Uses of Package
de.lmu.ifi.dbs.elki.algorithm.clustering

Packages that use de.lmu.ifi.dbs.elki.algorithm.clustering

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.
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.

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering used by de.lmu.ifi.dbs.elki.algorithm.clustering

Class and Description
CanopyPreClustering Canopy pre-clustering is a simple preprocessing step for clustering.
ClusteringAlgorithm Interface for Algorithms that are capable to provide a Clustering as Result. in general, clustering algorithms are supposed to implement the Algorithm-Interface.
DBSCAN Density-Based Clustering of Applications with Noise (DBSCAN), an algorithm to find density-connected sets in a database.
NaiveMeanShiftClustering Mean-shift based clustering algorithm.
SNNClustering Shared nearest neighbor clustering.

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering used by de.lmu.ifi.dbs.elki.algorithm.clustering.affinitypropagation

Class and Description
ClusteringAlgorithm 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 de.lmu.ifi.dbs.elki.algorithm.clustering used by de.lmu.ifi.dbs.elki.algorithm.clustering.biclustering

Class and Description
ClusteringAlgorithm 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 de.lmu.ifi.dbs.elki.algorithm.clustering used by de.lmu.ifi.dbs.elki.algorithm.clustering.correlation

Class and Description
AbstractProjectedClustering Abstract superclass for projected clustering algorithms, like PROCLUS and ORCLUS.
AbstractProjectedClustering.Parameterizer Parameterization class.
ClusteringAlgorithm 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 de.lmu.ifi.dbs.elki.algorithm.clustering used by de.lmu.ifi.dbs.elki.algorithm.clustering.em

Class and Description
ClusteringAlgorithm 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 de.lmu.ifi.dbs.elki.algorithm.clustering used by de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan

Class and Description
ClusteringAlgorithm 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 de.lmu.ifi.dbs.elki.algorithm.clustering used by de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.extraction

Class and Description
ClusteringAlgorithm 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 de.lmu.ifi.dbs.elki.algorithm.clustering used by de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans

Class and Description
ClusteringAlgorithm 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 de.lmu.ifi.dbs.elki.algorithm.clustering used by de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.parallel

Class and Description
ClusteringAlgorithm 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 de.lmu.ifi.dbs.elki.algorithm.clustering used by de.lmu.ifi.dbs.elki.algorithm.clustering.meta

Class and Description
ClusteringAlgorithm 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 de.lmu.ifi.dbs.elki.algorithm.clustering used by de.lmu.ifi.dbs.elki.algorithm.clustering.onedimensional

Class and Description
ClusteringAlgorithm 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 de.lmu.ifi.dbs.elki.algorithm.clustering used by de.lmu.ifi.dbs.elki.algorithm.clustering.optics

Class and Description
ClusteringAlgorithm 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 de.lmu.ifi.dbs.elki.algorithm.clustering used by de.lmu.ifi.dbs.elki.algorithm.clustering.subspace

Class and Description
AbstractProjectedClustering Abstract superclass for projected clustering algorithms, like PROCLUS and ORCLUS.
AbstractProjectedClustering.Parameterizer Parameterization class.
ClusteringAlgorithm 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 de.lmu.ifi.dbs.elki.algorithm.clustering used by de.lmu.ifi.dbs.elki.algorithm.clustering.trivial

Class and Description
ClusteringAlgorithm 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 de.lmu.ifi.dbs.elki.algorithm.clustering used by de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain

Class and Description
ClusteringAlgorithm 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 de.lmu.ifi.dbs.elki.algorithm.clustering used by de.lmu.ifi.dbs.elki.algorithm.outlier.clustering

Class and Description
ClusteringAlgorithm 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 de.lmu.ifi.dbs.elki.algorithm.clustering used by de.lmu.ifi.dbs.elki.evaluation.clustering

Class and Description
ClusteringAlgorithm Interface for Algorithms that are capable to provide a Clustering as Result. in general, clustering algorithms are supposed to implement the Algorithm-Interface.