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

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

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.
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.paircounting	Evaluation of clustering results via pair counting.
de.lmu.ifi.dbs.elki.visualization.visualizers.optics	Visualizers that do work on OPTICS plots

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

KMeans
Provides the k-means 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.

SLINK
Efficient implementation of the Single-Link Algorithm SLINK of R.

SNNClustering
Shared nearest neighbor clustering.

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

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.subspace

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

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

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.paircounting

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

OPTICSXi.SteepAreaResult
Result containing the chi-steep areas.