Package | Description |
---|---|
de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan |
Generalized DBSCAN.
|
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.outlier.subspace |
Subspace outlier detection methods.
|
Class and Description |
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PreDeCon.Settings
Class containing all the PreDeCon settings.
|
Class and Description |
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CLIQUE
Implementation of the CLIQUE algorithm, a grid-based algorithm to identify
dense clusters in subspaces of maximum dimensionality.
|
DiSH
Algorithm for detecting subspace hierarchies.
|
DiSH.DiSHClusterOrder
DiSH cluster order.
|
DOC
The DOC algorithm, and it's heuristic variant, FastDOC.
|
HiSC
Implementation of the HiSC algorithm, an algorithm for detecting hierarchies
of subspace clusters.
|
P3C
P3C: A Robust Projected Clustering Algorithm.
|
P3C.ClusterCandidate
This class is used to represent potential clusters.
|
P3C.Signature
P3C Cluster signature.
|
PreDeCon
PreDeCon computes clusters of subspace preference weighted connected points.
|
PreDeCon.Settings
Class containing all the PreDeCon settings.
|
PROCLUS
The PROCLUS algorithm, an algorithm to find subspace clusters in high
dimensional spaces.
|
PROCLUS.DoubleIntInt
Simple triple.
|
PROCLUS.PROCLUSCluster
Encapsulates the attributes of a cluster.
|
SUBCLU
Implementation of the SUBCLU algorithm, an algorithm to detect arbitrarily
shaped and positioned clusters in subspaces.
|
SubspaceClusteringAlgorithm
Interface for subspace clustering algorithms that use a model derived from
SubspaceModel , that can then be post-processed for outlier detection. |
Class and Description |
---|
SubspaceClusteringAlgorithm
Interface for subspace clustering algorithms that use a model derived from
SubspaceModel , that can then be post-processed for outlier detection. |
Copyright © 2015 ELKI Development Team, Lehr- und Forschungseinheit für Datenbanksysteme, Ludwig-Maximilians-Universität München. License information.