Package  Description 

de.lmu.ifi.dbs.elki.algorithm 
Algorithms suitable as a task for the
KDDTask
main routine. 
de.lmu.ifi.dbs.elki.algorithm.benchmark 
Benchmarking pseudo algorithms.

de.lmu.ifi.dbs.elki.algorithm.classification 
Classification algorithms.

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.em 
ExpectationMaximization clustering algorithm.

de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan 
Generalized DBSCAN
Generalized DBSCAN is an abstraction of the original DBSCAN idea,
that allows the use of arbitrary "neighborhood" and "core point" predicates.

de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan.parallel 
Parallel versions of Generalized DBSCAN.

de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical 
Hierarchical agglomerative clustering (HAC).

de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.birch 
BIRCH clustering.

de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.extraction 
Extraction of partitional clusterings from hierarchical results.

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans 
Kmeans clustering and variations

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.parallel 
Parallelized implementations of kmeans.

de.lmu.ifi.dbs.elki.algorithm.clustering.meta 
Meta clustering algorithms, that get their result from other clusterings or external sources.

de.lmu.ifi.dbs.elki.algorithm.clustering.onedimensional 
Clustering algorithms for onedimensional data.

de.lmu.ifi.dbs.elki.algorithm.clustering.optics 
OPTICS family of clustering algorithms.

de.lmu.ifi.dbs.elki.algorithm.clustering.subspace 
Axisparallel 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 axisparallel subspaces.

de.lmu.ifi.dbs.elki.algorithm.clustering.trivial 
Trivial clustering algorithms: all in one, no clusters, label clusterings
These methods are mostly useful for providing a reference result in
evaluation.

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.outlier 
Outlier detection algorithms

de.lmu.ifi.dbs.elki.algorithm.outlier.anglebased 
Anglebased outlier detection algorithms.

de.lmu.ifi.dbs.elki.algorithm.outlier.clustering 
Clustering based outlier detection.

de.lmu.ifi.dbs.elki.algorithm.outlier.distance 
Distancebased outlier detection algorithms, such as DBOutlier and kNN.

de.lmu.ifi.dbs.elki.algorithm.outlier.distance.parallel 
Parallel implementations of distancebased 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 
SupportVectorMachines for outlier detection.

de.lmu.ifi.dbs.elki.algorithm.outlier.trivial 
Trivial outlier detection algorithms: no outliers, all outliers, label
outliers.

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.evaluation.clustering.extractor 
Classes to extract clusterings from hierarchical clustering.

de.lmu.ifi.dbs.elki.workflow 
Work flow packages, e.g., following the usual KDD model.

tutorial.clustering 
Classes from the tutorial on implementing a custom kmeans variation

tutorial.outlier 
Tutorials on implementing outlier detection methods in ELKI.

Modifier and Type  Interface and Description 

interface 
DistanceBasedAlgorithm<O>
Very broad interface for distance based algorithms.

Modifier and Type  Class and Description 

class 
AbstractAlgorithm<R extends Result>
This class serves also as a model of implementing an algorithm within this
framework.

class 
AbstractDistanceBasedAlgorithm<O,R extends Result>
Abstract base class for distancebased algorithms.

class 
AbstractNumberVectorDistanceBasedAlgorithm<O,R extends Result>
Abstract base class for distancebased algorithms that need to work with
synthetic numerical vectors such as mean vectors.

class 
AbstractPrimitiveDistanceBasedAlgorithm<O,R extends Result>
Abstract base class for distancebased algorithms that need to work with
synthetic objects such as mean vectors.

class 
DependencyDerivator<V extends NumberVector>
Dependency derivator computes quantitatively linear dependencies among
attributes of a given dataset based on a linear correlation PCA.

class 
DummyAlgorithm<O extends NumberVector>
Dummy algorithm, which just iterates over all points once, doing a 10NN query
each.

class 
KNNDistancesSampler<O>
Provides an order of the kNNdistances for all objects within the database.

class 
KNNJoin<V extends NumberVector,N extends SpatialNode<N,E>,E extends SpatialEntry>
Joins in a given spatial database to each object its knearest neighbors.

class 
NullAlgorithm
Null Algorithm, which does nothing.

Modifier and Type  Class and Description 

class 
KNNBenchmarkAlgorithm<O>
Benchmarking algorithm that computes the k nearest neighbors for each query
point.

class 
RangeQueryBenchmarkAlgorithm<O extends NumberVector>
Benchmarking algorithm that computes a range query for each point.

class 
ValidateApproximativeKNNIndex<O>
Algorithm to validate the quality of an approximative kNN index, by
performing a number of queries and comparing them to the results obtained by
exact indexing (e.g. linear scanning).

Modifier and Type  Interface and Description 

interface 
Classifier<O>
A Classifier is to hold a model that is built based on a database, and to
classify a new instance of the same type.

Modifier and Type  Class and Description 

class 
AbstractClassifier<O,R extends Result>
Abstract base class for algorithms.

class 
KNNClassifier<O>
KNNClassifier classifies instances based on the class distribution among the
k nearest neighbors in a database.

class 
PriorProbabilityClassifier
Classifier to classify instances based on the prior probability of classes in
the database, without using the actual data values.

Modifier and Type  Interface and Description 

interface 
ClusteringAlgorithm<C extends Clustering<? extends Model>>
Interface for Algorithms that are capable to provide a
Clustering as Result. in general, clustering algorithms are supposed to
implement the Algorithm Interface. 
Modifier and Type  Class and Description 

class 
AbstractProjectedClustering<R extends Clustering<?>,V extends NumberVector>

class 
CanopyPreClustering<O>
Canopy preclustering is a simple preprocessing step for clustering.

class 
DBSCAN<O>
DensityBased Clustering of Applications with Noise (DBSCAN), an algorithm to
find densityconnected sets in a database.

class 
GriDBSCAN<V extends NumberVector>
Using Grid for Accelerating DensityBased Clustering.

class 
Leader<O>
Leader clustering algorithm.

class 
NaiveMeanShiftClustering<V extends NumberVector>
Meanshift based clustering algorithm.

class 
SNNClustering<O>
Shared nearest neighbor clustering.

Modifier and Type  Class and Description 

class 
AffinityPropagationClusteringAlgorithm<O>
Cluster analysis by affinity propagation.

Modifier and Type  Class and Description 

class 
AbstractBiclustering<V extends NumberVector,M extends BiclusterModel>
Abstract class as a convenience for different biclustering approaches.

class 
ChengAndChurch<V extends NumberVector>
Cheng and Church biclustering.

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.

Modifier and Type  Class and Description 

class 
EM<V extends NumberVector,M extends MeanModel>
Clustering by expectation maximization (EMAlgorithm), also known as Gaussian
Mixture Modeling (GMM), with optional MAP regularization.

Modifier and Type  Class and Description 

class 
GeneralizedDBSCAN
Generalized DBSCAN, densitybased clustering with noise.

class 
LSDBC<O extends NumberVector>
Locally Scaled Density Based Clustering.

Modifier and Type  Class and Description 

class 
ParallelGeneralizedDBSCAN
Parallel version of DBSCAN clustering.

Modifier and Type  Interface and Description 

interface 
HierarchicalClusteringAlgorithm
Interface for hierarchical clustering algorithms.

Modifier and Type  Class and Description 

class 
AbstractHDBSCAN<O,R extends Result>
Abstract base class for HDBSCAN variations.

class 
AGNES<O>
Hierarchical Agglomerative Clustering (HAC) or Agglomerative Nesting (AGNES)
is a classic hierarchical clustering algorithm.

class 
AnderbergHierarchicalClustering<O>
This is a modification of the classic AGNES algorithm for hierarchical
clustering using a nearestneighbor heuristic for acceleration.

class 
CLINK<O>
CLINK algorithm for complete linkage.

class 
HDBSCANLinearMemory<O>
Linear memory implementation of HDBSCAN clustering.

class 
MiniMax<O>
Minimax Linkage clustering.

class 
MiniMaxAnderberg<O>
This is a modification of the classic MiniMax algorithm for hierarchical
clustering using a nearestneighbor heuristic for acceleration.

class 
MiniMaxNNChain<O>
MiniMax hierarchical clustering using the NNchain algorithm.

class 
NNChain<O>
NNchain clustering algorithm.

class 
SLINK<O>
Implementation of the efficient SingleLink Algorithm SLINK of R.

class 
SLINKHDBSCANLinearMemory<O>
Linear memory implementation of HDBSCAN clustering based on SLINK.

Modifier and Type  Class and Description 

class 
BIRCHLeafClustering
BIRCHbased clustering algorithm that simply treats the leafs of the CFTree
as clusters.

Modifier and Type  Class and Description 

class 
AbstractCutDendrogram
Abstract base class for extracting clusters from dendrograms.

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

class 
CutDendrogramByHeight
Extract a flat clustering from a full hierarchy, represented in pointer form.

class 
CutDendrogramByNumberOfClusters
Extract a flat clustering from a full hierarchy, represented in pointer form.

class 
HDBSCANHierarchyExtraction
Extraction of simplified cluster hierarchies, as proposed in HDBSCAN.

class 
SimplifiedHierarchyExtraction
Extraction of simplified cluster hierarchies, as proposed in HDBSCAN.

Modifier and Type  Interface and Description 

interface 
KMeans<V extends NumberVector,M extends Model>
Some constants and options shared among kmeans family algorithms.

Modifier and Type  Class and Description 

class 
AbstractKMeans<V extends NumberVector,M extends Model>
Abstract base class for kmeans implementations.

class 
BestOfMultipleKMeans<V extends NumberVector,M extends MeanModel>
Run KMeans multiple times, and keep the best run.

class 
CLARA<V>
Clustering Large Applications (CLARA) is a clustering method for large data
sets based on PAM, partitioning around medoids (
KMedoidsPAM ) based on
sampling. 
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 nonmedoid.

class 
KMeansAnnulus<V extends NumberVector>
Annulus kmeans algorithm.

class 
KMeansBisecting<V extends NumberVector,M extends MeanModel>
The bisecting kmeans 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>
CompareMeans: Accelerated kmeans by exploiting the triangle inequality and
pairwise distances of means to prune candidate means.

class 
KMeansElkan<V extends NumberVector>
Elkan's fast kmeans by exploiting the triangle inequality.

class 
KMeansExponion<V extends NumberVector>
Newlings's exponion kmeans algorithm, exploiting the triangle inequality.

class 
KMeansHamerly<V extends NumberVector>
Hamerly's fast kmeans by exploiting the triangle inequality.

class 
KMeansLloyd<V extends NumberVector>
The standard kmeans algorithm, using bulk iterations and commonly attributed
to Lloyd and Forgy (independently).

class 
KMeansMacQueen<V extends NumberVector>
The original kmeans algorithm, using MacQueen style incremental updates;
making this effectively an "online" (streaming) algorithm.

class 
KMeansMinusMinus<V extends NumberVector>
kmeans: A Unified Approach to Clustering and Outlier Detection.

class 
KMeansSimplifiedElkan<V extends NumberVector>
Simplified version of Elkan's kmeans by exploiting the triangle inequality.

class 
KMeansSort<V extends NumberVector>
SortMeans: Accelerated kmeans by exploiting the triangle inequality and
pairwise distances of means to prune candidate means (with sorting).

class 
KMediansLloyd<V extends NumberVector>
kmedians clustering algorithm, but using Lloydstyle 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((nk)Â²).

class 
KMedoidsPAM<V>
The original Partitioning Around Medoids (PAM) algorithm or kmedoids
clustering, as proposed by Kaufman and Rousseeuw in "Clustering by means of
Medoids".

class 
KMedoidsPAMReynolds<V>
The Partitioning Around Medoids (PAM) algorithm with some additional
optimizations proposed by Reynolds et al.

class 
KMedoidsPark<V>
A kmedoids clustering algorithm, implemented as EMstyle bulk algorithm.

class 
SingleAssignmentKMeans<V extends NumberVector>
PseudokMeans variations, that assigns each object to the nearest center.

class 
XMeans<V extends NumberVector,M extends MeanModel>
Xmeans: Extending Kmeans with Efficient Estimation on the Number of
Clusters.

Modifier and Type  Class and Description 

class 
ParallelLloydKMeans<V extends NumberVector>
Parallel implementation of kMeans clustering.

Modifier and Type  Class and Description 

class 
ExternalClustering
Read an external clustering result from a file, such as produced by
ClusteringVectorDumper . 
Modifier and Type  Class and Description 

class 
KNNKernelDensityMinimaClustering<V extends NumberVector>
Cluster onedimensional data by splitting the data set on local minima after
performing kernel density estimation.

Modifier and Type  Interface and Description 

interface 
OPTICSTypeAlgorithm
Interface for OPTICS type algorithms, that can be analyzed by OPTICS Xi etc.

Modifier and Type  Class and Description 

class 
AbstractOPTICS<O>
The OPTICS algorithm for densitybased hierarchical clustering.

class 
DeLiClu<V extends NumberVector>
DeliClu: DensityBased Hierarchical Clustering
A hierarchical algorithm to find densityconnected sets in a database,
closely related to OPTICS but exploiting the structure of a Rtree for
acceleration.

class 
FastOPTICS<V extends NumberVector>
FastOPTICS algorithm (Fast approximation of OPTICS)
Note that this is not FOPTICS as in "Fuzzy OPTICS"!

class 
GeneralizedOPTICS<O,R extends ClusterOrder>
A trivial generalization of OPTICS that is not restricted to numerical
distances, and serves as a base for several other algorithms (HiCO, HiSC).

class 
OPTICSHeap<O>
The OPTICS algorithm for densitybased hierarchical clustering.

class 
OPTICSList<O>
The OPTICS algorithm for densitybased hierarchical clustering.

class 
OPTICSXi
Extract clusters from OPTICS Plots using the original Xi extraction.

Modifier and Type  Interface and Description 

interface 
SubspaceClusteringAlgorithm<M extends SubspaceModel>
Interface for subspace clustering algorithms that use a model derived from
SubspaceModel , that can then be postprocessed for outlier detection. 
Modifier and Type  Class and Description 

class 
CLIQUE
Implementation of the CLIQUE algorithm, a gridbased 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.

Modifier and Type  Class and Description 

class 
ByLabelClustering
Pseudo clustering using labels.

class 
ByLabelHierarchicalClustering
Pseudo clustering using labels.

class 
ByLabelOrAllInOneClustering
Trivial class that will try to cluster by label, and fall back to an
"allinone" clustering.

class 
ByModelClustering
Pseudo clustering using annotated models.

class 
TrivialAllInOne
Trivial pseudoclustering that just considers all points to be one big
cluster.

class 
TrivialAllNoise
Trivial pseudoclustering that just considers all points to be noise.

Modifier and Type  Class and Description 

class 
CenterOfMassMetaClustering<C extends Clustering<?>>
Centerofmass meta clustering reduces uncertain objects to their center of
mass, then runs a vectororiented clustering algorithm on this data set.

class 
CKMeans
Run kmeans on the centers of each uncertain object.

class 
FDBSCAN
FDBSCAN is an adaption of DBSCAN for fuzzy (uncertain) objects.

class 
RepresentativeUncertainClustering
Representative clustering of uncertain data.

class 
UKMeans
Uncertain KMeans clustering, using the average deviation from the center.

Modifier and Type  Class and Description 

class 
AbstractFrequentItemsetAlgorithm
Abstract base class for frequent itemset mining.

class 
APRIORI
The APRIORI algorithm for Mining Association Rules.

class 
Eclat
Eclat is a depthfirst discovery algorithm for mining frequent itemsets.

class 
FPGrowth
FPGrowth is an algorithm for mining the frequent itemsets by using a
compressed representation of the database called
FPGrowth.FPTree . 
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.

Modifier and Type  Interface and Description 

interface 
OutlierAlgorithm
Generic super interface for outlier detection algorithms.

Modifier and Type  Class and Description 

class 
COP<V extends NumberVector>
Correlation outlier probability: Outlier Detection in Arbitrarily Oriented
Subspaces
Reference:
HansPeter Kriegel, Peer KrÃ¶ger, Erich Schubert, Arthur Zimek
Outlier Detection in Arbitrarily Oriented Subspaces Proc. 
class 
DWOF<O>
Algorithm to compute dynamicwindow 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 
GaussianModel<V extends NumberVector>
Outlier detection based on the probability density of the single normal
distribution.

class 
GaussianUniformMixture<V extends NumberVector>
Outlier detection algorithm using a mixture model approach.

class 
OPTICSOF<O>
OPTICSOF 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.

Modifier and Type  Class and Description 

class 
ABOD<V extends NumberVector>
AngleBased Outlier Detection / AngleBased Outlier Factor.

class 
FastABOD<V extends NumberVector>
FastABOD (approximateABOF) version of
AngleBased Outlier Detection / AngleBased Outlier Factor.

class 
LBABOD<V extends NumberVector>
LBABOD (lowerbound) version of
AngleBased Outlier Detection / AngleBased Outlier Factor.

Modifier and Type  Class and Description 

class 
CBLOF<O extends NumberVector>
Clusterbased local outlier factor (CBLOF).

class 
EMOutlier<V extends NumberVector>
Outlier detection algorithm using EM Clustering.

class 
KMeansOutlierDetection<O extends NumberVector>
Outlier detection by using kmeans clustering.

class 
SilhouetteOutlierDetection<O>
Outlier detection by using the Silhouette Coefficients.

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 
KNNSOS<O>
kNNbased adaption of Stochastic Outlier Selection.

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 indegree of the kNN graph.

class 
ReferenceBasedOutlierDetection
ReferenceBased Outlier Detection algorithm, an algorithm that computes kNN
distances approximately, using reference points.

class 
SOS<O>
Stochastic Outlier Selection.

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.

Modifier and Type  Class and Description 

class 
IDOS<O>
Intrinsic Dimensional Outlier Detection in HighDimensional Data.

class 
IntrinsicDimensionalityOutlier<O>
Use intrinsic dimensionality for outlier detection.

class 
ISOS<O>
Intrinsic Stochastic Outlier Selection.

Modifier and Type  Class and Description 

class 
ALOCI<O extends NumberVector>
Fast Outlier Detection Using the "approximate Local Correlation Integral".

class 
COF<O>
Connectivitybased Outlier Factor (COF).

class 
FlexibleLOF<O>
Flexible variant of the "Local Outlier Factor" algorithm.

class 
INFLO<O>
Influence Outliers using Symmetric Relationship (INFLO) using twoway 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 DistanceBased 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 densitybased 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 
OnlineLOF<O>
Incremental version of the
LOF Algorithm, supports insertions and
removals. 
class 
SimpleKernelDensityLOF<O extends NumberVector>
A simple variant of the LOF algorithm, which uses a simple kernel density
estimation instead of the local reachability density.

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.

Modifier and Type  Class and Description 

class 
ParallelLOF<O>
Parallel implementation of Local Outlier Factor using processors.

class 
ParallelSimplifiedLOF<O>
Parallel implementation of SimplifiedLOF Outlier detection using processors.

Modifier and Type  Class and Description 

class 
ExternalDoubleOutlierScore
External outlier detection scores, loading outlier scores from an external
file.

class 
FeatureBagging
A simple ensemble method called "Feature bagging" for outlier detection.

class 
HiCS<V extends NumberVector>
Algorithm to compute High Contrast Subspaces for DensityBased Outlier
Ranking.

class 
RescaleMetaOutlierAlgorithm
Scale another outlier score using the given scaling function.

class 
SimpleOutlierEnsemble
Simple outlier ensemble method.

Modifier and Type  Field and Description 

private Algorithm 
RescaleMetaOutlierAlgorithm.algorithm
Holds the algorithm to run.

private Algorithm 
RescaleMetaOutlierAlgorithm.Parameterizer.algorithm
Holds the algorithm to run.

Constructor and Description 

RescaleMetaOutlierAlgorithm(Algorithm algorithm,
ScalingFunction scaling)
Constructor.

Modifier and Type  Class and Description 

class 
AbstractDistanceBasedSpatialOutlier<N,O>
Abstract base class for distancebased spatial outlier detection methods.

class 
AbstractNeighborhoodOutlier<O>
Abstract base class for spatial outlier detection methods using a spatial
neighborhood.

class 
CTLuGLSBackwardSearchAlgorithm<V extends NumberVector>
GLSBackward 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>
Scatterplotoutlier 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.

Modifier and Type  Class and Description 

class 
AbstractAggarwalYuOutlier<V extends NumberVector>
Abstract base class for the sparsegridcell based outlier detection of
Aggarwal and Yu.

class 
AggarwalYuEvolutionary<V extends NumberVector>
Evolutionary variant (EAFOD) of the highdimensional outlier detection
algorithm by Aggarwal and Yu.

class 
AggarwalYuNaive<V extends NumberVector>
BruteForce variant of the highdimensional 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.

Modifier and Type  Class and Description 

class 
LibSVMOneClassOutlierDetection<V extends NumberVector>
Outlierdetection using oneclass support vector machines.

Modifier and Type  Class and Description 

class 
ByLabelOutlier
Trivial algorithm that marks outliers by their label.

class 
TrivialAllOutlier
Trivial method that claims all objects to be outliers.

class 
TrivialAverageCoordinateOutlier
Trivial method that takes the average of all dimensions (for onedimensional
data that is just the actual value!)

class 
TrivialGeneratedOutlier
Extract outlier score from the model the objects were generated by.

class 
TrivialNoOutlier
Trivial method that claims to find no outliers.

Modifier and Type  Class and Description 

class 
AbstractProjectionAlgorithm<R extends Result>
Abstract base class for projection algorithms.

class 
BarnesHutTSNE<O>
tSNE using BarnesHutApproximation.

class 
SNE<O>
Stochastic Neighbor Embedding is a projection technique designed for
visualization that tries to preserve the nearest neighbor structure.

class 
TSNE<O>
tStochastic Neighbor Embedding is a projection technique designed for
visualization that tries to preserve the nearest neighbor structure.

Modifier and Type  Class and Description 

class 
AddSingleScale
Pseudo "algorithm" that computes the global min/max for a relation across all
attributes.

class 
AddUniformScale
Pseudo "algorithm" that computes the global min/max for a relation across all
attributes.

class 
AveragePrecisionAtK<O>
Evaluate a distance functions performance by computing the average precision
at k, when ranking the objects by distance.

class 
DistanceQuantileSampler<O>
Compute a quantile of a distance sample, useful for choosing parameters for
algorithms.

class 
DistanceStatisticsWithClasses<O>
Algorithm to gather statistics over the distance distribution in the data
set.

class 
EstimateIntrinsicDimensionality<O>
Estimate global average intrinsic dimensionality of a data set.

class 
EvaluateRankingQuality<V extends NumberVector>
Evaluate a distance function with respect to kNN queries.

class 
EvaluateRetrievalPerformance<O>
Evaluate a distance functions performance by computing the mean average
precision, ROC, and NN classification performance when ranking the objects by
distance.

class 
HopkinsStatisticClusteringTendency
The Hopkins Statistic of Clustering Tendency measures the probability that a
data set is generated by a uniform data distribution.

class 
RangeQuerySelectivity<V extends NumberVector>
Evaluate the range query selectivity.

class 
RankingQualityHistogram<O>
Evaluate a distance function with respect to kNN queries.

Modifier and Type  Class and Description 

class 
OfflineChangePointDetectionAlgorithm
Offline change point detection algorithm detecting a change in mean, based
on the cumulative sum (CUSUM), samevariance assumption, and using bootstrap
sampling for significance estimation.

class 
SigniTrendChangeDetection
SigniTrend detection algorithm applies to a single timeseries.

Modifier and Type  Class and Description 

protected static class 
CutDendrogramByHeightExtractor.DummyHierarchicalClusteringAlgorithm
Dummy instance.

Modifier and Type  Field and Description 

private java.util.List<? extends Algorithm> 
AlgorithmStep.algorithms
Holds the algorithm to run.

protected java.util.List<? extends Algorithm> 
AlgorithmStep.Parameterizer.algorithms
Holds the algorithm to run.

Constructor and Description 

AlgorithmStep(java.util.List<? extends Algorithm> algorithms)
Constructor.

Modifier and Type  Class and Description 

class 
NaiveAgglomerativeHierarchicalClustering1<O>
This tutorial will step you through implementing a well known clustering
algorithm, agglomerative hierarchical clustering, in multiple steps.

class 
NaiveAgglomerativeHierarchicalClustering2<O>
This tutorial will step you through implementing a well known clustering
algorithm, agglomerative hierarchical clustering, in multiple steps.

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.

class 
SameSizeKMeansAlgorithm<V extends NumberVector>
Kmeans variation that produces equally sized clusters.

Modifier and Type  Class and Description 

class 
DistanceStddevOutlier<O>
A simple outlier detection algorithm that computes the standard deviation of
the kNN distances.

Copyright © 2019 ELKI Development Team. License information.