## Uses of Interfacede.lmu.ifi.dbs.elki.algorithm.Algorithm

• Packages that use Algorithm
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
Expectation-Maximization 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
K-means clustering and variations
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.parallel
Parallelized implementations of k-means.
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 one-dimensional data.
de.lmu.ifi.dbs.elki.algorithm.clustering.optics
OPTICS family of clustering algorithms.
de.lmu.ifi.dbs.elki.algorithm.clustering.subspace
Axis-parallel subspace clustering algorithms The clustering algorithms in this package are instances of both, projected clustering algorithms or subspace clustering algorithms according to the classical but somewhat obsolete classification schema of clustering algorithms for axis-parallel subspaces.
de.lmu.ifi.dbs.elki.algorithm.clustering.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
Angle-based outlier detection algorithms.
de.lmu.ifi.dbs.elki.algorithm.outlier.clustering
Clustering based outlier detection.
de.lmu.ifi.dbs.elki.algorithm.outlier.distance
Distance-based outlier detection algorithms, such as DBOutlier and kNN.
de.lmu.ifi.dbs.elki.algorithm.outlier.distance.parallel
Parallel implementations of distance-based outlier detectors.
de.lmu.ifi.dbs.elki.algorithm.outlier.intrinsic
Outlier detection algorithms based on intrinsic dimensionality.
de.lmu.ifi.dbs.elki.algorithm.outlier.lof
LOF family of outlier detection algorithms
de.lmu.ifi.dbs.elki.algorithm.outlier.lof.parallel
Parallelized variants of LOF.
de.lmu.ifi.dbs.elki.algorithm.outlier.meta
Meta outlier detection algorithms: external scores, score rescaling
de.lmu.ifi.dbs.elki.algorithm.outlier.spatial
Spatial outlier detection algorithms
de.lmu.ifi.dbs.elki.algorithm.outlier.subspace
Subspace outlier detection methods Methods that detect outliers in subspaces (projections) of the data set.
de.lmu.ifi.dbs.elki.algorithm.outlier.svm
Support-Vector-Machines for outlier detection.
de.lmu.ifi.dbs.elki.algorithm.outlier.trivial
Trivial outlier detection algorithms: no outliers, all outliers, label outliers.
de.lmu.ifi.dbs.elki.algorithm.projection
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 k-means variation
tutorial.outlier
Tutorials on implementing outlier detection methods in ELKI.
• ### Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm

Subinterfaces of Algorithm in de.lmu.ifi.dbs.elki.algorithm
Modifier and Type Interface and Description
interface  DistanceBasedAlgorithm<O>
Very broad interface for distance based algorithms.
Classes in de.lmu.ifi.dbs.elki.algorithm that implement Algorithm
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 distance-based algorithms.
class  AbstractNumberVectorDistanceBasedAlgorithm<O,R extends Result>
Abstract base class for distance-based algorithms that need to work with synthetic numerical vectors such as mean vectors.
class  AbstractPrimitiveDistanceBasedAlgorithm<O,R extends Result>
Abstract base class for distance-based 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 kNN-distances 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 k-nearest neighbors.
class  NullAlgorithm
Null Algorithm, which does nothing.
• ### Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.benchmark

Classes in de.lmu.ifi.dbs.elki.algorithm.benchmark that implement Algorithm
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).
• ### Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.classification

Subinterfaces of Algorithm in de.lmu.ifi.dbs.elki.algorithm.classification
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.
Classes in de.lmu.ifi.dbs.elki.algorithm.classification that implement Algorithm
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.
• ### Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.clustering

Subinterfaces of Algorithm in de.lmu.ifi.dbs.elki.algorithm.clustering
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.
Classes in de.lmu.ifi.dbs.elki.algorithm.clustering that implement Algorithm
Modifier and Type Class and Description
class  AbstractProjectedClustering<R extends Clustering<?>,V extends NumberVector>
Abstract superclass for projected clustering algorithms, like PROCLUS and ORCLUS.
class  CanopyPreClustering<O>
Canopy pre-clustering is a simple preprocessing step for clustering.
class  DBSCAN<O>
Density-Based Clustering of Applications with Noise (DBSCAN), an algorithm to find density-connected sets in a database.
class  GriDBSCAN<V extends NumberVector>
Using Grid for Accelerating Density-Based Clustering.
class  Leader<O>
class  NaiveMeanShiftClustering<V extends NumberVector>
Mean-shift based clustering algorithm.
class  SNNClustering<O>
Shared nearest neighbor clustering.
• ### Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.clustering.affinitypropagation

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.affinitypropagation that implement Algorithm
Modifier and Type Class and Description
class  AffinityPropagationClusteringAlgorithm<O>
Cluster analysis by affinity propagation.
• ### Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.clustering.biclustering

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.biclustering that implement Algorithm
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.
• ### Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation that implement Algorithm
Modifier and Type Class and Description
class  CASH<V extends NumberVector>
The CASH algorithm is a subspace clustering algorithm based on the Hough transform.
class  COPAC<V extends NumberVector>
COPAC is an algorithm to partition a database according to the correlation dimension of its objects and to then perform an arbitrary clustering algorithm over the partitions.
class  ERiC<V extends NumberVector>
Performs correlation clustering on the data partitioned according to local correlation dimensionality and builds a hierarchy of correlation clusters that allows multiple inheritance from the clustering result.
class  FourC<V extends NumberVector>
4C identifies local subgroups of data objects sharing a uniform correlation.
class  HiCO<V extends NumberVector>
Implementation of the HiCO algorithm, an algorithm for detecting hierarchies of correlation clusters.
class  LMCLUS
Linear manifold clustering in high dimensional spaces by stochastic search.
class  ORCLUS<V extends NumberVector>
ORCLUS: Arbitrarily ORiented projected CLUSter generation.
• ### Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.clustering.em

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.em that implement Algorithm
Modifier and Type Class and Description
class  EM<V extends NumberVector,M extends MeanModel>
Clustering by expectation maximization (EM-Algorithm), also known as Gaussian Mixture Modeling (GMM), with optional MAP regularization.
• ### Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan that implement Algorithm
Modifier and Type Class and Description
class  GeneralizedDBSCAN
Generalized DBSCAN, density-based clustering with noise.
class  LSDBC<O extends NumberVector>
Locally Scaled Density Based Clustering.
• ### Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan.parallel

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan.parallel that implement Algorithm
Modifier and Type Class and Description
class  ParallelGeneralizedDBSCAN
Parallel version of DBSCAN clustering.
• ### Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical

Subinterfaces of Algorithm in de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical
Modifier and Type Interface and Description
interface  HierarchicalClusteringAlgorithm
Interface for hierarchical clustering algorithms.
Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical that implement Algorithm
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 nearest-neighbor heuristic for acceleration.
class  CLINK<O>
class  HDBSCANLinearMemory<O>
Linear memory implementation of HDBSCAN clustering.
class  MiniMax<O>
class  MiniMaxAnderberg<O>
This is a modification of the classic MiniMax algorithm for hierarchical clustering using a nearest-neighbor heuristic for acceleration.
class  MiniMaxNNChain<O>
MiniMax hierarchical clustering using the NNchain algorithm.
class  NNChain<O>
NNchain clustering algorithm.
class  SLINK<O>
class  SLINKHDBSCANLinearMemory<O>
Linear memory implementation of HDBSCAN clustering based on SLINK.
• ### Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.birch

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.birch that implement Algorithm
Modifier and Type Class and Description
class  BIRCHLeafClustering
BIRCH-based clustering algorithm that simply treats the leafs of the CFTree as clusters.
• ### Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.extraction

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.extraction that implement Algorithm
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.
• ### Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans

Subinterfaces of Algorithm in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans
Modifier and Type Interface and Description
interface  KMeans<V extends NumberVector,M extends Model>
Some constants and options shared among kmeans family algorithms.
Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans that implement Algorithm
Modifier and Type Class and Description
class  AbstractKMeans<V extends NumberVector,M extends Model>
Abstract base class for k-means implementations.
class  BestOfMultipleKMeans<V extends NumberVector,M extends MeanModel>
Run K-Means 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 non-medoid.
class  KMeansAnnulus<V extends NumberVector>
Annulus k-means algorithm.
class  KMeansBisecting<V extends NumberVector,M extends MeanModel>
The bisecting k-means algorithm works by starting with an initial partitioning into two clusters, then repeated splitting of the largest cluster to get additional clusters.
class  KMeansCompare<V extends NumberVector>
Compare-Means: Accelerated k-means by exploiting the triangle inequality and pairwise distances of means to prune candidate means.
class  KMeansElkan<V extends NumberVector>
Elkan's fast k-means by exploiting the triangle inequality.
class  KMeansExponion<V extends NumberVector>
Newlings's exponion k-means algorithm, exploiting the triangle inequality.
class  KMeansHamerly<V extends NumberVector>
Hamerly's fast k-means by exploiting the triangle inequality.
class  KMeansLloyd<V extends NumberVector>
The standard k-means algorithm, using bulk iterations and commonly attributed to Lloyd and Forgy (independently).
class  KMeansMacQueen<V extends NumberVector>
The original k-means algorithm, using MacQueen style incremental updates; making this effectively an "online" (streaming) algorithm.
class  KMeansMinusMinus<V extends NumberVector>
k-means--: A Unified Approach to Clustering and Outlier Detection.
class  KMeansSimplifiedElkan<V extends NumberVector>
Simplified version of Elkan's k-means by exploiting the triangle inequality.
class  KMeansSort<V extends NumberVector>
Sort-Means: Accelerated k-means by exploiting the triangle inequality and pairwise distances of means to prune candidate means (with sorting).
class  KMediansLloyd<V extends NumberVector>
k-medians clustering algorithm, but using Lloyd-style bulk iterations instead of the more complicated approach suggested by Kaufman and Rousseeuw (see KMedoidsPAM instead).
class  KMedoidsFastPAM<V>
FastPAM: An improved version of PAM, that is usually O(k) times faster.
class  KMedoidsFastPAM1<V>
FastPAM1: A version of PAM that is O(k) times faster, i.e., now in O((n-k)²).
class  KMedoidsPAM<V>
The original Partitioning Around Medoids (PAM) algorithm or k-medoids 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 k-medoids clustering algorithm, implemented as EM-style bulk algorithm.
class  SingleAssignmentKMeans<V extends NumberVector>
Pseudo-k-Means variations, that assigns each object to the nearest center.
class  XMeans<V extends NumberVector,M extends MeanModel>
X-means: Extending K-means with Efficient Estimation on the Number of Clusters.
• ### Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.parallel

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.parallel that implement Algorithm
Modifier and Type Class and Description
class  ParallelLloydKMeans<V extends NumberVector>
Parallel implementation of k-Means clustering.
• ### Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.clustering.meta

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.meta that implement Algorithm
Modifier and Type Class and Description
class  ExternalClustering
Read an external clustering result from a file, such as produced by ClusteringVectorDumper.
• ### Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.clustering.onedimensional

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.onedimensional that implement Algorithm
Modifier and Type Class and Description
class  KNNKernelDensityMinimaClustering<V extends NumberVector>
Cluster one-dimensional data by splitting the data set on local minima after performing kernel density estimation.
• ### Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.clustering.optics

Subinterfaces of Algorithm in de.lmu.ifi.dbs.elki.algorithm.clustering.optics
Modifier and Type Interface and Description
interface  OPTICSTypeAlgorithm
Interface for OPTICS type algorithms, that can be analyzed by OPTICS Xi etc.
Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.optics that implement Algorithm
Modifier and Type Class and Description
class  AbstractOPTICS<O>
The OPTICS algorithm for density-based hierarchical clustering.
class  DeLiClu<V extends NumberVector>
DeliClu: Density-Based Hierarchical Clustering A hierarchical algorithm to find density-connected sets in a database, closely related to OPTICS but exploiting the structure of a R-tree for acceleration.
class  FastOPTICS<V extends NumberVector>
FastOPTICS algorithm (Fast approximation of OPTICS) Note that this is not FOPTICS as in "Fuzzy OPTICS"!
class  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 density-based hierarchical clustering.
class  OPTICSList<O>
The OPTICS algorithm for density-based hierarchical clustering.
class  OPTICSXi
Extract clusters from OPTICS Plots using the original Xi extraction.
• ### Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace

Subinterfaces of Algorithm in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace
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 post-processed for outlier detection.
Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace that implement Algorithm
Modifier and Type Class and Description
class  CLIQUE
Implementation of the CLIQUE algorithm, a grid-based algorithm to identify dense clusters in subspaces of maximum dimensionality.
class  DiSH<V extends NumberVector>
Algorithm for detecting subspace hierarchies.
class  DOC<V extends NumberVector>
DOC is a sampling based subspace clustering algorithm.
class  FastDOC<V extends NumberVector>
The heuristic variant of the DOC algorithm, FastDOC Reference: C.
class  HiSC<V extends NumberVector>
Implementation of the HiSC algorithm, an algorithm for detecting hierarchies of subspace clusters.
class  P3C<V extends NumberVector>
P3C: A Robust Projected Clustering Algorithm.
class  PreDeCon<V extends NumberVector>
PreDeCon computes clusters of subspace preference weighted connected points.
class  PROCLUS<V extends NumberVector>
The PROCLUS algorithm, an algorithm to find subspace clusters in high dimensional spaces.
class  SUBCLU<V extends NumberVector>
Implementation of the SUBCLU algorithm, an algorithm to detect arbitrarily shaped and positioned clusters in subspaces.
• ### Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.clustering.trivial

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.trivial that implement Algorithm
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 "all-in-one" clustering.
class  ByModelClustering
Pseudo clustering using annotated models.
class  TrivialAllInOne
Trivial pseudo-clustering that just considers all points to be one big cluster.
class  TrivialAllNoise
Trivial pseudo-clustering that just considers all points to be noise.
• ### Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain that implement Algorithm
Modifier and Type Class and Description
class  CenterOfMassMetaClustering<C extends Clustering<?>>
Center-of-mass meta clustering reduces uncertain objects to their center of mass, then runs a vector-oriented clustering algorithm on this data set.
class  CKMeans
Run k-means on the centers of each uncertain object.
class  FDBSCAN
FDBSCAN is an adaption of DBSCAN for fuzzy (uncertain) objects.
class  RepresentativeUncertainClustering
Representative clustering of uncertain data.
class  UKMeans
Uncertain K-Means clustering, using the average deviation from the center.
• ### Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.itemsetmining

Classes in de.lmu.ifi.dbs.elki.algorithm.itemsetmining that implement Algorithm
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 depth-first discovery algorithm for mining frequent itemsets.
class  FPGrowth
FP-Growth is an algorithm for mining the frequent itemsets by using a compressed representation of the database called FPGrowth.FPTree.
• ### Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.itemsetmining.associationrules

Classes in de.lmu.ifi.dbs.elki.algorithm.itemsetmining.associationrules that implement Algorithm
Modifier and Type Class and Description
class  AssociationRuleGeneration
Association rule generation from frequent itemsets This algorithm calls a specified frequent itemset algorithm and calculates all association rules, having a interest value between then the specified boundaries form the obtained frequent itemsets Reference: M.
• ### Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.outlier

Subinterfaces of Algorithm in de.lmu.ifi.dbs.elki.algorithm.outlier
Modifier and Type Interface and Description
interface  OutlierAlgorithm
Generic super interface for outlier detection algorithms.
Classes in de.lmu.ifi.dbs.elki.algorithm.outlier that implement Algorithm
Modifier and Type Class and Description
class  COP<V extends NumberVector>
Correlation outlier probability: Outlier Detection in Arbitrarily Oriented Subspaces Reference: Hans-Peter Kriegel, Peer Kröger, Erich Schubert, Arthur Zimek
Outlier Detection in Arbitrarily Oriented Subspaces
Proc.
class  DWOF<O>
Algorithm to compute dynamic-window outlier factors in a database based on a specified parameter k, which specifies the number of the neighbors to be considered during the calculation of the DWOF score.
class  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>
OPTICS-OF outlier detection algorithm, an algorithm to find Local Outliers in a database based on ideas from OPTICSTypeAlgorithm clustering.
class  SimpleCOP<V extends NumberVector>
Algorithm to compute local correlation outlier probability.
• ### Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.outlier.anglebased

Classes in de.lmu.ifi.dbs.elki.algorithm.outlier.anglebased that implement Algorithm
Modifier and Type Class and Description
class  ABOD<V extends NumberVector>
Angle-Based Outlier Detection / Angle-Based Outlier Factor.
class  FastABOD<V extends NumberVector>
Fast-ABOD (approximateABOF) version of Angle-Based Outlier Detection / Angle-Based Outlier Factor.
class  LBABOD<V extends NumberVector>
LB-ABOD (lower-bound) version of Angle-Based Outlier Detection / Angle-Based Outlier Factor.
• ### Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.outlier.clustering

Classes in de.lmu.ifi.dbs.elki.algorithm.outlier.clustering that implement Algorithm
Modifier and Type Class and Description
class  CBLOF<O extends NumberVector>
Cluster-based local outlier factor (CBLOF).
class  EMOutlier<V extends NumberVector>
Outlier detection algorithm using EM Clustering.
class  KMeansOutlierDetection<O extends NumberVector>
Outlier detection by using k-means clustering.
class  SilhouetteOutlierDetection<O>
Outlier detection by using the Silhouette Coefficients.
• ### Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.outlier.distance

Classes in de.lmu.ifi.dbs.elki.algorithm.outlier.distance that implement Algorithm
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>
kNN-based 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 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.
• ### Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.outlier.distance.parallel

Classes in de.lmu.ifi.dbs.elki.algorithm.outlier.distance.parallel that implement Algorithm
Modifier and Type Class and Description
class  ParallelKNNOutlier<O>
Parallel implementation of KNN Outlier detection.
class  ParallelKNNWeightOutlier<O>
Parallel implementation of KNN Weight Outlier detection.
• ### Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.outlier.intrinsic

Classes in de.lmu.ifi.dbs.elki.algorithm.outlier.intrinsic that implement Algorithm
Modifier and Type Class and Description
class  IDOS<O>
Intrinsic Dimensional Outlier Detection in High-Dimensional Data.
class  IntrinsicDimensionalityOutlier<O>
Use intrinsic dimensionality for outlier detection.
class  ISOS<O>
Intrinsic Stochastic Outlier Selection.
• ### Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.outlier.lof

Classes in de.lmu.ifi.dbs.elki.algorithm.outlier.lof that implement Algorithm
Modifier and Type Class and Description
class  ALOCI<O extends NumberVector>
Fast Outlier Detection Using the "approximate Local Correlation Integral".
class  COF<O>
Connectivity-based Outlier Factor (COF).
class  FlexibleLOF<O>
Flexible variant of the "Local Outlier Factor" algorithm.
class  INFLO<O>
Influence Outliers using Symmetric Relationship (INFLO) using two-way search, is an outlier detection method based on LOF; but also using the reverse kNN.
class  KDEOS<O>
Generalized Outlier Detection with Flexible Kernel Density Estimates.
class  LDF<O extends NumberVector>
Outlier Detection with Kernel Density Functions.
class  LDOF<O>
Computes the LDOF (Local Distance-Based Outlier Factor) for all objects of a Database.
class  LOCI<O>
Fast Outlier Detection Using the "Local Correlation Integral".
class  LOF<O>
Algorithm to compute density-based local outlier factors in a database based on a specified parameter -lof.k.
class  LoOP<O>
LoOP: Local Outlier Probabilities Distance/density based algorithm similar to LOF to detect outliers, but with statistical methods to achieve better result stability.
class  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.
• ### Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.outlier.lof.parallel

Classes in de.lmu.ifi.dbs.elki.algorithm.outlier.lof.parallel that implement Algorithm
Modifier and Type Class and Description
class  ParallelLOF<O>
Parallel implementation of Local Outlier Factor using processors.
class  ParallelSimplifiedLOF<O>
Parallel implementation of Simplified-LOF Outlier detection using processors.
• ### Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.outlier.meta

Classes in de.lmu.ifi.dbs.elki.algorithm.outlier.meta that implement Algorithm
Modifier and Type Class and Description
class  ExternalDoubleOutlierScore
class  FeatureBagging
A simple ensemble method called "Feature bagging" for outlier detection.
class  HiCS<V extends NumberVector>
Algorithm to compute High Contrast Subspaces for Density-Based Outlier Ranking.
class  RescaleMetaOutlierAlgorithm
Scale another outlier score using the given scaling function.
class  SimpleOutlierEnsemble
Simple outlier ensemble method.
Fields in de.lmu.ifi.dbs.elki.algorithm.outlier.meta declared as Algorithm
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.
Constructors in de.lmu.ifi.dbs.elki.algorithm.outlier.meta with parameters of type Algorithm
Constructor and Description
RescaleMetaOutlierAlgorithm(Algorithm algorithm, ScalingFunction scaling)
Constructor.
• ### Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.outlier.spatial

Classes in de.lmu.ifi.dbs.elki.algorithm.outlier.spatial that implement Algorithm
Modifier and Type Class and Description
class  AbstractDistanceBasedSpatialOutlier<N,O>
Abstract base class for distance-based spatial outlier detection methods.
class  AbstractNeighborhoodOutlier<O>
Abstract base class for spatial outlier detection methods using a spatial neighborhood.
class  CTLuGLSBackwardSearchAlgorithm<V extends NumberVector>
GLS-Backward Search is a statistical approach to detecting spatial outliers.
class  CTLuMeanMultipleAttributes<N,O extends NumberVector>
Mean Approach is used to discover spatial outliers with multiple attributes.
class  CTLuMedianAlgorithm<N>
Median Algorithm of C.
class  CTLuMedianMultipleAttributes<N,O extends NumberVector>
Median Approach is used to discover spatial outliers with multiple attributes.
class  CTLuMoranScatterplotOutlier<N>
Moran scatterplot outliers, based on the standardized deviation from the local and global means.
class  CTLuRandomWalkEC<P>
Spatial outlier detection based on random walks.
class  CTLuScatterplotOutlier<N>
Scatterplot-outlier is a spatial outlier detection method that performs a linear regression of object attributes and their neighbors average value.
class  CTLuZTestOutlier<N>
Detect outliers by comparing their attribute value to the mean and standard deviation of their neighborhood.
class  SLOM<N,O>
SLOM: a new measure for local spatial outliers Reference: S.
class  SOF<N,O>
The Spatial Outlier Factor (SOF) is a spatial LOF variation.
class  TrimmedMeanApproach<N>
A Trimmed Mean Approach to Finding Spatial Outliers.
• ### Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.outlier.subspace

Classes in de.lmu.ifi.dbs.elki.algorithm.outlier.subspace that implement Algorithm
Modifier and Type Class and Description
class  AbstractAggarwalYuOutlier<V extends NumberVector>
Abstract base class for the sparse-grid-cell based outlier detection of Aggarwal and Yu.
class  AggarwalYuEvolutionary<V extends NumberVector>
Evolutionary variant (EAFOD) of the high-dimensional outlier detection algorithm by Aggarwal and Yu.
class  AggarwalYuNaive<V extends NumberVector>
BruteForce variant of the high-dimensional outlier detection algorithm by Aggarwal and Yu.
class  OutRankS1
OutRank: ranking outliers in high dimensional data.
class  OUTRES
Adaptive outlierness for subspace outlier ranking (OUTRES).
class  SOD<V extends NumberVector>
Subspace Outlier Degree.
• ### Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.outlier.svm

Classes in de.lmu.ifi.dbs.elki.algorithm.outlier.svm that implement Algorithm
Modifier and Type Class and Description
class  LibSVMOneClassOutlierDetection<V extends NumberVector>
Outlier-detection using one-class support vector machines.
• ### Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.outlier.trivial

Classes in de.lmu.ifi.dbs.elki.algorithm.outlier.trivial that implement Algorithm
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 one-dimensional 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.
• ### Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.projection

Classes in de.lmu.ifi.dbs.elki.algorithm.projection that implement Algorithm
Modifier and Type Class and Description
class  AbstractProjectionAlgorithm<R extends Result>
Abstract base class for projection algorithms.
class  BarnesHutTSNE<O>
tSNE using Barnes-Hut-Approximation.
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 Algorithm in de.lmu.ifi.dbs.elki.algorithm.statistics

Classes in de.lmu.ifi.dbs.elki.algorithm.statistics that implement Algorithm
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.
• ### Uses of Algorithm in de.lmu.ifi.dbs.elki.algorithm.timeseries

Classes in de.lmu.ifi.dbs.elki.algorithm.timeseries that implement Algorithm
Modifier and Type Class and Description
class  OfflineChangePointDetectionAlgorithm
Off-line change point detection algorithm detecting a change in mean, based on the cumulative sum (CUSUM), same-variance assumption, and using bootstrap sampling for significance estimation.
class  SigniTrendChangeDetection
Signi-Trend detection algorithm applies to a single time-series.
• ### Uses of Algorithm in de.lmu.ifi.dbs.elki.evaluation.clustering.extractor

Classes in de.lmu.ifi.dbs.elki.evaluation.clustering.extractor that implement Algorithm
Modifier and Type Class and Description
protected static class  CutDendrogramByHeightExtractor.DummyHierarchicalClusteringAlgorithm
Dummy instance.
• ### Uses of Algorithm in de.lmu.ifi.dbs.elki.workflow

Fields in de.lmu.ifi.dbs.elki.workflow with type parameters of type Algorithm
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 parameters in de.lmu.ifi.dbs.elki.workflow with type arguments of type Algorithm
Constructor and Description
AlgorithmStep(java.util.List<? extends Algorithm> algorithms)
Constructor.
• ### Uses of Algorithm in tutorial.clustering

Classes in tutorial.clustering that implement Algorithm
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>
K-means variation that produces equally sized clusters.
• ### Uses of Algorithm in tutorial.outlier

Classes in tutorial.outlier that implement Algorithm
Modifier and Type Class and Description
class  DistanceStddevOutlier<O>
A simple outlier detection algorithm that computes the standard deviation of the kNN distances.