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

Packages that use ClusteringAlgorithm

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.correlation	Correlation 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.

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

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

class	ByLabelClustering<O extends DatabaseObject> Pseudo clustering using labels.
class	ByLabelHierarchicalClustering<O extends DatabaseObject> Pseudo clustering using labels.
class	DBSCAN<O extends DatabaseObject,D extends Distance<D>> DBSCAN provides the DBSCAN algorithm, an algorithm to find density-connected sets in a database.
class	EM<V extends RealVector<V,?>> Provides the EM algorithm (clustering by expectation maximization).
class	KMeans<D extends Distance<D>,V extends RealVector<V,?>> Provides the k-means algorithm.
class	ProjectedDBSCAN<V extends RealVector<V,?>> Provides an abstract algorithm requiring a VarianceAnalysisPreprocessor.
class	SNNClustering<O extends DatabaseObject,D extends Distance<D>> Shared nearest neighbor clustering.
class	TrivialAllInOne<O extends DatabaseObject> Trivial pseudo-clustering that just considers all points to be one big cluster.
class	TrivialAllNoise<O extends DatabaseObject> Trivial pseudo-clustering that just considers all points to be noise.

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

class	CASH Provides the CASH algorithm, an subspace clustering algorithm based on the hough transform.
class	COPAC<V extends RealVector<V,?>> 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 RealVector<V,?>> 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<O extends RealVector<O,?>> 4C identifies local subgroups of data objects sharing a uniform correlation.
class	ORCLUS<V extends RealVector<V,?>> ORCLUS provides the ORCLUS algorithm, an algorithm to find clusters in high dimensional spaces.

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

private ClusteringAlgorithm<Clustering<Model>,V>

COPAC.partitionAlgorithm Holds the instance of the partitioning algorithm specified by COPAC.PARTITION_ALGORITHM_PARAM.

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

protected ClassParameter<ClusteringAlgorithm<Clustering<Model>,V>>

COPAC.PARTITION_ALGORITHM_PARAM Parameter to specify the clustering algorithm to apply to each partition, must extend ClusteringAlgorithm.

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

ClusteringAlgorithm<Clustering<Model>,V>

COPAC.getPartitionAlgorithm() Returns the partition algorithm.

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

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

class	CLIQUE<V extends RealVector<V,?>> Implementation of the CLIQUE algorithm, a grid-based algorithm to identify dense clusters in subspaces of maximum dimensionality.
class	DiSH<V extends RealVector<V,?>> Algorithm for detecting subspace hierarchies.
class	PreDeCon<V extends RealVector<V,?>> PreDeCon computes clusters of subspace preference weighted connected points.
class	PROCLUS<V extends RealVector<V,?>> Provides the PROCLUS algorithm, an algorithm to find subspace clusters in high dimensional spaces.
class	ProjectedClustering<V extends RealVector<V,?>> Abstract superclass for projected clustering algorithms, like PROCLUS and ORCLUS.