| Package | Description |
|---|---|
| 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 |
|---|
| 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.
|
| DOC
Provides 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.
|
| PROCLUS
Provides the PROCLUS algorithm, an algorithm to find subspace clusters in
high dimensional spaces.
|
| 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. |