Uses of Interface
elki.clustering.ClusteringAlgorithm

Packages that use ClusteringAlgorithm

Package	Description
elki.application.experiments	Packaged experiments to make them easy to reproduce.
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.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.evaluation.clustering	Evaluation of clustering results.
elki.outlier.clustering	Clustering based outlier detection.
tutorial.clustering	Classes from the tutorial on implementing a custom k-means variation.

Uses of ClusteringAlgorithm in elki.application.experiments

Fields in elki.application.experiments with type parameters of type ClusteringAlgorithm

Modifier and Type	Field	Description
private java.lang.Class<? extends ClusteringAlgorithm<?>>	ORLibBenchmark.alg	Clustering algorithm
private java.lang.Class<? extends ClusteringAlgorithm<?>>	ORLibBenchmark.Par.alg	Clustering algorithm

Constructor parameters in elki.application.experiments with type arguments of type ClusteringAlgorithm

Constructor	Description
ORLibBenchmark(java.net.URI file, java.lang.Class<? extends ClusteringAlgorithm<?>> alg, KMedoidsInitialization<DBID> init, int k, RandomFactory rnd)	Constructor.

Uses of ClusteringAlgorithm in elki.clustering

Classes in elki.clustering that implement ClusteringAlgorithm

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 ClusteringAlgorithm in elki.clustering.affinitypropagation

Classes in elki.clustering.affinitypropagation that implement ClusteringAlgorithm

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

Uses of ClusteringAlgorithm in elki.clustering.biclustering

Classes in elki.clustering.biclustering that implement ClusteringAlgorithm

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 ClusteringAlgorithm in elki.clustering.correlation

Classes in elki.clustering.correlation that implement ClusteringAlgorithm

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	LMCLUS	Linear manifold clustering in high dimensional spaces by stochastic search.
class	ORCLUS	ORCLUS: Arbitrarily ORiented projected CLUSter generation.

Uses of ClusteringAlgorithm in elki.clustering.dbscan

Classes in elki.clustering.dbscan that implement ClusteringAlgorithm

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 ClusteringAlgorithm in elki.clustering.dbscan.parallel

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

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

Uses of ClusteringAlgorithm in elki.clustering.em

Classes in elki.clustering.em that implement ClusteringAlgorithm

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 ClusteringAlgorithm in elki.clustering.hierarchical.birch

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

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 ClusteringAlgorithm in elki.clustering.hierarchical.extraction

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

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 ClusteringAlgorithm in elki.clustering.kcenter

Classes in elki.clustering.kcenter that implement ClusteringAlgorithm

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 ClusteringAlgorithm in elki.clustering.kmeans

Subinterfaces of ClusteringAlgorithm 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 ClusteringAlgorithm

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 ClusteringAlgorithm in elki.clustering.kmeans.parallel

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

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

Uses of ClusteringAlgorithm in elki.clustering.kmeans.spherical

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

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 ClusteringAlgorithm in elki.clustering.kmedoids

Subinterfaces of ClusteringAlgorithm 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 ClusteringAlgorithm

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 ClusteringAlgorithm in elki.clustering.meta

Classes in elki.clustering.meta that implement ClusteringAlgorithm

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

Uses of ClusteringAlgorithm in elki.clustering.onedimensional

Classes in elki.clustering.onedimensional that implement ClusteringAlgorithm

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 ClusteringAlgorithm in elki.clustering.optics

Classes in elki.clustering.optics that implement ClusteringAlgorithm

Modifier and Type	Class	Description
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 ClusteringAlgorithm in elki.clustering.silhouette

Classes in elki.clustering.silhouette that implement ClusteringAlgorithm

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 ClusteringAlgorithm in elki.clustering.subspace

Subinterfaces of ClusteringAlgorithm 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 ClusteringAlgorithm

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	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 ClusteringAlgorithm in elki.clustering.svm

Classes in elki.clustering.svm that implement ClusteringAlgorithm

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 ClusteringAlgorithm in elki.clustering.trivial

Classes in elki.clustering.trivial that implement ClusteringAlgorithm

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 ClusteringAlgorithm in elki.clustering.uncertain

Classes in elki.clustering.uncertain that implement ClusteringAlgorithm

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.

Fields in elki.clustering.uncertain declared as ClusteringAlgorithm

Modifier and Type	Field	Description
protected ClusteringAlgorithm<C>	CenterOfMassMetaClustering.inner	The algorithm to be wrapped and run.
protected ClusteringAlgorithm<C>	CenterOfMassMetaClustering.Par.inner	Field to store the algorithm.
protected ClusteringAlgorithm<?>	RepresentativeUncertainClustering.metaAlgorithm	The algorithm for meta-clustering.
protected ClusteringAlgorithm<?>	RepresentativeUncertainClustering.Par.metaAlgorithm	Field to store the inner algorithm for meta-clustering
protected ClusteringAlgorithm<?>	RepresentativeUncertainClustering.Par.samplesAlgorithm	Field to store the algorithm.
protected ClusteringAlgorithm<?>	RepresentativeUncertainClustering.samplesAlgorithm	The algorithm to be wrapped and run.

Constructors in elki.clustering.uncertain with parameters of type ClusteringAlgorithm

Constructor	Description
CenterOfMassMetaClustering(ClusteringAlgorithm<C> inner)	Constructor, quite trivial.
RepresentativeUncertainClustering(ClusteringDistanceSimilarity distance, ClusteringAlgorithm<?> metaAlgorithm, ClusteringAlgorithm<?> samplesAlgorithm, int numsamples, RandomFactory random, double alpha, boolean keep)	Constructor, quite trivial.

Uses of ClusteringAlgorithm in elki.evaluation.clustering

Fields in elki.evaluation.clustering declared as ClusteringAlgorithm

Modifier and Type	Field	Description
private ClusteringAlgorithm<?>	EvaluateClustering.Par.referencealg	Reference algorithm.
private ClusteringAlgorithm<?>	EvaluateClustering.referencealg	Reference algorithm.

Constructors in elki.evaluation.clustering with parameters of type ClusteringAlgorithm

Constructor	Description
EvaluateClustering(ClusteringAlgorithm<?> referencealg, boolean noiseSpecialHandling, boolean selfPairing)	Constructor.

Uses of ClusteringAlgorithm in elki.outlier.clustering

Fields in elki.outlier.clustering declared as ClusteringAlgorithm

Modifier and Type	Field	Description
protected ClusteringAlgorithm<?>	SilhouetteOutlierDetection.clusterer	Clustering algorithm to use
protected ClusteringAlgorithm<?>	SilhouetteOutlierDetection.Par.clusterer	Clustering algorithm to use
(package private) ClusteringAlgorithm<?>	NoiseAsOutliers.clustering	Clustering algorithm to use
(package private) ClusteringAlgorithm<?>	NoiseAsOutliers.Par.clustering	Clustering algorithm to run.
protected ClusteringAlgorithm<Clustering<MeanModel>>	CBLOF.clusteringAlgorithm	The clustering algorithm to use.
protected ClusteringAlgorithm<Clustering<MeanModel>>	CBLOF.Par.clusteringAlgorithm	The clustering algorithm to use.

Constructors in elki.outlier.clustering with parameters of type ClusteringAlgorithm

Constructor	Description
CBLOF(NumberVectorDistance<? super O> distance, ClusteringAlgorithm<Clustering<MeanModel>> clusteringAlgorithm, double alpha, double beta)	Constructor.
NoiseAsOutliers(ClusteringAlgorithm<?> clustering)	Constructor.
SilhouetteOutlierDetection(Distance<? super O> distance, ClusteringAlgorithm<?> clusterer, NoiseHandling noiseOption)	Constructor.

Uses of ClusteringAlgorithm in tutorial.clustering

Classes in tutorial.clustering that implement ClusteringAlgorithm

Modifier and Type	Class	Description
class	CFSFDP<O>	Tutorial code for Clustering by fast search and find of density peaks.
class	SameSizeKMeans<V extends NumberVector>	K-means variation that produces equally sized clusters.