Uses of Interface
de.lmu.ifi.dbs.elki.algorithm.clustering.ClusteringAlgorithm

Packages that use ClusteringAlgorithm

Package	Description
de.lmu.ifi.dbs.elki.algorithm.clustering	Clustering algorithms.
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.
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.outlier.clustering	Clustering based outlier detection.
de.lmu.ifi.dbs.elki.evaluation.clustering	Evaluation of clustering results.
tutorial.clustering	Classes from the tutorial on implementing a custom k-means variation.

Uses of ClusteringAlgorithm in de.lmu.ifi.dbs.elki.algorithm.clustering

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering that implement ClusteringAlgorithm

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	NaiveMeanShiftClustering<V extends NumberVector> Mean-shift based clustering algorithm.
class	SNNClustering<O> Shared nearest neighbor clustering.

Uses of ClusteringAlgorithm in de.lmu.ifi.dbs.elki.algorithm.clustering.affinitypropagation

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.affinitypropagation that implement ClusteringAlgorithm

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

Uses of ClusteringAlgorithm in de.lmu.ifi.dbs.elki.algorithm.clustering.biclustering

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.biclustering that implement ClusteringAlgorithm

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> Perform Cheng and Church biclustering.

Uses of ClusteringAlgorithm in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation that implement ClusteringAlgorithm

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	ORCLUS<V extends NumberVector> ORCLUS: Arbitrarily ORiented projected CLUSter generation.

Uses of ClusteringAlgorithm in de.lmu.ifi.dbs.elki.algorithm.clustering.em

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.em that implement ClusteringAlgorithm

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).

Uses of ClusteringAlgorithm in de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan that implement ClusteringAlgorithm

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 ClusteringAlgorithm in de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.extraction

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.extraction that implement ClusteringAlgorithm

Modifier and Type	Class and Description
class	ExtractFlatClusteringFromHierarchy 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 ClusteringAlgorithm in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans

Subinterfaces of ClusteringAlgorithm 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 ClusteringAlgorithm

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	KMeansBatchedLloyd<V extends NumberVector> An algorithm for k-means, using Lloyd-style bulk iterations.
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	KMeansElkan<V extends NumberVector> Elkan's fast k-means by exploiting the triangle inequality.
class	KMeansHamerly<V extends NumberVector> Hamerly's fast k-means by exploiting the triangle inequality.
class	KMeansHybridLloydMacQueen<V extends NumberVector> A hybrid k-means algorithm, alternating between MacQueen-style incremental processing and Lloyd-Style batch steps.
class	KMeansLloyd<V extends NumberVector> The standard k-means algorithm, using Lloyd-style bulk iterations.
class	KMeansMacQueen<V extends NumberVector> The original k-means algorithm, using MacQueen style incremental updates; making this effectively an "online" (streaming) algorithm.
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	KMedoidsEM<V> A k-medoids clustering algorithm, implemented as EM-style bulk algorithm.
class	KMedoidsPAM<V> The original PAM algorithm or k-medoids clustering, as proposed by Kaufman and Rousseeuw in "Partitioning Around Medoids".
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 ClusteringAlgorithm in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.parallel

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.parallel that implement ClusteringAlgorithm

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

Uses of ClusteringAlgorithm in de.lmu.ifi.dbs.elki.algorithm.clustering.meta

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.meta that implement ClusteringAlgorithm

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

Uses of ClusteringAlgorithm in de.lmu.ifi.dbs.elki.algorithm.clustering.onedimensional

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.onedimensional that implement ClusteringAlgorithm

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 ClusteringAlgorithm in de.lmu.ifi.dbs.elki.algorithm.clustering.optics

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.optics that implement ClusteringAlgorithm

Modifier and Type	Class and Description
class	OPTICSXi Class to handle OPTICS Xi extraction.

Uses of ClusteringAlgorithm in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace

Subinterfaces of ClusteringAlgorithm 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 ClusteringAlgorithm

Modifier and Type	Class and Description
class	CLIQUE<V extends NumberVector> 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> The DOC algorithm, and it's heuristic variant, FastDOC.
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 ClusteringAlgorithm in de.lmu.ifi.dbs.elki.algorithm.clustering.trivial

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.trivial that implement ClusteringAlgorithm

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 ClusteringAlgorithm in de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain that implement ClusteringAlgorithm

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.

Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain declared as ClusteringAlgorithm

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

Constructors in de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain with parameters of type ClusteringAlgorithm

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

Uses of ClusteringAlgorithm in de.lmu.ifi.dbs.elki.algorithm.outlier.clustering

Fields in de.lmu.ifi.dbs.elki.algorithm.outlier.clustering declared as ClusteringAlgorithm

Modifier and Type	Field and Description
(package private) ClusteringAlgorithm<?>	SilhouetteOutlierDetection.clusterer Clustering algorithm to use
(package private) ClusteringAlgorithm<?>	SilhouetteOutlierDetection.Parameterizer.clusterer Clustering algorithm to use

Constructors in de.lmu.ifi.dbs.elki.algorithm.outlier.clustering with parameters of type ClusteringAlgorithm

Constructor and Description
SilhouetteOutlierDetection(DistanceFunction<? super O> distanceFunction, ClusteringAlgorithm<?> clusterer, NoiseHandling noiseOption) Constructor.

Uses of ClusteringAlgorithm in de.lmu.ifi.dbs.elki.evaluation.clustering

Fields in de.lmu.ifi.dbs.elki.evaluation.clustering declared as ClusteringAlgorithm

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

Constructors in de.lmu.ifi.dbs.elki.evaluation.clustering with parameters of type ClusteringAlgorithm

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

Uses of ClusteringAlgorithm in tutorial.clustering

Classes in tutorial.clustering that implement ClusteringAlgorithm

Modifier and Type	Class and Description
class	SameSizeKMeansAlgorithm<V extends NumberVector> K-means variation that produces equally sized clusters.