Uses of Interface
de.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	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 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> Leader clustering algorithm.
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> 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 nearest-neighbor 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 Single-Link Algorithm SLINK of R.
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 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 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.