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.correlation	Correlation clustering algorithms
de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan	Generalized DBSCAN.
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical
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.algorithm.outlier	Outlier detection algorithms
de.lmu.ifi.dbs.elki.evaluation.clustering	Evaluation of clustering results.
de.lmu.ifi.dbs.elki.visualization.visualizers.optics	Visualizers that do work on OPTICS plots
tutorial.clustering	Classes from the tutorial on implementing a custom k-means variation.

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 DBSCAN provides the DBSCAN algorithm, an algorithm to find density-connected sets in a database.
DeLiClu DeLiClu provides the DeLiClu algorithm, a hierarchical algorithm to find density-connected sets in a database.
DeLiClu.SpatialObjectPair Encapsulates an entry in the cluster order.
EM Provides the EM algorithm (clustering by expectation maximization).
NaiveMeanShiftClustering Mean-shift based clustering algorithm.
OPTICS OPTICS provides the OPTICS algorithm.
OPTICSTypeAlgorithm Interface for OPTICS type algorithms, that can be analysed by OPTICS Xi etc.
OPTICSXi Class to handle OPTICS Xi extraction.
OPTICSXi.SteepArea Data structure to represent a steep-down-area for the xi method.
OPTICSXi.SteepDownArea Data structure to represent a steep-down-area for the xi method.
SNNClustering Shared nearest neighbor clustering.

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.
AbstractProjectedDBSCAN Provides an abstract algorithm requiring a VarianceAnalysisPreprocessor.
AbstractProjectedDBSCAN.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.
OPTICS OPTICS provides the OPTICS algorithm.
OPTICSTypeAlgorithm Interface for OPTICS type algorithms, that can be analysed by OPTICS Xi etc.

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

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.subspace

Class and Description
AbstractProjectedClustering Abstract superclass for projected clustering algorithms, like PROCLUS and ORCLUS.
AbstractProjectedClustering.Parameterizer Parameterization class.
AbstractProjectedDBSCAN Provides an abstract algorithm requiring a VarianceAnalysisPreprocessor.
AbstractProjectedDBSCAN.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.
OPTICS OPTICS provides the OPTICS algorithm.
OPTICSTypeAlgorithm Interface for OPTICS type algorithms, that can be analysed by OPTICS Xi etc.

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.outlier

Class and Description
EM Provides the EM algorithm (clustering by expectation maximization).

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.

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

Class and Description
OPTICSXi.SteepAreaResult Result containing the chi-steep areas.

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