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.correlation	Correlation clustering algorithms
de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan	Generalized DBSCAN.
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans	K-means clustering and variations.
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.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	AbstractProjectedDBSCAN<R extends Clustering<Model>,V extends NumberVector<?>> Provides an abstract algorithm requiring a VarianceAnalysisPreprocessor.
class	DBSCAN<O,D extends Distance<D>> DBSCAN provides the DBSCAN algorithm, an algorithm to find density-connected sets in a database.
class	EM<V extends NumberVector<?>> Provides the EM algorithm (clustering by expectation maximization).
class	NaiveMeanShiftClustering<V extends NumberVector<?>,D extends NumberDistance<D,?>> Mean-shift based clustering algorithm.
class	OPTICSXi<N extends NumberDistance<N,?>> Class to handle OPTICS Xi extraction.
class	SNNClustering<O> Shared nearest neighbor clustering.

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<?>> Provides the CASH algorithm, an subspace clustering algorithm based on the Hough transform.
class	COPAC<V extends NumberVector<?>,D extends Distance<D>> Provides the COPAC algorithm, 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 provides the ORCLUS algorithm, an algorithm to find clusters in high dimensional spaces.

Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation with type parameters of type ClusteringAlgorithm

Modifier and Type	Field and Description
protected Class<? extends ClusteringAlgorithm<Clustering<Model>>>	COPAC.Parameterizer.algC
private Class<? extends ClusteringAlgorithm<Clustering<Model>>>	COPAC.partitionAlgorithm Get the algorithm to run on each partition.

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation that return ClusteringAlgorithm

Modifier and Type	Method and Description
ClusteringAlgorithm<Clustering<Model>>	COPAC.getPartitionAlgorithm(DistanceQuery<V,D> query) Returns the partition algorithm.

Constructor parameters in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation with type arguments of type ClusteringAlgorithm

Constructor and Description
COPAC(FilteredLocalPCABasedDistanceFunction<V,?,D> partitionDistanceFunction, Class<? extends ClusteringAlgorithm<Clustering<Model>>> partitionAlgorithm, Collection<Pair<OptionID,Object>> partitionAlgorithmParameters) Constructor.

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.

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

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

Modifier and Type	Class and Description
class	AbstractKMeans<V extends NumberVector<?>,D extends Distance<D>,M extends MeanModel<V>> Abstract base class for k-means implementations.
class	KMeansLloyd<V extends NumberVector<?>,D extends Distance<D>> Provides the k-means algorithm, using Lloyd-style bulk iterations.
class	KMeansMacQueen<V extends NumberVector<?>,D extends Distance<D>> Provides the k-means algorithm, using MacQueen style incremental updates.
class	KMediansLloyd<V extends NumberVector<?>,D extends Distance<D>> Provides the k-medians clustering algorithm, using Lloyd-style bulk iterations.
class	KMedoidsEM<V,D extends NumberDistance<D,?>> Provides the k-medoids clustering algorithm, using a "bulk" variation of the "Partitioning Around Medoids" approach.
class	KMedoidsPAM<V,D extends NumberDistance<D,?>> Provides the k-medoids clustering algorithm, using the "Partitioning Around Medoids" approach.

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	PreDeCon<V extends NumberVector<?>> PreDeCon computes clusters of subspace preference weighted connected points.
class	PROCLUS<V extends NumberVector<?>> Provides 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.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.