Uses of Interface
elki.Algorithm

Packages that use Algorithm

Package	Description
elki	ELKI framework "Environment for Developing KDD-Applications Supported by Index-Structures".
elki.algorithm	Miscellaneous algorithms.
elki.algorithm.statistics	Statistical analysis algorithms.
elki.classification	Classification algorithms.
elki.clustering	Clustering algorithms.
elki.clustering.affinitypropagation	Affinity Propagation (AP) clustering.
elki.clustering.biclustering	Biclustering algorithms.
elki.clustering.correlation	Correlation clustering algorithms.
elki.clustering.dbscan	DBSCAN and its generalizations.
elki.clustering.dbscan.parallel	Parallel versions of Generalized DBSCAN.
elki.clustering.em	Expectation-Maximization clustering algorithm for Gaussian Mixture Modeling (GMM).
elki.clustering.hierarchical	Hierarchical agglomerative clustering (HAC).
elki.clustering.hierarchical.birch	BIRCH clustering.
elki.clustering.hierarchical.extraction	Extraction of partitional clusterings from hierarchical results.
elki.clustering.kcenter	K-center clustering.
elki.clustering.kmeans	K-means clustering and variations.
elki.clustering.kmeans.parallel	Parallelized implementations of k-means.
elki.clustering.kmeans.spherical	Spherical k-means clustering and variations.
elki.clustering.kmedoids	K-medoids clustering (PAM).
elki.clustering.meta	Meta clustering algorithms, that get their result from other clusterings or external sources.
elki.clustering.onedimensional	Clustering algorithms for one-dimensional data.
elki.clustering.optics	OPTICS family of clustering algorithms.
elki.clustering.silhouette	Silhouette clustering algorithms.
elki.clustering.subspace	Axis-parallel subspace clustering algorithms.
elki.clustering.svm
elki.clustering.trivial	Trivial clustering algorithms: all in one, no clusters, label clusterings.
elki.clustering.uncertain	Clustering algorithms for uncertain data.
elki.itemsetmining	Algorithms for frequent itemset mining such as APRIORI.
elki.itemsetmining.associationrules	Association rule mining.
elki.outlier	Outlier detection algorithms.
elki.outlier.anglebased	Angle-based outlier detection algorithms.
elki.outlier.clustering	Clustering based outlier detection.
elki.outlier.density	Density-based outlier detection algorithms.
elki.outlier.distance	Distance-based outlier detection algorithms, such as DBOutlier and kNN.
elki.outlier.distance.parallel	Parallel implementations of distance-based outlier detectors.
elki.outlier.intrinsic	Outlier detection algorithms based on intrinsic dimensionality.
elki.outlier.lof	LOF family of outlier detection algorithms.
elki.outlier.lof.parallel	Parallelized variants of LOF.
elki.outlier.meta	Meta outlier detection algorithms: external scores, score rescaling.
elki.outlier.spatial	Spatial outlier detection algorithms.
elki.outlier.subspace	Subspace outlier detection methods.
elki.outlier.svm	Support-Vector-Machines for outlier detection.
elki.outlier.trivial	Trivial outlier detection algorithms: no outliers, all outliers, label outliers.
elki.projection	Data projections (see also preprocessing filters for basic projections).
elki.timeseries	Algorithms for change point detection in time series.
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 elki

Methods in elki with parameters of type Algorithm

Modifier and Type	Method	Description
static java.lang.Object	Algorithm.Utils.autorun(Algorithm a, Database database)	Try to auto-run the algorithm on a database by calling a method called run, with an optional Database first, and with data relations as specified by getInputTypeRestriction().

Uses of Algorithm in elki.algorithm

Classes in elki.algorithm that implement Algorithm

Modifier and Type	Class	Description
class	DependencyDerivator<V extends NumberVector>	Dependency derivator computes quantitatively linear dependencies among attributes of a given dataset based on a linear correlation PCA.
class	KNNDistancesSampler<O>	Provides an order of the kNN-distances for all objects within the database.
class	KNNJoin	Joins in a given spatial database to each object its k-nearest neighbors.
class	NullAlgorithm	Null algorithm, which does nothing.

Uses of Algorithm in elki.algorithm.statistics

Classes in elki.algorithm.statistics that implement Algorithm

Modifier and Type	Class	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	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	RankingQualityHistogram<O>	Evaluate a distance function with respect to kNN queries.

Uses of Algorithm in elki.classification

Subinterfaces of Algorithm in elki.classification

Modifier and Type	Interface	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 elki.classification that implement Algorithm

Modifier and Type	Class	Description
class	AbstractClassifier<O,R>	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 elki.clustering

Subinterfaces of Algorithm in elki.clustering

Modifier and Type	Interface	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 elki.clustering that implement Algorithm

Modifier and Type	Class	Description
class	AbstractProjectedClustering<R extends Clustering<?>>	Abstract superclass for projected clustering algorithms, like PROCLUS and ORCLUS.
class	BetulaLeafPreClustering	BETULA-based clustering algorithm that simply treats the leafs of the CFTree as clusters.
class	CanopyPreClustering<O>	Canopy pre-clustering is a simple preprocessing step for clustering.
class	CFSFDP<O>	Clustering by fast search and find of density peaks (CFSFDP) is a density-based clustering method similar to mean-shift clustering.
class	Leader<O>	Leader clustering algorithm.
class	NaiveMeanShiftClustering<V extends NumberVector>	Mean-shift based clustering algorithm.
class	SNNClustering<O>	Shared nearest neighbor clustering.

Uses of Algorithm in elki.clustering.affinitypropagation

Classes in elki.clustering.affinitypropagation that implement Algorithm

Modifier and Type	Class	Description
class	AffinityPropagation<O>	Cluster analysis by affinity propagation.

Uses of Algorithm in elki.clustering.biclustering

Classes in elki.clustering.biclustering that implement Algorithm

Modifier and Type	Class	Description
class	AbstractBiclustering<M extends BiclusterModel>	Abstract class as a convenience for different biclustering approaches.
class	ChengAndChurch	Cheng and Church biclustering.

Uses of Algorithm in elki.clustering.correlation

Classes in elki.clustering.correlation that implement Algorithm

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

Uses of Algorithm in elki.clustering.dbscan

Classes in elki.clustering.dbscan that implement Algorithm

Modifier and Type	Class	Description
class	DBSCAN<O>	Density-Based Clustering of Applications with Noise (DBSCAN), an algorithm to find density-connected sets in a database.
class	GeneralizedDBSCAN	Generalized DBSCAN, density-based clustering with noise.
class	GriDBSCAN<V extends NumberVector>	Using Grid for Accelerating Density-Based Clustering.
class	LSDBC<O extends NumberVector>	Locally Scaled Density Based Clustering.

Uses of Algorithm in elki.clustering.dbscan.parallel

Classes in elki.clustering.dbscan.parallel that implement Algorithm

Modifier and Type	Class	Description
class	ParallelGeneralizedDBSCAN	Parallel version of DBSCAN clustering.

Uses of Algorithm in elki.clustering.em

Classes in elki.clustering.em that implement Algorithm

Modifier and Type	Class	Description
class	BetulaGMM	Clustering by expectation maximization (EM-Algorithm), also known as Gaussian Mixture Modeling (GMM), with optional MAP regularization.
class	BetulaGMMWeighted	Clustering by expectation maximization (EM-Algorithm), also known as Gaussian Mixture Modeling (GMM), with optional MAP regularization.
class	EM<O,M extends MeanModel>	Clustering by expectation maximization (EM-Algorithm), also known as Gaussian Mixture Modeling (GMM), with optional MAP regularization.
class	KDTreeEM	Clustering by expectation maximization (EM-Algorithm), also known as Gaussian Mixture Modeling (GMM), calculated on a kd-tree.

Uses of Algorithm in elki.clustering.hierarchical

Subinterfaces of Algorithm in elki.clustering.hierarchical

Modifier and Type	Interface	Description
interface	HierarchicalClusteringAlgorithm	Interface for hierarchical clustering algorithms.

Classes in elki.clustering.hierarchical that implement Algorithm

Modifier and Type	Class	Description
class	AbstractHDBSCAN<O>	Abstract base class for HDBSCAN variations.
class	AGNES<O>	Hierarchical Agglomerative Clustering (HAC) or Agglomerative Nesting (AGNES) is a classic hierarchical clustering algorithm.
class	Anderberg<O>	This is a modification of the classic AGNES algorithm for hierarchical clustering using a nearest-neighbor heuristic for acceleration.
class	CLINK<O>	CLINK algorithm for complete linkage.
class	HACAM<O>	Hierarchical Agglomerative Clustering Around Medoids (HACAM) is a hierarchical clustering method that merges the clusters with the smallest distance to the medoid of the union.
class	HDBSCANLinearMemory<O>	Linear memory implementation of HDBSCAN clustering.
class	LinearMemoryNNChain<O extends NumberVector>	NNchain clustering algorithm with linear memory, for particular linkages (that can be aggregated) and numerical vector data only.
class	MedoidLinkage<O>	Medoid linkage uses the distance of medoids as criterion.
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	OPTICSToHierarchical	Convert a OPTICS ClusterOrder to a hierarchical clustering.
class	SLINK<O>	Implementation of the efficient Single-Link Algorithm SLINK of R.
class	SLINKHDBSCANLinearMemory<O>	Linear memory implementation of HDBSCAN clustering based on SLINK.

Uses of Algorithm in elki.clustering.hierarchical.birch

Classes in elki.clustering.hierarchical.birch that implement Algorithm

Modifier and Type	Class	Description
class	BIRCHLeafClustering	BIRCH-based clustering algorithm that simply treats the leafs of the CFTree as clusters.
class	BIRCHLloydKMeans	BIRCH-based clustering algorithm that simply treats the leafs of the CFTree as clusters.

Uses of Algorithm in elki.clustering.hierarchical.extraction

Classes in elki.clustering.hierarchical.extraction that implement Algorithm

Modifier and Type	Class	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, and additionally also compute the GLOSH outlier scores.
class	SimplifiedHierarchyExtraction	Extraction of simplified cluster hierarchies, as proposed in HDBSCAN.

Uses of Algorithm in elki.clustering.kcenter

Classes in elki.clustering.kcenter that implement Algorithm

Modifier and Type	Class	Description
class	GreedyKCenter<O>	Greedy algorithm for k-center algorithm also known as Gonzalez clustering, or farthest-first traversal.

Uses of Algorithm in elki.clustering.kmeans

Subinterfaces of Algorithm in elki.clustering.kmeans

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

Classes in elki.clustering.kmeans that implement Algorithm

Modifier and Type	Class	Description
class	AbstractKMeans<V extends NumberVector,M extends Model>	Abstract base class for k-means implementations.
class	AnnulusKMeans<V extends NumberVector>	Annulus k-means algorithm.
class	BestOfMultipleKMeans<V extends NumberVector,M extends MeanModel>	Run K-Means multiple times, and keep the best run.
class	BetulaLloydKMeans	BIRCH/BETULA-based clustering algorithm that simply treats the leafs of the CFTree as clusters.
class	BisectingKMeans<V extends NumberVector,M extends MeanModel>	The bisecting k-means algorithm works by starting with an initial partitioning into two clusters, then repeated splitting of the largest cluster to get additional clusters.
class	CompareMeans<V extends NumberVector>	Compare-Means: Accelerated k-means by exploiting the triangle inequality and pairwise distances of means to prune candidate means.
class	ElkanKMeans<V extends NumberVector>	Elkan's fast k-means by exploiting the triangle inequality.
class	ExponionKMeans<V extends NumberVector>	Newlings's Exponion k-means algorithm, exploiting the triangle inequality.
class	FuzzyCMeans<V extends NumberVector>	Fuzzy Clustering developed by Dunn and revisited by Bezdek
class	GMeans<V extends NumberVector,M extends MeanModel>	G-Means extends K-Means and estimates the number of centers with Anderson Darling Test. Implemented as specialization of XMeans.
class	HamerlyKMeans<V extends NumberVector>	Hamerly's fast k-means by exploiting the triangle inequality.
class	HartiganWongKMeans<V extends NumberVector>	Hartigan and Wong k-means clustering.
class	KDTreeFilteringKMeans<V extends NumberVector>	Filtering or "blacklisting" K-means with k-d-tree acceleration.
class	KDTreePruningKMeans<V extends NumberVector>	Pruning K-means with k-d-tree acceleration.
class	KMeansMinusMinus<V extends NumberVector>	k-means--: A Unified Approach to Clustering and Outlier Detection.
class	KMediansLloyd<V extends NumberVector>	k-medians clustering algorithm, but using Lloyd-style bulk iterations instead of the more complicated approach suggested by Kaufman and Rousseeuw (see PAM instead).
class	LloydKMeans<V extends NumberVector>	The standard k-means algorithm, using bulk iterations and commonly attributed to Lloyd and Forgy (independently).
class	MacQueenKMeans<V extends NumberVector>	The original k-means algorithm, using MacQueen style incremental updates; making this effectively an "online" (streaming) algorithm.
class	ShallotKMeans<V extends NumberVector>	Borgelt's Shallot k-means algorithm, exploiting the triangle inequality.
class	SimplifiedElkanKMeans<V extends NumberVector>	Simplified version of Elkan's k-means by exploiting the triangle inequality.
class	SingleAssignmentKMeans<V extends NumberVector>	Pseudo-k-means variations, that assigns each object to the nearest center.
class	SortMeans<V extends NumberVector>	Sort-Means: Accelerated k-means by exploiting the triangle inequality and pairwise distances of means to prune candidate means (with sorting).
class	XMeans<V extends NumberVector,M extends MeanModel>	X-means: Extending K-means with Efficient Estimation on the Number of Clusters.
class	YinYangKMeans<V extends NumberVector>	Yin-Yang k-Means Clustering.

Uses of Algorithm in elki.clustering.kmeans.parallel

Classes in elki.clustering.kmeans.parallel that implement Algorithm

Modifier and Type	Class	Description
class	ParallelLloydKMeans<V extends NumberVector>	Parallel implementation of k-Means clustering.

Uses of Algorithm in elki.clustering.kmeans.spherical

Classes in elki.clustering.kmeans.spherical that implement Algorithm

Modifier and Type	Class	Description
class	EuclideanSphericalElkanKMeans<V extends NumberVector>	Elkan's fast k-means by exploiting the triangle inequality in the corresponding Euclidean space.
class	EuclideanSphericalHamerlyKMeans<V extends NumberVector>	A spherical k-Means algorithm based on Hamerly's fast k-means by exploiting the triangle inequality in the corresponding Euclidean space.
class	EuclideanSphericalSimplifiedElkanKMeans<V extends NumberVector>	A spherical k-Means algorithm based on Hamerly's fast k-means by exploiting the triangle inequality in the corresponding Euclidean space.
class	SphericalElkanKMeans<V extends NumberVector>	Elkan's fast k-means by exploiting the triangle inequality.
class	SphericalHamerlyKMeans<V extends NumberVector>	A spherical k-Means algorithm based on Hamerly's fast k-means by exploiting the triangle inequality.
class	SphericalKMeans<V extends NumberVector>	The standard spherical k-means algorithm.
class	SphericalSimplifiedElkanKMeans<V extends NumberVector>	A spherical k-Means algorithm based on Hamerly's fast k-means by exploiting the triangle inequality.
class	SphericalSimplifiedHamerlyKMeans<V extends NumberVector>	A spherical k-Means algorithm based on Hamerly's fast k-means by exploiting the triangle inequality.
class	SphericalSingleAssignmentKMeans<V extends NumberVector>	Pseudo-k-Means variations, that assigns each object to the nearest center.

Uses of Algorithm in elki.clustering.kmedoids

Subinterfaces of Algorithm in elki.clustering.kmedoids

Modifier and Type	Interface	Description
interface	KMedoidsClustering<O>	Interface for clustering algorithms that produce medoids.

Classes in elki.clustering.kmedoids that implement Algorithm

Modifier and Type	Class	Description
class	AlternatingKMedoids<O>	A k-medoids clustering algorithm, implemented as EM-style batch algorithm; known in literature as the "alternate" method.
class	CLARA<V>	Clustering Large Applications (CLARA) is a clustering method for large data sets based on PAM, partitioning around medoids (PAM) based on sampling.
class	CLARANS<O>	CLARANS: a method for clustering objects for spatial data mining is inspired by PAM (partitioning around medoids, PAM) and CLARA and also based on sampling.
class	EagerPAM<O>	Variation of PAM that eagerly performs all swaps that yield an improvement during an iteration.
class	FastCLARA<V>	Clustering Large Applications (CLARA) with the FastPAM improvements, to increase scalability in the number of clusters.
class	FastCLARANS<V>	A faster variation of CLARANS, that can explore O(k) as many swaps at a similar cost by considering all medoids for each candidate non-medoid.
class	FasterCLARA<O>	Clustering Large Applications (CLARA) with the FastPAM improvements, to increase scalability in the number of clusters.
class	FasterPAM<O>	Variation of FastPAM that eagerly performs any swap that yields an improvement during an iteration.
class	FastPAM<O>	FastPAM: An improved version of PAM, that is usually O(k) times faster.
class	FastPAM1<O>	FastPAM1: A version of PAM that is O(k) times faster, i.e., now in O((n-k)²).
class	PAM<O>	The original Partitioning Around Medoids (PAM) algorithm or k-medoids clustering, as proposed by Kaufman and Rousseeuw; a largely equivalent method was also proposed by Whitaker in the operations research domain, and is well known by the name "fast interchange" there.
class	ReynoldsPAM<O>	The Partitioning Around Medoids (PAM) algorithm with some additional optimizations proposed by Reynolds et al.
class	SingleAssignmentKMedoids<O>	K-medoids clustering by using the initialization only, then assigning each object to the nearest neighbor.

Uses of Algorithm in elki.clustering.meta

Classes in elki.clustering.meta that implement Algorithm

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

Uses of Algorithm in elki.clustering.onedimensional

Classes in elki.clustering.onedimensional that implement Algorithm

Modifier and Type	Class	Description
class	KNNKernelDensityMinimaClustering	Cluster one-dimensional data by splitting the data set on local minima after performing kernel density estimation.

Uses of Algorithm in elki.clustering.optics

Subinterfaces of Algorithm in elki.clustering.optics

Modifier and Type	Interface	Description
interface	GeneralizedOPTICS	A trivial generalization of OPTICS that is not restricted to numerical distances, and serves as a base for several other algorithms (HiCO, HiSC).
interface	OPTICSTypeAlgorithm	Interface for OPTICS type algorithms, that can be analyzed by OPTICS Xi etc.

Classes in elki.clustering.optics that implement Algorithm

Modifier and Type	Class	Description
class	AbstractOPTICS<O>	The OPTICS algorithm for density-based hierarchical clustering.
class	DeLiClu<V extends NumberVector>	DeliClu: Density-Based Hierarchical Clustering
class	FastOPTICS<V extends NumberVector>	FastOPTICS algorithm (Fast approximation of OPTICS)
class	OPTICSHeap<O>	The OPTICS algorithm for density-based hierarchical clustering.
class	OPTICSList<O>	The OPTICS algorithm for density-based hierarchical clustering.
class	OPTICSXi	Extract clusters from OPTICS plots using the original ξ (Xi) extraction, which defines steep areas if the reachability drops below 1-ξ, respectively increases to 1+ξ, of the current value, then constructs valleys that begin with a steep down, and end with a matching steep up area.

Uses of Algorithm in elki.clustering.silhouette

Classes in elki.clustering.silhouette that implement Algorithm

Modifier and Type	Class	Description
class	FasterMSC<O>	Fast and Eager Medoid Silhouette Clustering.
class	FastMSC<O>	Fast Medoid Silhouette Clustering.
class	PAMMEDSIL<O>	Clustering to optimize the Medoid Silhouette coefficient with a PAM-based swap heuristic.
class	PAMSIL<O>	Clustering to optimize the Silhouette coefficient with a PAM-based swap heuristic.

Uses of Algorithm in elki.clustering.subspace

Subinterfaces of Algorithm in elki.clustering.subspace

Modifier and Type	Interface	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 elki.clustering.subspace that implement Algorithm

Modifier and Type	Class	Description
class	CLIQUE	Implementation of the CLIQUE algorithm, a grid-based algorithm to identify dense clusters in subspaces of maximum dimensionality.
class	DiSH	Algorithm for detecting subspace hierarchies.
class	DOC	DOC is a sampling based subspace clustering algorithm.
class	FastDOC	The heuristic variant of the DOC algorithm, FastDOC
class	HiSC	Implementation of the HiSC algorithm, an algorithm for detecting hierarchies of subspace clusters.
class	P3C	P3C: A Robust Projected Clustering Algorithm.
class	PreDeCon	PreDeCon computes clusters of subspace preference weighted connected points.
class	PROCLUS	The PROCLUS algorithm, an algorithm to find subspace clusters in high dimensional spaces.
class	SUBCLU<V extends NumberVector>	Implementation of the SUBCLU algorithm, an algorithm to detect arbitrarily shaped and positioned clusters in subspaces.

Uses of Algorithm in elki.clustering.svm

Classes in elki.clustering.svm that implement Algorithm

Modifier and Type	Class	Description
class	SupportVectorClustering	Support Vector Clustering works on SVDD, which tries to find the smallest sphere enclosing all objects in kernel space.

Uses of Algorithm in elki.clustering.trivial

Classes in elki.clustering.trivial that implement Algorithm

Modifier and Type	Class	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 elki.clustering.uncertain

Classes in elki.clustering.uncertain that implement Algorithm

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

Uses of Algorithm in elki.itemsetmining

Classes in elki.itemsetmining that implement Algorithm

Modifier and Type	Class	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 elki.itemsetmining.associationrules

Classes in elki.itemsetmining.associationrules that implement Algorithm

Modifier and Type	Class	Description
class	AssociationRuleGeneration	Association rule generation from frequent itemsets

Uses of Algorithm in elki.outlier

Subinterfaces of Algorithm in elki.outlier

Modifier and Type	Interface	Description
interface	OutlierAlgorithm	Generic super interface for outlier detection algorithms.

Classes in elki.outlier that implement Algorithm

Modifier and Type	Class	Description
class	COP<V extends NumberVector>	Correlation outlier probability: Outlier Detection in Arbitrarily Oriented Subspaces
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	Outlier detection based on the probability density of the single normal distribution.
class	GaussianUniformMixture	Outlier detection algorithm using a mixture model approach.
class	OPTICSOF<O>	OPTICS-OF outlier detection algorithm, an algorithm to find Local Outliers in a database based on ideas from OPTICSTypeAlgorithm clustering.
class	SimpleCOP<V extends NumberVector>	Algorithm to compute local correlation outlier probability.

Uses of Algorithm in elki.outlier.anglebased

Classes in elki.outlier.anglebased that implement Algorithm

Modifier and Type	Class	Description
class	ABOD<V extends NumberVector>	Angle-Based Outlier Detection / Angle-Based Outlier Factor.
class	FastABOD<V extends NumberVector>	Fast-ABOD (approximateABOF) version of Angle-Based Outlier Detection / Angle-Based Outlier Factor.
class	LBABOD<V extends NumberVector>	LB-ABOD (lower-bound) version of Angle-Based Outlier Detection / Angle-Based Outlier Factor.

Uses of Algorithm in elki.outlier.clustering

Classes in elki.outlier.clustering that implement Algorithm

Modifier and Type	Class	Description
class	CBLOF<O extends NumberVector>	Cluster-based local outlier factor (CBLOF).
class	DBSCANOutlierDetection	Outlier detection algorithm using DBSCAN Clustering.
class	EMOutlier<V extends NumberVector>	Outlier detection algorithm using EM Clustering.
class	GLOSH	Global-Local Outlier Scores from Hierarchies.
class	KMeansMinusMinusOutlierDetection	k-means--: A Unified Approach to Clustering and Outlier Detection.
class	KMeansOutlierDetection<O extends NumberVector>	Outlier detection by using k-means clustering.
class	NoiseAsOutliers	Noise as outliers, from a clustering algorithm.
class	SilhouetteOutlierDetection<O>	Outlier detection by using the Silhouette Coefficients.

Uses of Algorithm in elki.outlier.density

Classes in elki.outlier.density that implement Algorithm

Modifier and Type	Class	Description
class	HySortOD	Hypercube-Based Outlier Detection.
class	IsolationForest	Isolation-Based Anomaly Detection.

Uses of Algorithm in elki.outlier.distance

Classes in elki.outlier.distance that implement Algorithm

Modifier and Type	Class	Description
class	AbstractDBOutlier<O>	Simple distance based outlier detection algorithms.
class	DBOutlierDetection<O>	Simple distanced based outlier detection algorithm.
class	DBOutlierScore<O>	Compute percentage of neighbors in the given neighborhood with size d.
class	HilOut<O extends NumberVector>	Fast Outlier Detection in High Dimensional Spaces
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 elki.outlier.distance.parallel

Classes in elki.outlier.distance.parallel that implement Algorithm

Modifier and Type	Class	Description
class	ParallelKNNOutlier<O>	Parallel implementation of KNN Outlier detection.
class	ParallelKNNWeightOutlier<O>	Parallel implementation of KNN Weight Outlier detection.

Uses of Algorithm in elki.outlier.intrinsic

Classes in elki.outlier.intrinsic that implement Algorithm

Modifier and Type	Class	Description
class	IDOS<O>	Intrinsic Dimensional Outlier Detection in High-Dimensional Data.
class	ISOS<O>	Intrinsic Stochastic Outlier Selection.
class	LID<O>	Use intrinsic dimensionality for outlier detection.

Uses of Algorithm in elki.outlier.lof

Classes in elki.outlier.lof that implement Algorithm

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

Classes in elki.outlier.lof.parallel that implement Algorithm

Modifier and Type	Class	Description
class	ParallelLOF<O>	Parallel implementation of Local Outlier Factor using processors.
class	ParallelSimplifiedLOF<O>	Parallel implementation of Simplified-LOF Outlier detection using processors.

Uses of Algorithm in elki.outlier.meta

Classes in elki.outlier.meta that implement Algorithm

Modifier and Type	Class	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	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 elki.outlier.meta declared as Algorithm

Modifier and Type	Field	Description
private Algorithm	RescaleMetaOutlierAlgorithm.algorithm	Holds the algorithm to run.
private Algorithm	RescaleMetaOutlierAlgorithm.Par.algorithm	Holds the algorithm to run.

Constructors in elki.outlier.meta with parameters of type Algorithm

Constructor	Description
RescaleMetaOutlierAlgorithm(Algorithm algorithm, ScalingFunction scaling)	Constructor.

Uses of Algorithm in elki.outlier.spatial

Classes in elki.outlier.spatial that implement Algorithm

Modifier and Type	Class	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<O>	Spatial outlier detection based on random walks.
class	CTLuScatterplotOutlier<N>	Scatterplot-outlier is a spatial outlier detection method that performs a linear regression of object attributes and their neighbors average value.
class	CTLuZTestOutlier<N>	Detect outliers by comparing their attribute value to the mean and standard deviation of their neighborhood.
class	SLOM<N,O>	SLOM: a new measure for local spatial outliers
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 elki.outlier.subspace

Classes in elki.outlier.subspace that implement Algorithm

Modifier and Type	Class	Description
class	AbstractAggarwalYuOutlier	Abstract base class for the sparse-grid-cell based outlier detection of Aggarwal and Yu.
class	AggarwalYuEvolutionary	Evolutionary variant (EAFOD) of the high-dimensional outlier detection algorithm by Aggarwal and Yu.
class	AggarwalYuNaive	BruteForce variant of the high-dimensional outlier detection algorithm by Aggarwal and Yu.
class	OutRankS1	OutRank: ranking outliers in high dimensional data.
class	OUTRES	Adaptive outlierness for subspace outlier ranking (OUTRES).
class	SOD<V extends NumberVector>	Subspace Outlier Degree: Outlier Detection in Axis-Parallel Subspaces of High Dimensional Data.

Uses of Algorithm in elki.outlier.svm

Classes in elki.outlier.svm that implement Algorithm

Modifier and Type	Class	Description
class	LibSVMOneClassOutlierDetection<V extends NumberVector>	Outlier-detection using one-class support vector machines.
class	OCSVM<V>	Outlier-detection using one-class support vector machines.
class	SVDD<V>	Support Vector Data Description for outlier detection.

Uses of Algorithm in elki.outlier.trivial

Classes in elki.outlier.trivial that implement Algorithm

Modifier and Type	Class	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 elki.projection

Classes in elki.projection that implement Algorithm

Modifier and Type	Class	Description
class	AbstractProjectionAlgorithm<R>	Abstract base class for projection algorithms.
class	BarnesHutTSNE<O>	t-SNE 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 elki.timeseries

Classes in elki.timeseries that implement Algorithm

Modifier and Type	Class	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 elki.workflow

Fields in elki.workflow with type parameters of type Algorithm

Modifier and Type	Field	Description
private java.util.List<? extends Algorithm>	AlgorithmStep.algorithms	Holds the algorithm to run.
protected java.util.List<? extends Algorithm>	AlgorithmStep.Par.algorithms	Holds the algorithm to run.

Constructor parameters in elki.workflow with type arguments of type Algorithm

Constructor	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	Description
class	CFSFDP<O>	Tutorial code for Clustering by fast search and find of density peaks.
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	SameSizeKMeans<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	Description
class	DistanceStddevOutlier<O>	A simple outlier detection algorithm that computes the standard deviation of the kNN distances.
class	ODIN<O>	Outlier detection based on the in-degree of the kNN graph.