans.initialization.betula with annotations of type Reference Modifier and Type Class Description class
CFKPlusPlusLeaves
K-Means++-like initialization for BETULA k-means, treating the leaf clustering features as a flat list, and called "leaves" in the publication.class
CFKPlusPlusTree
Initialize K-means by following tree paths weighted by their variance contribution.class
CFKPlusPlusTrunk
Trunk strategy for initializing k-means with BETULA: only the nodes up to a particular level are considered for k-means++ style initialization.class
CFRandomlyChosen
Initialize K-means by randomly choosing k existing elements as initial cluster centers for Clustering Features.class
CFWeightedRandomlyChosen
Initialize K-means by randomly choosing k existing elements as initial cluster centers for Clustering Features.class
InterclusterWeight
Initialization via n2 * D2²(cf1, cf2), which supposedly is closes to the idea of k-means++ initialization.class
SquaredEuclideanWeight
Use the squared Euclidean distance only for distance measurement.class
VarianceWeight
Variance-based weighting scheme for k-means clustering with BETULA. -
Uses of Reference in elki.clustering.kmeans.quality
Classes in elki.clustering.kmeans.quality with annotations of type Reference Modifier and Type Class Description class
AkaikeInformationCriterionXMeans
Akaike Information Criterion (AIC).class
BayesianInformationCriterion
Bayesian Information Criterion (BIC), also known as Schwarz criterion (SBC, SBIC) for the use with evaluating k-means results.class
BayesianInformationCriterionXMeans
Bayesian Information Criterion (BIC), also known as Schwarz criterion (SBC, SBIC) for the use with evaluating k-means results.class
BayesianInformationCriterionZhao
Different version of the BIC criterion.Methods in elki.clustering.kmeans.quality with annotations of type Reference Modifier and Type Method Description static double
AbstractKMeansQualityMeasure. logLikelihood(Relation<? extends NumberVector> relation, Clustering<? extends MeanModel> clustering, NumberVectorDistance<?> distance)
Computes log likelihood of an entire clustering. -
Uses of Reference in elki.clustering.kmeans.spherical
Classes in elki.clustering.kmeans.spherical with annotations of type Reference Modifier and Type Class Description class
EuclideanSphericalElkanKMeans<V extends NumberVector>
Elkan's fast k-means by exploiting the triangle inequality in the corresponding Euclidean space.class
EuclideanSphericalHamerlyKMeans<V extends NumberVector>
A spherical k-Means algorithm based on Hamerly's fast k-means by exploiting the triangle inequality in the corresponding Euclidean space.class
EuclideanSphericalSimplifiedElkanKMeans<V extends NumberVector>
A spherical k-Means algorithm based on Hamerly's fast k-means by exploiting the triangle inequality in the corresponding Euclidean space.class
SphericalKMeans<V extends NumberVector>
The standard spherical k-means algorithm. -
Uses of Reference in elki.clustering.kmedoids
Classes in elki.clustering.kmedoids with annotations of type Reference Modifier and Type Class Description class
CLARANS<O>
CLARANS: a method for clustering objects for spatial data mining is inspired by PAM (partitioning around medoids,PAM
) and CLARA and also based on sampling.class
FastCLARA<V>
Clustering Large Applications (CLARA) with theFastPAM
improvements, to increase scalability in the number of clusters.class
FastCLARANS<V>
A faster variation of CLARANS, that can explore O(k) as many swaps at a similar cost by considering all medoids for each candidate non-medoid.class
FasterCLARA<O>
Clustering Large Applications (CLARA) with theFastPAM
improvements, to increase scalability in the number of clusters.class
FasterPAM<O>
Variation of FastPAM that eagerly performs any swap that yields an improvement during an iteration.class
FastPAM<O>
FastPAM: An improved version of PAM, that is usually O(k) times faster.class
FastPAM1<O>
FastPAM1: A version of PAM that is O(k) times faster, i.e., now in O((n-k)²).class
ReynoldsPAM<O>
The Partitioning Around Medoids (PAM) algorithm with some additional optimizations proposed by Reynolds et al. -
Uses of Reference in elki.clustering.kmedoids.initialization
Classes in elki.clustering.kmedoids.initialization with annotations of type Reference Modifier and Type Class Description class
GreedyG<O>
Initialization method for k-medoids that combines the Greedy (PAMBUILD
) with "alternate" refinement steps.class
LAB<O>
Linear approximative BUILD (LAB) initialization for FastPAM (and k-means).class
ParkJun<O>
Initialization method proposed by Park and Jun. -
Uses of Reference in elki.clustering.optics
Classes in elki.clustering.optics with annotations of type Reference Modifier and Type Class Description class
AbstractOPTICS<O>
The OPTICS algorithm for density-based hierarchical clustering.class
DeLiClu<V extends NumberVector>
DeliClu: Density-Based Hierarchical Clusteringclass
FastOPTICS<V extends NumberVector>
FastOPTICS algorithm (Fast approximation of OPTICS)class
OPTICSHeap<O>
The OPTICS algorithm for density-based hierarchical clustering.class
OPTICSList<O>
The OPTICS algorithm for density-based hierarchical clustering. -
Uses of Reference in elki.clustering.silhouette
Classes in elki.clustering.silhouette with annotations of type Reference Modifier and Type Class Description class
FasterMSC<O>
Fast and Eager Medoid Silhouette Clustering.class
FastMSC<O>
Fast Medoid Silhouette Clustering. -
Uses of Reference in elki.clustering.subspace
Classes in elki.clustering.subspace with annotations of type Reference Modifier and Type Class Description class
CLIQUE
Implementation of the CLIQUE algorithm, a grid-based algorithm to identify dense clusters in subspaces of maximum dimensionality.class
DiSH
Algorithm for detecting subspace hierarchies.class
DOC
DOC is a sampling based subspace clustering algorithm.class
FastDOC
The heuristic variant of the DOC algorithm, FastDOCclass
HiSC
Implementation of the HiSC algorithm, an algorithm for detecting hierarchies of subspace clusters.class
P3C
P3C: A Robust Projected Clustering Algorithm.class
PreDeCon
PreDeCon computes clusters of subspace preference weighted connected points.class
PROCLUS
The PROCLUS algorithm, an algorithm to find subspace clusters in high dimensional spaces.class
SUBCLU<V extends NumberVector>
Implementation of the SUBCLU algorithm, an algorithm to detect arbitrarily shaped and positioned clusters in subspaces. -
Uses of Reference in elki.clustering.uncertain
Classes in elki.clustering.uncertain with annotations of type Reference Modifier and Type Class Description class
CenterOfMassMetaClustering<C extends Clustering<?>>
Center-of-mass meta clustering reduces uncertain objects to their center of mass, then runs a vector-oriented clustering algorithm on this data set.class
CKMeans
Run k-means on the centers of each uncertain object.class
FDBSCAN
FDBSCAN is an adaption of DBSCAN for fuzzy (uncertain) objects.class
FDBSCANNeighborPredicate
Density-based Clustering of Applications with Noise and Fuzzy objects (FDBSCAN) is an Algorithm to find sets in a fuzzy database that are density-connected with minimum probability.class
RepresentativeUncertainClustering
Representative clustering of uncertain data.class
UKMeans
Uncertain K-Means clustering, using the average deviation from the center. -
Uses of Reference in elki.data.projection.random
Classes in elki.data.projection.random with annotations of type Reference Modifier and Type Class Description class
AchlioptasRandomProjectionFamily
Random projections as suggested by Dimitris Achlioptas.class
CauchyRandomProjectionFamily
Random projections using Cauchy distributions (1-stable).class
GaussianRandomProjectionFamily
Random projections using Cauchy distributions (1-stable).class
RandomSubsetProjectionFamily
Random projection family based on selecting random features.class
SimplifiedRandomHyperplaneProjectionFamily
Random hyperplane projection family. -
Uses of Reference in elki.database.ids.integer
Classes in elki.database.ids.integer with annotations of type Reference Modifier and Type Class Description (package private) class
IntegerDBIDArrayQuickSort
Class to sort an integer DBID array, using a modified quicksort. -
Uses of Reference in elki.datasource.filter.transform
Classes in elki.datasource.filter.transform with annotations of type Reference Modifier and Type Class Description class
LinearDiscriminantAnalysisFilter<V extends NumberVector>
Linear Discriminant Analysis (LDA) / Fisher's linear discriminant.class
PerturbationFilter<V extends NumberVector>
A filter to perturb the values by adding micro-noise. -
Uses of Reference in elki.distance
Classes in elki.distance with annotations of type Reference Modifier and Type Class Description class
CanberraDistance
Canberra distance function, a variation of Manhattan distance.class
ClarkDistance
Clark distance function for vector spaces.class
MahalanobisDistance
Mahalanobis quadratic form distance for feature vectors. -
Uses of Reference in elki.distance.colorhistogram
Classes in elki.distance.colorhistogram with annotations of type Reference Modifier and Type Class Description class
HistogramIntersectionDistance
Intersection distance for color histograms.class
HSBHistogramQuadraticDistance
Distance function for HSB color histograms based on a quadratic form and color similarity.class
RGBHistogramQuadraticDistance
Distance function for RGB color histograms based on a quadratic form and color similarity. -
Uses of Reference in elki.distance.geo
Classes in elki.distance.geo with annotations of type Reference Modifier and Type Class Description class
DimensionSelectingLatLngDistance
Distance function for 2D vectors in Latitude, Longitude form.class
LatLngDistance
Distance function for 2D vectors in Latitude, Longitude form.class
LngLatDistance
Distance function for 2D vectors in Longitude, Latitude form. -
Uses of Reference in elki.distance.histogram
Classes in elki.distance.histogram with annotations of type Reference Modifier and Type Class Description class
HistogramMatchDistance
Distance function based on histogram matching, i.e., Manhattan distance on the cumulative density function. -
Uses of Reference in elki.distance.probabilistic
Classes in elki.distance.probabilistic with annotations of type Reference Modifier and Type Class Description class
ChiSquaredDistance
χ² distance function, symmetric version.class
KullbackLeiblerDivergenceAsymmetricDistance
Kullback-Leibler divergence, also known as relative entropy, information deviation, or just KL-distance (albeit asymmetric).class
KullbackLeiblerDivergenceReverseAsymmetricDistance
Kullback-Leibler divergence, also known as relative entropy, information deviation or just KL-distance (albeit asymmetric).class
SqrtJensenShannonDivergenceDistance
The square root of Jensen-Shannon divergence is a metric.class
TriangularDiscriminationDistance
Triangular Discrimination has relatively tight upper and lower bounds to the Jensen-Shannon divergence, but is much less expensive.class
TriangularDistance
Triangular Distance has relatively tight upper and lower bounds to the (square root of the) Jensen-Shannon divergence, but is much less expensive. -
Uses of Reference in elki.distance.set
Classes in elki.distance.set with annotations of type Reference Modifier and Type Class Description class
HammingDistance
Computes the Hamming distance of arbitrary vectors - i.e. counting, on how many places they differ.class
JaccardSimilarityDistance
A flexible extension of Jaccard similarity to non-binary vectors. -
Uses of Reference in elki.distance.strings
Classes in elki.distance.strings with annotations of type Reference Modifier and Type Class Description class
LevenshteinDistance
Classic Levenshtein distance on strings.class
NormalizedLevenshteinDistance
Levenshtein distance on strings, normalized by string length. -
Uses of Reference in elki.distance.timeseries
Classes in elki.distance.timeseries with annotations of type Reference Modifier and Type Class Description class
DerivativeDTWDistance
Derivative Dynamic Time Warping distance for numerical vectors.class
DTWDistance
Dynamic Time Warping distance (DTW) for numerical vectors.class
EDRDistance
Edit Distance on Real Sequence distance for numerical vectors.class
ERPDistance
Edit Distance With Real Penalty distance for numerical vectors.class
LCSSDistance
Longest Common Subsequence distance for numerical vectors. -
Uses of Reference in elki.evaluation.clustering
Classes in elki.evaluation.clustering with annotations of type Reference Modifier and Type Class Description class
EditDistance
Edit distance measures.class
MaximumMatchingAccuracy
Calculates the accuracy of a clustering based on the maximum set matching found by the Hungarian algorithm.class
PairSetsIndex
The Pair Sets Index calculates an index based on the maximum matching of relative cluster sizes by the Hungarian algorithm.class
SetMatchingPurity
Set matching purity measures.Methods in elki.evaluation.clustering with annotations of type Reference Modifier and Type Method Description double
PairCounting. adjustedRandIndex()
Computes the adjusted Rand index (ARI).double
SetMatchingPurity. f1Measure()
Get the set matching F1-Measuredouble
SetMatchingPurity. fMeasureFirst()
Get the Van Rijsbergen’s F measure (asymmetric) for first clusteringdouble
SetMatchingPurity. fMeasureSecond()
Get the Van Rijsbergen’s F measure (asymmetric) for second clusteringdouble
PairCounting. fowlkesMallows()
Computes the pair-counting Fowlkes-mallows (flat only, non-hierarchical!)double
Entropy. geometricNMI()
Get the geometric mean normalized mutual information (using the square root).double
PairCounting. jaccard()
Computes the Jaccard indexdouble
Entropy. jointNMI()
Get the joint-normalized mutual information.double
Entropy. maxNMI()
Get the max-normalized mutual information.double
Entropy. minNMI()
Get the min-normalized mutual information.long
PairCounting. mirkin()
Computes the Mirkin index, aka Equivalence Mismatch Distance.double
SetMatchingPurity. purity()
Get the set matchings purity (first:second clustering) (normalized, 1 = equal)double
PairCounting. randIndex()
Computes the Rand index (RI). -
Uses of Reference in elki.evaluation.clustering.internal
Classes in elki.evaluation.clustering.internal with annotations of type Reference Modifier and Type Class Description class
CIndex<O>
Compute the C-index of a data set.class
ConcordantPairsGammaTau
Compute the Gamma Criterion of a data set.class
DaviesBouldinIndex
Compute the Davies-Bouldin index of a data set.class
DBCV<O>
Compute the Density-Based Clustering Validation Index.class
PBMIndex
Compute the PBM index of a clusteringclass
Silhouette<O>
Compute the silhouette of a data set.class
VarianceRatioCriterion
Compute the Variance Ratio Criterion of a data set, also known as Calinski-Harabasz index.Methods in elki.evaluation.clustering.internal with annotations of type Reference Modifier and Type Method Description double
ConcordantPairsGammaTau. computeTau(long c, long d, double m, long wd, long bd)
Compute the Tau correlation measure -
Uses of Reference in elki.evaluation.clustering.pairsegments
Classes in elki.evaluation.clustering.pairsegments with annotations of type Reference Modifier and Type Class Description class
ClusterPairSegmentAnalysis
Evaluate clustering results by building segments for their pairs: shared pairs and differences.class
Segments
Creates segments of two or more clusterings. -
Uses of Reference in elki.evaluation.outlier
Classes in elki.evaluation.outlier with annotations of type Reference Modifier and Type Class Description class
OutlierPrecisionRecallCurve
Compute a curve containing the precision values for an outlier detection method.class
OutlierPrecisionRecallGainCurve
Compute a curve containing the precision gain and revall gain values for an outlier detection method.class
OutlierSmROCCurve
Smooth ROC curves are a variation of classic ROC curves that takes the scores into account. -
Uses of Reference in elki.evaluation.scores
Classes in elki.evaluation.scores with annotations of type Reference Modifier and Type Class Description class
DCGEvaluation
Discounted Cumulative Gain.class
NDCGEvaluation
Normalized Discounted Cumulative Gain.class
PRGCEvaluation
Compute the area under the precision-recall-gain curve -
Uses of Reference in elki.index.laesa
Classes in elki.index.laesa with annotations of type Reference Modifier and Type Class Description class
LAESA<O>
Linear Approximating and Eliminating Search Algorithm -
Uses of Reference in elki.index.lsh.hashfamilies
Classes in elki.index.lsh.hashfamilies with annotations of type Reference Modifier and Type Class Description class
EuclideanHashFunctionFamily
2-stable hash function family for Euclidean distances.class
ManhattanHashFunctionFamily
2-stable hash function family for Euclidean distances. -
Uses of Reference in elki.index.lsh.hashfunctions
Classes in elki.index.lsh.hashfunctions with annotations of type Reference Modifier and Type Class Description class
CosineLocalitySensitiveHashFunction
Random projection family to use with sparse vectors.class
MultipleProjectionsLocalitySensitiveHashFunction
LSH hash function for vector space data. -
Uses of Reference in elki.index.preprocessed.fastoptics
Classes in elki.index.preprocessed.fastoptics with annotations of type Reference Modifier and Type Class Description class
RandomProjectedNeighborsAndDensities
Random Projections used for computing neighbors and density estimates. -
Uses of Reference in elki.index.preprocessed.knn
Classes in elki.index.preprocessed.knn with annotations of type Reference Modifier and Type Class Description class
NaiveProjectedKNNPreprocessor<O extends NumberVector>
Compute the approximate k nearest neighbors using 1 dimensional projections.class
NNDescent<O>
NN-descent (also known as KNNGraph) is an approximate nearest neighbor search algorithm beginning with a random sample, then iteratively refining this sample until.class
RandomSampleKNNPreprocessor<O>
Class that computed the kNN only on a random sample.class
SpacefillingKNNPreprocessor<O extends NumberVector>
Compute the nearest neighbors approximatively using space filling curves.class
SpacefillingMaterializeKNNPreprocessor<O extends NumberVector>
Compute the nearest neighbors approximatively using space filling curves. -
Uses of Reference in elki.index.projected
Classes in elki.index.projected with annotations of type Reference Modifier and Type Class Description class
PINN<O extends NumberVector>
Projection-Indexed nearest-neighbors (PINN) is an index to retrieve the nearest neighbors in high dimensional spaces by using a random projection based index. -
Uses of Reference in elki.index.tree.betula.distance
Classes in elki.index.tree.betula.distance with annotations of type Reference Modifier and Type Class Description class
BIRCHAverageInterclusterDistance
Average intercluster distance.class
BIRCHAverageIntraclusterDistance
Average intracluster distance.class
BIRCHRadiusDistance
Average Radius (R) criterion.class
BIRCHVarianceIncreaseDistance
Variance increase distance. -
Uses of Reference in elki.index.tree.metrical.covertree
Classes in elki.index.tree.metrical.covertree with annotations of type Reference Modifier and Type Class Description class
CoverTree<O>
Cover tree data structure (in-memory). -
Uses of Reference in elki.index.tree.metrical.mtreevariants.mtree
Classes in elki.index.tree.metrical.mtreevariants.mtree with annotations of type Reference Modifier and Type Class Description class
MTree<O>
MTree is a metrical index structure based on the concepts of the M-Tree. -
Uses of Reference in elki.index.tree.metrical.mtreevariants.strategies.insert
Classes in elki.index.tree.metrical.mtreevariants.strategies.insert with annotations of type Reference Modifier and Type Class Description class
MinimumEnlargementInsert<N extends AbstractMTreeNode<?,N,E>,E extends MTreeEntry>
Minimum enlargement insert - default insertion strategy for the M-tree. -
Uses of Reference in elki.index.tree.metrical.mtreevariants.strategies.split
Classes in elki.index.tree.metrical.mtreevariants.strategies.split with annotations of type Reference Modifier and Type Class Description class
MLBDistSplit<E extends MTreeEntry,N extends AbstractMTreeNode<?,N,E>>
Encapsulates the required methods for a split of a node in an M-Tree.class
MMRadSplit<E extends MTreeEntry,N extends AbstractMTreeNode<?,N,E>>
Encapsulates the required methods for a split of a node in an M-Tree.class
MRadSplit<E extends MTreeEntry,N extends AbstractMTreeNode<?,N,E>>
Encapsulates the required methods for a split of a node in an M-Tree.class
MSTSplit<E extends MTreeEntry,N extends AbstractMTreeNode<?,N,E>>
Splitting algorithm using the minimum spanning tree (MST), as proposed by the Slim-Tree variant.class
RandomSplit<E extends MTreeEntry,N extends AbstractMTreeNode<?,N,E>>
Encapsulates the required methods for a split of a node in an M-Tree. -
Uses of Reference in elki.index.tree.metrical.mtreevariants.strategies.split.distribution
Classes in elki.index.tree.metrical.mtreevariants.strategies.split.distribution with annotations of type Reference Modifier and Type Class Description class
BalancedDistribution
Balanced entry distribution strategy of the M-tree.class
GeneralizedHyperplaneDistribution
Generalized hyperplane entry distribution strategy of the M-tree. -
Uses of Reference in elki.index.tree.metrical.vptree
Classes in elki.index.tree.metrical.vptree with annotations of type Reference Modifier and Type Class Description class
GNAT<O>
Geometric Near-neighbor Access Tree (GNAT), also known as Multi Vantage Point Tree or MVP-Tree.class
VPTree<O>
Vantage Point Tree with no additional information -
Uses of Reference in elki.index.tree.spatial.kd
Classes in elki.index.tree.spatial.kd with annotations of type Reference Modifier and Type Class Description class
MemoryKDTree<O extends NumberVector>
Implementation of a static in-memory K-D-tree.class
MemoryKDTree.KDTreeKNNSearcher
kNN query for the k-d-tree.class
MemoryKDTree.KDTreeRangeSearcher
Range query for the k-d-tree.class
MinimalisticMemoryKDTree<O extends NumberVector>
Simple implementation of a static in-memory K-D-tree.class
MinimalisticMemoryKDTree.KDTreeKNNSearcher
kNN query for the k-d-tree.class
MinimalisticMemoryKDTree.KDTreeRangeSearcher
Range query for the k-d-tree.class
SmallMemoryKDTree<O extends NumberVector>
Simple implementation of a static in-memory K-D-tree.class
SmallMemoryKDTree.KDTreeKNNSearcher
kNN query for the k-d-tree.class
SmallMemoryKDTree.KDTreeRangeSearcher
Range query for the k-d-tree. -
Uses of Reference in elki.index.tree.spatial.kd.split
Classes in elki.index.tree.spatial.kd.split with annotations of type Reference Modifier and Type Class Description class
MeanVarianceSplit
Split on the median of the axis with the largest variance.class
MedianVarianceSplit
Split on the median of the axis with the largest variance. -
Uses of Reference in elki.index.tree.spatial.rstarvariants.query
Classes in elki.index.tree.spatial.rstarvariants.query with annotations of type Reference Modifier and Type Class Description class
EuclideanRStarTreeKNNQuery<O extends NumberVector>
Instance of a KNN query for a particular spatial index.class
EuclideanRStarTreeRangeQuery<O extends NumberVector>
Instance of a range query for a particular spatial index.class
RStarTreeKNNSearcher<O extends SpatialComparable>
Instance of a KNN query for a particular spatial index.class
RStarTreeRangeSearcher<O extends SpatialComparable>
Instance of a range query for a particular spatial index. -
Uses of Reference in elki.index.tree.spatial.rstarvariants.rstar
Classes in elki.index.tree.spatial.rstarvariants.rstar with annotations of type Reference Modifier and Type Class Description class
RStarTree
RStarTree is a spatial index structure based on the concepts of the R*-Tree. -
Uses of Reference in elki.index.tree.spatial.rstarvariants.strategies.bulk
Classes in elki.index.tree.spatial.rstarvariants.strategies.bulk with annotations of type Reference Modifier and Type Class Description class
OneDimSortBulkSplit
Simple bulk loading strategy by sorting the data along the first dimension.class
SortTileRecursiveBulkSplit
Sort-Tile-Recursive aims at tiling the data space with a grid-like structure for partitioning the dataset into the required number of buckets.class
SpatialSortBulkSplit
Bulk loading by spatially sorting the objects, then partitioning the sorted list appropriately. -
Uses of Reference in elki.index.tree.spatial.rstarvariants.strategies.insert
Classes in elki.index.tree.spatial.rstarvariants.strategies.insert with annotations of type Reference Modifier and Type Class Description class
ApproximativeLeastOverlapInsertionStrategy
The choose subtree method proposed by the R*-Tree with slightly better performance for large leaf sizes (linear approximation).class
CombinedInsertionStrategy
Use two different insertion strategies for directory and leaf nodes.class
LeastEnlargementInsertionStrategy
The default R-Tree insertion strategy: find rectangle with least volume enlargement.class
LeastEnlargementWithAreaInsertionStrategy
A slight modification of the default R-Tree insertion strategy: find rectangle with least volume enlargement, but choose least area on ties.class
LeastOverlapInsertionStrategy
The choose subtree method proposed by the R*-Tree for leaf nodes. -
Uses of Reference in elki.index.tree.spatial.rstarvariants.strategies.overflow
Classes in elki.index.tree.spatial.rstarvariants.strategies.overflow with annotations of type Reference Modifier and Type Class Description class
LimitedReinsertOverflowTreatment
Limited reinsertions, as proposed by the R*-Tree: For each real insert, allow reinsertions to happen only once per level. -
Uses of Reference in elki.index.tree.spatial.rstarvariants.strategies.reinsert
Classes in elki.index.tree.spatial.rstarvariants.strategies.reinsert with annotations of type Reference Modifier and Type Class Description class
CloseReinsert
Reinsert objects on page overflow, starting with close objects first (even when they will likely be inserted into the same page again!)class
FarReinsert
Reinsert objects on page overflow, starting with farther objects first (even when they will likely be inserted into the same page again!) -
Uses of Reference in elki.index.tree.spatial.rstarvariants.strategies.split
Classes in elki.index.tree.spatial.rstarvariants.strategies.split with annotations of type Reference Modifier and Type Class Description class
AngTanLinearSplit
Line-time complexity split proposed by Ang and Tan.class
GreeneSplit
Quadratic-time complexity split as used by Diane Greene for the R-Tree.class
RTreeLinearSplit
Linear-time complexity greedy split as used by the original R-Tree.class
RTreeQuadraticSplit
Quadratic-time complexity greedy split as used by the original R-Tree.class
TopologicalSplitter
Encapsulates the required parameters for a topological split of a R*-Tree. -
Uses of Reference in elki.index.vafile
Classes in elki.index.vafile with annotations of type Reference Modifier and Type Class Description class
DAFile
Dimension approximation file, a one-dimensional part of thePartialVAFile
.class
PartialVAFile<V extends NumberVector>
PartialVAFile.class
VAFile<V extends NumberVector>
Vector-approximation file (VAFile) -
Uses of Reference in elki.itemsetmining
Classes in elki.itemsetmining with annotations of type Reference Modifier and Type Class Description class
APRIORI
The APRIORI algorithm for Mining Association Rules.class
Eclat
Eclat is a depth-first discovery algorithm for mining frequent itemsets.class
FPGrowth
FP-Growth is an algorithm for mining the frequent itemsets by using a compressed representation of the database calledFPGrowth.FPTree
. -
Uses of Reference in elki.itemsetmining.associationrules
Classes in elki.itemsetmining.associationrules with annotations of type Reference Modifier and Type Class Description class
AssociationRuleGeneration
Association rule generation from frequent itemsets -
Uses of Reference in elki.itemsetmining.associationrules.interest
Classes in elki.itemsetmining.associationrules.interest with annotations of type Reference Modifier and Type Class Description class
AddedValue
Added value (AV) interestingness measure: \( \text{confidence}(X \rightarrow Y) - \text{support}(Y) = P(Y|X)-P(Y) \).class
CertaintyFactor
Certainty factor (CF; Loevinger) interestingness measure. \( \tfrac{\text{confidence}(X \rightarrow Y) - \text{support}(Y)}{\text{support}(\neg Y)} \).class
Confidence
Confidence interestingness measure, \( \tfrac{\text{support}(X \cup Y)}{\text{support}(X)} = \tfrac{P(X \cap Y)}{P(X)}=P(Y|X) \).class
Conviction
Conviction interestingness measure: \(\frac{P(X) P(\neg Y)}{P(X\cap\neg Y)}\).class
Cosine
Cosine interestingness measure, \(\tfrac{\text{support}(A\cup B)}{\sqrt{\text{support}(A)\text{support}(B)}} =\tfrac{P(A\cap B)}{\sqrt{P(A)P(B)}}\).class
JMeasure
J-Measure interestingness measure.class
Klosgen
Klösgen interestingness measure.class
Leverage
Leverage interestingness measure.class
Lift
Lift interestingness measure.class
SebagSchonauer
Sebag Schonauer interestingness measure. -
Uses of Reference in elki.math
Methods in elki.math with annotations of type Reference Modifier and Type Method Description static double
Mean. highPrecision(double... data)
Static helper function, with extra precision -
Uses of Reference in elki.math.geodesy
Classes in elki.math.geodesy with annotations of type Reference Modifier and Type Class Description class
SphereUtil
Class with utility functions for distance computations on the sphere.Methods in elki.math.geodesy with annotations of type Reference Modifier and Type Method Description static double
SphereUtil. ellipsoidVincentyFormulaRad(double f, double lat1, double lon1, double lat2, double lon2)
Compute the approximate great-circle distance of two points.static double
SphereUtil. haversineFormulaRad(double lat1, double lon1, double lat2, double lon2)
Compute the approximate great-circle distance of two points using the Haversine formulastatic double
SphereUtil. latlngMinDistDeg(double plat, double plng, double rminlat, double rminlng, double rmaxlat, double rmaxlng)
Point to rectangle minimum distance.static double
SphereUtil. latlngMinDistRad(double plat, double plng, double rminlat, double rminlng, double rmaxlat, double rmaxlng)
Point to rectangle minimum distance.static double
SphereUtil. latlngMinDistRadFull(double plat, double plng, double rminlat, double rminlng, double rmaxlat, double rmaxlng)
Point to rectangle minimum distance.static double
SphereUtil. sphericalVincentyFormulaRad(double lat1, double lon1, double lat2, double lon2)
Compute the approximate great-circle distance of two points. -
Uses of Reference in elki.math.geometry
Classes in elki.math.geometry with annotations of type Reference Modifier and Type Class Description class
AlphaShape
Compute the alpha-shape of a point set, using Delaunay triangulation.class
GrahamScanConvexHull2D
Classes to compute the convex hull of a set of points in 2D, using the classic Grahams scan.class
PrimsMinimumSpanningTree
Prim's algorithm for finding the minimum spanning tree.class
SweepHullDelaunay2D
Compute the Convex Hull and/or Delaunay Triangulation, using the sweep-hull approach of David Sinclair. -
Uses of Reference in elki.math.linearalgebra
Methods in elki.math.linearalgebra with annotations of type Reference Modifier and Type Method Description static double
VMath. mahalanobisDistance(double[][] B, double[] a, double[] c)
Matrix multiplication, (a-c)T * B * (a-c) -
Uses of Reference in elki.math.linearalgebra.pca
Classes in elki.math.linearalgebra.pca with annotations of type Reference Modifier and Type Class Description class
AutotuningPCA
Performs a self-tuning local PCA based on the covariance matrices of given objects.class
WeightedCovarianceMatrixBuilder
CovarianceMatrixBuilder
with weights. -
Uses of Reference in elki.math.spacefillingcurves
Classes in elki.math.spacefillingcurves with annotations of type Reference Modifier and Type Class Description class
BinarySplitSpatialSorter
Spatially sort the data set by repetitive binary splitting, circulating through the dimensions.class
HilbertSpatialSorter
Sort object along the Hilbert Space Filling curve by mapping them to their Hilbert numbers and sorting them.class
PeanoSpatialSorter
Bulk-load an R-tree index by presorting the objects with their position on the Peano curve. -
Uses of Reference in elki.math.statistics.dependence
Classes in elki.math.statistics.dependence with annotations of type Reference Modifier and Type Class Description class
DCor
Distance correlation.class
HoeffdingsD
Calculate Hoeffding's D as a measure of dependence.class
HoughSpaceMeasure
HSM: Compute the "interestingness" of dimension connections using the Hough transformation.class
MaximumConditionalEntropy
Compute a mutual information based dependence measure using a nested means discretization, originally proposed for ordering axes in parallel coordinate plots.class
MCDEDependence
Implementation of bivariate Monte Carlo Density Estimation as described inclass
SlopeDependence
Arrange dimensions based on the entropy of the slope spectrum.class
SlopeInversionDependence
Arrange dimensions based on the entropy of the slope spectrum. -
Uses of Reference in elki.math.statistics.dependence.mcde
Classes in elki.math.statistics.dependence.mcde with annotations of type Reference Modifier and Type Class Description class
MWPTest
Implementation of Mann-Whitney U test returning the p-value (not the test statistic, thus MWP) forMCDEDependence
. -
Uses of Reference in elki.math.statistics.distribution
Classes in elki.math.statistics.distribution with annotations of type Reference Modifier and Type Class Description class
HaltonUniformDistribution
Halton sequences are a pseudo-uniform distribution.class
SkewGeneralizedNormalDistribution
Generalized normal distribution by adding a skew term, similar to lognormal distributions.Methods in elki.math.statistics.distribution with annotations of type Reference Modifier and Type Method Description static double
NormalDistribution. cdf(double x, double mu, double sigma)
Cumulative probability density function (CDF) of a normal distribution.protected static double
GammaDistribution. chisquaredProbitApproximation(double p, double nu, double g)
Approximate probit for chi squared distributionprivate 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 functionstatic 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 distributionstatic 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 Errorprivate static double
PoissonDistribution. stirlingError(int n)
Calculates the Stirling Error -
Uses of Reference in elki.math.statistics.distribution.estimator
Classes in elki.math.statistics.distribution.estimator with annotations of type Reference Modifier and Type Class Description class
CauchyMADEstimator
Estimate Cauchy distribution parameters using Median and MAD.class
EMGOlivierNorbergEstimator
Naive distribution estimation using mean and sample variance.class
ExponentialLMMEstimator
Estimate the parameters of a Gamma Distribution, using the methods of L-Moments (LMM).class
ExponentialMADEstimator
Estimate Exponential distribution parameters using Median and MAD.class
ExponentialMedianEstimator
Estimate Exponential distribution parameters using Median and MAD.class
GammaChoiWetteEstimator
Estimate distribution parameters using the method by Choi and Wette.class
GammaLMMEstimator
Estimate the parameters of a Gamma Distribution, using the methods of L-Moments (LMM).class
GammaMOMEstimator
Simple parameter estimation for the Gamma distribution.class
GeneralizedExtremeValueLMMEstimator
Estimate the parameters of a Generalized Extreme Value Distribution, using the methods of L-Moments (LMM).class
GeneralizedLogisticAlternateLMMEstimator
Estimate the parameters of a Generalized Logistic Distribution, using the methods of L-Moments (LMM).class
GeneralizedParetoLMMEstimator
Estimate the parameters of a Generalized Pareto Distribution (GPD), using the methods of L-Moments (LMM).class
GumbelLMMEstimator
Estimate the parameters of a Gumbel Distribution, using the methods of L-Moments (LMM).class
GumbelMADEstimator
Parameter estimation via median and median absolute deviation from median (MAD).class
LaplaceMADEstimator
Estimate Laplace distribution parameters using Median and MAD.class
LaplaceMLEEstimator
Estimate Laplace distribution parameters using Median and mean deviation from median.class
LogisticLMMEstimator
Estimate the parameters of a Logistic Distribution, using the methods of L-Moments (LMM).class
LogisticMADEstimator
Estimate Logistic distribution parameters using Median and MAD.class
LogLogisticMADEstimator
Estimate Logistic distribution parameters using Median and MAD.class
LogNormalBilkovaLMMEstimator
Alternate estimate the parameters of a log Gamma Distribution, using the methods of L-Moments (LMM) for the Generalized Normal Distribution.class
LogNormalLMMEstimator
Estimate the parameters of a log Normal Distribution, using the methods of L-Moments (LMM) for the Generalized Normal Distribution.class
LogNormalLogMADEstimator
Estimator using Medians.class
NormalLMMEstimator
Estimate the parameters of a normal distribution using the method of L-Moments (LMM).class
NormalMADEstimator
Estimator using Medians.class
RayleighMADEstimator
Estimate the parameters of a RayleighDistribution using the MAD.class
SkewGNormalLMMEstimator
Estimate the parameters of a skew Normal Distribution (Hoskin's Generalized Normal Distribution), using the methods of L-Moments (LMM).class
UniformMADEstimator
Estimate Uniform distribution parameters using Median and MAD.class
WeibullLogMADEstimator
Parameter estimation via median and median absolute deviation from median (MAD). -
Uses of Reference in elki.math.statistics.distribution.estimator.meta
Classes in elki.math.statistics.distribution.estimator.meta with annotations of type Reference Modifier and Type Class Description class
WinsorizingEstimator<D extends Distribution>
Winsorizing or Georgization estimator. -
Uses of Reference in elki.math.statistics.intrinsicdimensionality
Classes in elki.math.statistics.intrinsicdimensionality with annotations of type Reference Modifier and Type Class Description class
ABIDEstimator
Angle based intrinsic dimensionality (ABID) estimator.class
AggregatedHillEstimator
Estimator using the weighted average of multiple hill estimators.class
ALIDEstimator
ALID estimator of the intrinsic dimensionality (maximum likelihood estimator for ID using auxiliary distances).class
GEDEstimator
Generalized Expansion Dimension for estimating the intrinsic dimensionality.class
HillEstimator
Hill estimator of the intrinsic dimensionality (maximum likelihood estimator for ID).class
MOMEstimator
Methods of moments estimator, using the first moment (i.e. average).class
RABIDEstimator
Raw angle based intrinsic dimensionality (RABID) estimator.class
RVEstimator
Regularly Varying Functions estimator of the intrinsic dimensionalityclass
TightLIDEstimator
TightLID Estimator (TLE) of the intrinsic dimensionality (maximum likelihood estimator for ID using auxiliary distances). -
Uses of Reference in elki.math.statistics.kernelfunctions
Fields in elki.math.statistics.kernelfunctions with annotations of type Reference Modifier and Type Field Description static double
BiweightKernelDensityFunction. CANONICAL_BANDWIDTH
Canonical bandwidth: 35^(1/5)static double
EpanechnikovKernelDensityFunction. CANONICAL_BANDWIDTH
Canonical bandwidth: 15^(1/5)static double
GaussianKernelDensityFunction. CANONICAL_BANDWIDTH
Canonical bandwidth: (1./(4*pi))^(1/10)static double
TriweightKernelDensityFunction. CANONICAL_BANDWIDTH
Canonical bandwidth: (9450/143)^(1/5)static double
UniformKernelDensityFunction. CANONICAL_BANDWIDTH
Canonical bandwidth: (9/2)^(1/5)Methods in elki.math.statistics.kernelfunctions with annotations of type Reference Modifier and Type Method Description double
KernelDensityFunction. canonicalBandwidth()
Get the canonical bandwidth for this kernel. -
Uses of Reference in elki.math.statistics.tests
Classes in elki.math.statistics.tests with annotations of type Reference Modifier and Type Class Description class
AndersonDarlingTest
Perform Anderson-Darling test for a Gaussian distribution.Methods in elki.math.statistics.tests with annotations of type Reference Modifier and Type Method Description static double
AndersonDarlingTest. pValueCase0(double A2, int n)
Calculates the p-value for an Anderson Darling statistic in the case where both center and variance are known.static double
AndersonDarlingTest. pValueCase3(double A2)
Calculates the p-value for an Anderson Darling statistic in the case where both center and variance are unknown.static double
AndersonDarlingTest. removeBiasNormalDistribution(double A2, int n)
Remove bias from the Anderson-Darling statistic if the mean and standard deviation were estimated from the data, and a normal distribution was assumed.static double
AndersonDarlingTest. removeBiasNormalDistributionDAgostino(double A2, int n)
Remove bias from the Anderson-Darling statistic if the mean and standard deviation were estimated from the data, and a normal distribution was assumed. -
Uses of Reference in elki.outlier
Classes in elki.outlier with annotations of type Reference Modifier and Type Class Description class
COP<V extends NumberVector>
Correlation outlier probability: Outlier Detection in Arbitrarily Oriented Subspacesclass
DWOF<O>
Algorithm to compute dynamic-window outlier factors in a database based on a specified parameter k, which specifies the number of the neighbors to be considered during the calculation of the DWOF score.class
GaussianUniformMixture
Outlier detection algorithm using a mixture model approach.class
OPTICSOF<O>
OPTICS-OF outlier detection algorithm, an algorithm to find Local Outliers in a database based on ideas fromOPTICSTypeAlgorithm
clustering.class
SimpleCOP<V extends NumberVector>
Algorithm to compute local correlation outlier probability. -
Uses of Reference in elki.outlier.anglebased
Classes in elki.outlier.anglebased with annotations of type Reference Modifier and Type Class Description class
ABOD<V extends NumberVector>
Angle-Based Outlier Detection / Angle-Based Outlier Factor.class
FastABOD<V extends NumberVector>
Fast-ABOD (approximateABOF) version of Angle-Based Outlier Detection / Angle-Based Outlier Factor.class
LBABOD<V extends NumberVector>
LB-ABOD (lower-bound) version of Angle-Based Outlier Detection / Angle-Based Outlier Factor. -
Uses of Reference in elki.outlier.clustering
Classes in elki.outlier.clustering with annotations of type Reference Modifier and Type Class Description class
CBLOF<O extends NumberVector>
Cluster-based local outlier factor (CBLOF).class
GLOSH
Global-Local Outlier Scores from Hierarchies.class
KMeansMinusMinusOutlierDetection
k-means--: A Unified Approach to Clustering and Outlier Detection.class
SilhouetteOutlierDetection<O>
Outlier detection by using the Silhouette Coefficients. -
Uses of Reference in elki.outlier.density
Classes in elki.outlier.density with annotations of type Reference Modifier and Type Class Description class
HySortOD
Hypercube-Based Outlier Detection.class
IsolationForest
Isolation-Based Anomaly Detection. -
Uses of Reference in elki.outlier.distance
Classes in elki.outlier.distance with annotations of type Reference Modifier and Type Class Description class
AbstractDBOutlier<O>
Simple distance based outlier detection algorithms.class
DBOutlierDetection<O>
Simple distanced based outlier detection algorithm.class
DBOutlierScore<O>
Compute percentage of neighbors in the given neighborhood with size d.class
HilOut<O extends NumberVector>
Fast Outlier Detection in High Dimensional Spacesclass
KNNDD<O>
Nearest Neighbor Data Description.class
KNNOutlier<O>
Outlier Detection based on the distance of an object to its k nearest neighbor.class
KNNWeightOutlier<O>
Outlier Detection based on the accumulated distances of a point to its k nearest neighbors.class
LocalIsolationCoefficient<O>
The Local Isolation Coefficient is the sum of the kNN distance and the average distance to its k nearest neighbors.class
ODIN<O>
Outlier detection based on the in-degree of the kNN graph.class
ReferenceBasedOutlierDetection
Reference-Based Outlier Detection algorithm, an algorithm that computes kNN distances approximately, using reference points.class
SOS<O>
Stochastic Outlier Selection.Methods in elki.outlier.distance with annotations of type Reference Modifier and Type Method Description protected static double
SOS. estimateInitialBeta(DBIDRef ignore, DoubleDBIDListIter it, double perplexity)
Estimate beta from the distances in a row. -
Uses of Reference in elki.outlier.distance.parallel
Classes in elki.outlier.distance.parallel with annotations of type Reference Modifier and Type Class Description class
ParallelKNNOutlier<O>
Parallel implementation of KNN Outlier detection.class
ParallelKNNWeightOutlier<O>
Parallel implementation of KNN Weight Outlier detection. -
Uses of Reference in elki.outlier.intrinsic
Classes in elki.outlier.intrinsic with annotations of type Reference Modifier and Type Class Description class
IDOS<O>
Intrinsic Dimensional Outlier Detection in High-Dimensional Data.class
ISOS<O>
Intrinsic Stochastic Outlier Selection.class
LID<O>
Use intrinsic dimensionality for outlier detection. -
Uses of Reference in elki.outlier.lof
Classes in elki.outlier.lof with annotations of type Reference Modifier and Type Class Description class
ALOCI<V extends NumberVector>
Fast Outlier Detection Using the "approximate Local Correlation Integral".class
COF<O>
Connectivity-based Outlier Factor (COF).class
FlexibleLOF<O>
Flexible variant of the "Local Outlier Factor" algorithm.class
INFLO<O>
Influence Outliers using Symmetric Relationship (INFLO) using two-way search, is an outlier detection method based on LOF; but also using the reverse kNN.class
KDEOS<O>
Generalized Outlier Detection with Flexible Kernel Density Estimates.class
LDF<O extends NumberVector>
Outlier Detection with Kernel Density Functions.class
LDOF<O>
Computes the LDOF (Local Distance-Based Outlier Factor) for all objects of a Database.class
LOCI<O>
Fast Outlier Detection Using the "Local Correlation Integral".class
LOF<O>
Algorithm to compute density-based local outlier factors in a database based on a specified parameter-lof.k
.class
LoOP<O>
LoOP: Local Outlier Probabilitiesclass
SimplifiedLOF<O>
A simplified version of the original LOF algorithm, which does not use the reachability distance, yielding less stable results on inliers.class
VarianceOfVolume<O extends SpatialComparable>
Variance of Volume for outlier detection. -
Uses of Reference in elki.outlier.lof.parallel
Classes in elki.outlier.lof.parallel with annotations of type Reference Modifier and Type Class Description class
ParallelLOF<O>
Parallel implementation of Local Outlier Factor using processors.class
ParallelSimplifiedLOF<O>
Parallel implementation of Simplified-LOF Outlier detection using processors. -
Uses of Reference in elki.outlier.meta
Classes in elki.outlier.meta with annotations of type Reference Modifier and Type Class Description class
FeatureBagging
A simple ensemble method called "Feature bagging" for outlier detection.class
HiCS
Algorithm to compute High Contrast Subspaces for Density-Based Outlier Ranking. -
Uses of Reference in elki.outlier.spatial
Classes in elki.outlier.spatial with annotations of type Reference Modifier and Type Class Description class
CTLuGLSBackwardSearchAlgorithm<V extends NumberVector>
GLS-Backward Search is a statistical approach to detecting spatial outliers.class
CTLuMeanMultipleAttributes<N,O extends NumberVector>
Mean Approach is used to discover spatial outliers with multiple attributes.class
CTLuMedianAlgorithm<N>
Median Algorithm of C.class
CTLuMedianMultipleAttributes<N,O extends NumberVector>
Median Approach is used to discover spatial outliers with multiple attributes.class
CTLuMoranScatterplotOutlier<N>
Moran scatterplot outliers, based on the standardized deviation from the local and global means.class
CTLuRandomWalkEC<O>
Spatial outlier detection based on random walks.class
CTLuScatterplotOutlier<N>
Scatterplot-outlier is a spatial outlier detection method that performs a linear regression of object attributes and their neighbors average value.class
CTLuZTestOutlier<N>
Detect outliers by comparing their attribute value to the mean and standard deviation of their neighborhood.class
SLOM<N,O>
SLOM: a new measure for local spatial outliersclass
SOF<N,O>
The Spatial Outlier Factor (SOF) is a spatialLOF
variation.class
TrimmedMeanApproach<N>
A Trimmed Mean Approach to Finding Spatial Outliers. -
Uses of Reference in elki.outlier.subspace
Classes in elki.outlier.subspace with annotations of type Reference Modifier and Type Class Description class
AbstractAggarwalYuOutlier
Abstract base class for the sparse-grid-cell based outlier detection of Aggarwal and Yu.class
AggarwalYuEvolutionary
Evolutionary variant (EAFOD) of the high-dimensional outlier detection algorithm by Aggarwal and Yu.class
AggarwalYuNaive
BruteForce variant of the high-dimensional outlier detection algorithm by Aggarwal and Yu.class
OutRankS1
OutRank: ranking outliers in high dimensional data.class
OUTRES
Adaptive outlierness for subspace outlier ranking (OUTRES).class
SOD<V extends NumberVector>
Subspace Outlier Degree: Outlier Detection in Axis-Parallel Subspaces of High Dimensional Data. -
Uses of Reference in elki.outlier.svm
Classes in elki.outlier.svm with annotations of type Reference Modifier and Type Class Description class
LibSVMOneClassOutlierDetection<V extends NumberVector>
Outlier-detection using one-class support vector machines.class
OCSVM<V>
Outlier-detection using one-class support vector machines.class
SVDD<V>
Support Vector Data Description for outlier detection. -
Uses of Reference in elki.projection
Classes in elki.projection with annotations of type Reference Modifier and Type Class Description class
BarnesHutTSNE<O>
t-SNE using Barnes-Hut-Approximation.class
GaussianAffinityMatrixBuilder<O>
Compute the affinity matrix for SNE and tSNE using a Gaussian distribution with a constant sigma.class
IntrinsicNearestNeighborAffinityMatrixBuilder<O>
Build sparse affinity matrix using the nearest neighbors only, adjusting for intrinsic dimensionality.class
NearestNeighborAffinityMatrixBuilder<O>
Build sparse affinity matrix using the nearest neighbors only.class
PerplexityAffinityMatrixBuilder<O>
Compute the affinity matrix for SNE and tSNE.class
SNE<O>
Stochastic Neighbor Embedding is a projection technique designed for visualization that tries to preserve the nearest neighbor structure.class
TSNE<O>
t-Stochastic Neighbor Embedding is a projection technique designed for visualization that tries to preserve the nearest neighbor structure. -
Uses of Reference in elki.result
Classes in elki.result with annotations of type Reference Modifier and Type Class Description class
KMLOutputHandler
Class to handle KML output. -
Uses of Reference in elki.similarity
Classes in elki.similarity with annotations of type Reference Modifier and Type Class Description class
Kulczynski1Similarity
Kulczynski similarity 1.class
Kulczynski2Similarity
Kulczynski similarity 2. -
Uses of Reference in elki.similarity.cluster
Classes in elki.similarity.cluster with annotations of type Reference Modifier and Type Class Description class
ClusteringAdjustedRandIndexSimilarity
Measure the similarity of clusters via the Adjusted Rand Index.class
ClusteringBCubedF1Similarity
Measure the similarity of clusters via the BCubed F1 Index.class
ClusteringFowlkesMallowsSimilarity
Measure the similarity of clusters via the Fowlkes-Mallows Index.class
ClusteringRandIndexSimilarity
Measure the similarity of clusters via the Rand Index.class
ClusterJaccardSimilarity
Measure the similarity of clusters via the Jaccard coefficient. -
Uses of Reference in elki.timeseries
Classes in elki.timeseries with annotations of type Reference Modifier and Type Class Description class
SigniTrendChangeDetection
Signi-Trend detection algorithm applies to a single time-series. -
Uses of Reference in elki.utilities.datastructures
Classes in elki.utilities.datastructures with annotations of type Reference Modifier and Type Class Description class
KuhnMunkresStern
A version of Kuhn-Munkres inspired by the implementation of Kevin L.class
KuhnMunkresWong
Kuhn-Munkres optimal matching (aka the Hungarian algorithm), supposedly in a modern variant. -
Uses of Reference in elki.utilities.datastructures.arrays
Classes in elki.utilities.datastructures.arrays with annotations of type Reference Modifier and Type Class Description class
IntegerArrayQuickSort
Class to sort an int array, using a modified quicksort. -
Uses of Reference in elki.utilities.datastructures.unionfind
Classes in elki.utilities.datastructures.unionfind with annotations of type Reference Modifier and Type Class Description class
WeightedQuickUnionInteger
Union-find algorithm for primitive integers, with optimizations.class
WeightedQuickUnionRangeDBIDs
Union-find algorithm forDBIDRange
only, with optimizations.class
WeightedQuickUnionStaticDBIDs
Union-find algorithm forStaticDBIDs
, with optimizations. -
Uses of Reference in elki.utilities.documentation
Methods in elki.utilities.documentation that return Reference Modifier and Type Method Description Reference[]
value()
References of the class / field / method. -
Uses of Reference in elki.utilities.random
Classes in elki.utilities.random with annotations of type Reference Modifier and Type Class Description class
Xoroshiro128NonThreadsafeRandom
Replacement for Java'sRandom
class, using a different random number generation strategy.class
XorShift1024NonThreadsafeRandom
Replacement for Java'sRandom
class, using a different random number generation strategy.class
XorShift64NonThreadsafeRandom
Replacement for Java'sRandom
class, using a different random number generation strategy.Methods in elki.utilities.random with annotations of type Reference Modifier and Type Method Description int
Xoroshiro128NonThreadsafeRandom. nextInt(int n)
Returns a pseudorandom, uniformly distributedint
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 distributedint
value between 0 (inclusive) and the specified value (exclusive), drawn from this random number generator's sequence.int
XorShift64NonThreadsafeRandom. nextInt(int n)
Returns a pseudorandom, uniformly distributedint
value between 0 (inclusive) and the specified value (exclusive), drawn from this random number generator's sequence. -
Uses of Reference in elki.utilities.scaling.outlier
Classes in elki.utilities.scaling.outlier with annotations of type Reference Modifier and Type Class Description class
HeDESNormalizationOutlierScaling
Normalization used by HeDESclass
MinusLogGammaScaling
Scaling that can map arbitrary values to a probability in the range of [0:1], by assuming a Gamma distribution on the data and evaluating the Gamma CDF.class
MinusLogStandardDeviationScaling
Scaling that can map arbitrary values to a probability in the range of [0:1].class
MixtureModelOutlierScaling
Tries to fit a mixture model (exponential for inliers and gaussian for outliers) to the outlier score distribution.class
MultiplicativeInverseScaling
Scaling function to invert values by computing 1/x, but in a variation that maps the values to the [0:1] interval and avoiding division by 0.class
OutlierGammaScaling
Scaling that can map arbitrary values to a probability in the range of [0:1] by assuming a Gamma distribution on the values.class
OutlierMinusLogScaling
Scaling function to invert values by computing -log(x)class
SigmoidOutlierScaling
Tries to fit a sigmoid to the outlier scores and use it to convert the values to probability estimates in the range of 0.0 to 1.0class
SqrtStandardDeviationScaling
Scaling that can map arbitrary values to a probability in the range of [0:1].class
StandardDeviationScaling
Scaling that can map arbitrary values to a probability in the range of [0:1]. -
Uses of Reference in elki.visualization.parallel3d
Classes in elki.visualization.parallel3d with annotations of type Reference Modifier and Type Class Description class
OpenGL3DParallelCoordinates<O extends NumberVector>
Simple JOGL2 based parallel coordinates visualization.class
Parallel3DRenderer<O extends NumberVector>
Renderer for 3D parallel plots. -
Uses of Reference in elki.visualization.parallel3d.layout
Classes in elki.visualization.parallel3d.layout with annotations of type Reference Modifier and Type Class Description class
CompactCircularMSTLayout3DPC
Simple circular layout based on the minimum spanning tree.class
MultidimensionalScalingMSTLayout3DPC
Layout the axes by multi-dimensional scaling.class
SimpleCircularMSTLayout3DPC
Simple circular layout based on the minimum spanning tree. -
Uses of Reference in elki.visualization.projector
Classes in elki.visualization.projector with annotations of type Reference Modifier and Type Class Description class
ParallelPlotProjector<V extends SpatialComparable>
ParallelPlotProjector is responsible for producing a parallel axes visualization. -
Uses of Reference in elki.visualization.visualizers.pairsegments
Classes in elki.visualization.visualizers.pairsegments with annotations of type Reference Modifier and Type Class Description class
CircleSegmentsVisualizer
Visualizer to draw circle segments of clusterings and enable interactive selection of segments. -
Uses of Reference in elki.visualization.visualizers.scatterplot.density
Methods in elki.visualization.visualizers.scatterplot.density with annotations of type Reference Modifier and Type Method Description private double[]
DensityEstimationOverlay.Instance. initializeBandwidth(double[][] data)
-
Uses of Reference in elki.visualization.visualizers.scatterplot.outlier
Classes in elki.visualization.visualizers.scatterplot.outlier with annotations of type Reference Modifier and Type Class Description class
BubbleVisualization
Generates a SVG-Element containing bubbles.class
COPVectorVisualization
Visualize error vectors as produced by COP. -
Uses of Reference in tutorial.clustering
Classes in tutorial.clustering with annotations of type Reference Modifier and Type Class Description class
NaiveAgglomerativeHierarchicalClustering3<O>
This tutorial will step you through implementing a well known clustering algorithm, agglomerative hierarchical clustering, in multiple steps.class
NaiveAgglomerativeHierarchicalClustering4<O>
This tutorial will step you through implementing a well known clustering algorithm, agglomerative hierarchical clustering, in multiple steps. -
Uses of Reference in tutorial.outlier
Classes in tutorial.outlier with annotations of type Reference Modifier and Type Class Description class
ODIN<O>
Outlier detection based on the in-degree of the kNN graph.
-