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Packages that use ClusteringAlgorithm | |
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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. |
Uses of ClusteringAlgorithm in de.lmu.ifi.dbs.elki.algorithm.clustering |
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Classes in de.lmu.ifi.dbs.elki.algorithm.clustering that implement ClusteringAlgorithm | |
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class |
ByLabelClustering<O extends DatabaseObject>
Pseudo clustering using labels. |
class |
ByLabelHierarchicalClustering<O extends DatabaseObject>
Pseudo clustering using labels. |
class |
DBSCAN<O extends DatabaseObject,D extends Distance<D>>
DBSCAN provides the DBSCAN algorithm, an algorithm to find density-connected sets in a database. |
class |
EM<V extends NumberVector<V,?>>
Provides the EM algorithm (clustering by expectation maximization). |
class |
KMeans<D extends Distance<D>,V extends NumberVector<V,?>>
Provides the k-means algorithm. |
class |
ProjectedDBSCAN<V extends NumberVector<V,?>>
Provides an abstract algorithm requiring a VarianceAnalysisPreprocessor. |
class |
SNNClustering<O extends DatabaseObject,D extends Distance<D>>
Shared nearest neighbor clustering. |
class |
TrivialAllInOne<O extends DatabaseObject>
Trivial pseudo-clustering that just considers all points to be one big cluster. |
class |
TrivialAllNoise<O extends DatabaseObject>
Trivial pseudo-clustering that just considers all points to be noise. |
Uses of ClusteringAlgorithm in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation |
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Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation that implement ClusteringAlgorithm | |
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class |
CASH
Provides the CASH algorithm, an subspace clustering algorithm based on the hough transform. |
class |
COPAC<V extends NumberVector<V,?>>
Provides the COPAC algorithm, an algorithm to partition a database according to the correlation dimension of its objects and to then perform an arbitrary clustering algorithm over the partitions. |
class |
ERiC<V extends NumberVector<V,?>>
Performs correlation clustering on the data partitioned according to local correlation dimensionality and builds a hierarchy of correlation clusters that allows multiple inheritance from the clustering result. |
class |
FourC<O extends NumberVector<O,?>>
4C identifies local subgroups of data objects sharing a uniform correlation. |
class |
ORCLUS<V extends NumberVector<V,?>>
ORCLUS provides the ORCLUS algorithm, an algorithm to find clusters in high dimensional spaces. |
Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation declared as ClusteringAlgorithm | |
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private ClusteringAlgorithm<Clustering<Model>,V> |
COPAC.partitionAlgorithm
Holds the instance of the partitioning algorithm specified by COPAC.PARTITION_ALGORITHM_PARAM . |
Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation with type parameters of type ClusteringAlgorithm | |
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protected ObjectParameter<ClusteringAlgorithm<Clustering<Model>,V>> |
COPAC.PARTITION_ALGORITHM_PARAM
Parameter to specify the clustering algorithm to apply to each partition, must extend ClusteringAlgorithm . |
Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation that return ClusteringAlgorithm | |
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ClusteringAlgorithm<Clustering<Model>,V> |
COPAC.getPartitionAlgorithm()
Returns the partition algorithm. |
Uses of ClusteringAlgorithm in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace |
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Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace that implement ClusteringAlgorithm | |
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class |
CLIQUE<V extends NumberVector<V,?>>
Implementation of the CLIQUE algorithm, a grid-based algorithm to identify dense clusters in subspaces of maximum dimensionality. |
class |
DiSH<V extends NumberVector<V,?>>
Algorithm for detecting subspace hierarchies. |
class |
PreDeCon<V extends NumberVector<V,?>>
PreDeCon computes clusters of subspace preference weighted connected points. |
class |
PROCLUS<V extends NumberVector<V,?>>
Provides the PROCLUS algorithm, an algorithm to find subspace clusters in high dimensional spaces. |
class |
ProjectedClustering<V extends NumberVector<V,?>>
Abstract superclass for projected clustering algorithms, like PROCLUS
and ORCLUS . |
class |
SUBCLU<V extends NumberVector<V,?>,D extends Distance<D>>
Implementation of the SUBCLU algorithm, an algorithm to detect arbitrarily shaped and positioned clusters in subspaces. |
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