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Packages that use Clustering | |
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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. |
de.lmu.ifi.dbs.elki.data.cluster.naming | Naming schemes for clusters (for output when an algorithm doesn't generate cluster names). |
de.lmu.ifi.dbs.elki.database | ELKI database layer - loading, storing, indexing and accessing data |
de.lmu.ifi.dbs.elki.evaluation.paircounting | Evaluation of clustering results via pair counting. |
de.lmu.ifi.dbs.elki.result | Result types, representation and handling |
Uses of Clustering in de.lmu.ifi.dbs.elki.algorithm.clustering |
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Classes in de.lmu.ifi.dbs.elki.algorithm.clustering with type parameters of type Clustering | |
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interface |
ClusteringAlgorithm<C extends Clustering<? extends Model>,O extends DatabaseObject>
Interface for Algorithms that are capable to provide a Clustering as Result. |
Fields in de.lmu.ifi.dbs.elki.algorithm.clustering declared as Clustering | |
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private Clustering<Model> |
ProjectedDBSCAN.result
Provides the result of the algorithm. |
protected Clustering<Model> |
SNNClustering.result
Provides the result of the algorithm. |
private Clustering<Model> |
ByLabelClustering.result
Holds the result of the algorithm. |
protected Clustering<Model> |
DBSCAN.result
Provides the result of the algorithm. |
private Clustering<EMModel<V>> |
EM.result
Keeps the result. |
private Clustering<Model> |
KMeans.result
Keeps the result. |
private Clustering<Model> |
TrivialAllInOne.result
Holds the result of the algorithm. |
private Clustering<Model> |
TrivialAllNoise.result
Holds the result of the algorithm. |
private Clustering<Model> |
ByLabelHierarchicalClustering.result
Holds the result of the algorithm. |
Methods in de.lmu.ifi.dbs.elki.algorithm.clustering that return Clustering | |
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Clustering<Model> |
ProjectedDBSCAN.getResult()
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Clustering<Model> |
SNNClustering.getResult()
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Clustering<Model> |
ByLabelClustering.getResult()
Return clustering result |
Clustering<Model> |
DBSCAN.getResult()
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Clustering<EMModel<V>> |
EM.getResult()
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Clustering<Model> |
KMeans.getResult()
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Clustering<Model> |
TrivialAllInOne.getResult()
Return clustering result |
Clustering<Model> |
TrivialAllNoise.getResult()
Return clustering result |
Clustering<Model> |
ByLabelHierarchicalClustering.getResult()
Return clustering result |
protected Clustering<Model> |
SNNClustering.runInTime(Database<O> database)
Performs the SNN clustering algorithm on the given database. |
protected Clustering<Model> |
ByLabelClustering.runInTime(Database<O> database)
Run the actual clustering algorithm. |
protected Clustering<Model> |
DBSCAN.runInTime(Database<O> database)
Performs the DBSCAN algorithm on the given database. |
protected Clustering<Model> |
TrivialAllInOne.runInTime(Database<O> database)
Run the actual clustering algorithm. |
protected Clustering<Model> |
TrivialAllNoise.runInTime(Database<O> database)
Run the actual clustering algorithm. |
protected Clustering<Model> |
ByLabelHierarchicalClustering.runInTime(Database<O> database)
Run the actual clustering algorithm. |
protected Clustering<Model> |
ProjectedDBSCAN.runInTime(Database<V> database)
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protected Clustering<EMModel<V>> |
EM.runInTime(Database<V> database)
Performs the EM clustering algorithm on the given database. |
protected Clustering<Model> |
KMeans.runInTime(Database<V> database)
Performs the k-means algorithm on the given database. |
Uses of Clustering in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation |
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Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation declared as Clustering | |
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private Clustering<CorrelationModel<V>> |
ERiC.result
Holds the result. |
private Clustering<Model> |
CASH.result
The result. |
private Clustering<Model> |
COPAC.result
Holds the result. |
Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation with type parameters of type Clustering | |
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protected ClassParameter<ClusteringAlgorithm<Clustering<Model>,V>> |
COPAC.PARTITION_ALGORITHM_PARAM
Parameter to specify the clustering algorithm to apply to each partition, must extend ClusteringAlgorithm . |
private ClusteringAlgorithm<Clustering<Model>,V> |
COPAC.partitionAlgorithm
Holds the instance of the partitioning algorithm specified by COPAC.PARTITION_ALGORITHM_PARAM . |
Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation that return Clustering | |
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private Clustering<Model> |
CASH.doRun(Database<ParameterizationFunction> database,
FiniteProgress progress)
Runs the CASH algorithm on the specified database, this method is recursively called until only noise is left. |
Clustering<CorrelationModel<V>> |
ERiC.getResult()
Returns the result of the algorithm. |
Clustering<Model> |
CASH.getResult()
Returns the result of the algorithm. |
Clustering<Model> |
COPAC.getResult()
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protected Clustering<Model> |
CASH.runInTime(Database<ParameterizationFunction> database)
Performs the CASH algorithm on the given database. |
protected Clustering<CorrelationModel<V>> |
ERiC.runInTime(Database<V> database)
Performs the ERiC algorithm on the given database. |
protected Clustering<Model> |
ORCLUS.runInTime(Database<V> database)
Performs the ORCLUS algorithm on the given database. |
protected Clustering<Model> |
COPAC.runInTime(Database<V> database)
Performs the COPAC algorithm on the given database. |
Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation that return types with arguments of type Clustering | |
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ClusteringAlgorithm<Clustering<Model>,V> |
COPAC.getPartitionAlgorithm()
Returns the partition algorithm. |
Uses of Clustering in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace |
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Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace declared as Clustering | |
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private Clustering<AxesModel> |
DiSH.result
Holds the result; |
private Clustering<Model> |
ProjectedClustering.result
The result. |
private Clustering<CLIQUESubspace<V>> |
CLIQUE.result
The result of the algorithm; |
Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace that return Clustering | |
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Clustering<AxesModel> |
DiSH.getResult()
Returns the result of the algorithm. |
Clustering<Model> |
ProjectedClustering.getResult()
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Clustering<CLIQUESubspace<V>> |
CLIQUE.getResult()
Returns the result of the algorithm. |
protected Clustering<AxesModel> |
DiSH.runInTime(Database<V> database)
Performs the DiSH algorithm on the given database. |
protected Clustering<Model> |
PROCLUS.runInTime(Database<V> database)
Performs the PROCLUS algorithm on the given database. |
protected Clustering<CLIQUESubspace<V>> |
CLIQUE.runInTime(Database<V> database)
Performs the CLIQUE algorithm on the given database. |
Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace with parameters of type Clustering | |
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protected void |
ProjectedClustering.setResult(Clustering<Model> result)
Sets the result of this algorithm. |
Uses of Clustering in de.lmu.ifi.dbs.elki.data.cluster.naming |
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Fields in de.lmu.ifi.dbs.elki.data.cluster.naming declared as Clustering | |
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private Clustering<?> |
SimpleEnumeratingScheme.clustering
Clustering this scheme is applied to. |
Constructors in de.lmu.ifi.dbs.elki.data.cluster.naming with parameters of type Clustering | |
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SimpleEnumeratingScheme(Clustering<?> clustering)
Constructor. |
Uses of Clustering in de.lmu.ifi.dbs.elki.database |
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Methods in de.lmu.ifi.dbs.elki.database with type parameters of type Clustering | ||
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LabelsFromClustering.makeDatabaseFromClustering(Database<O> olddb,
R clustering,
Class<L> classLabel)
Retrieve a cloned database that - does not contain noise points - has labels assigned based on the given clustering Useful for e.g. training a classifier based on a clustering. |
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PartitionsFromClustering.makeDatabasesFromClustering(Database<O> olddb,
R clustering)
Use an existing clustering to partition a database. |
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PartitionsFromClustering.makeDatabasesFromClustering(Database<O> olddb,
R clustering,
Class<L> classLabel)
Use an existing clustering to partition a database. |
Uses of Clustering in de.lmu.ifi.dbs.elki.evaluation.paircounting |
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Methods in de.lmu.ifi.dbs.elki.evaluation.paircounting with type parameters of type Clustering | ||
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static
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PairCountingFMeasure.compareClusterings(R result1,
S result2)
Compare two clustering results. |
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static
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PairCountingFMeasure.compareClusterings(R result1,
S result2)
Compare two clustering results. |
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static
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PairCountingFMeasure.compareClusterings(R result1,
S result2,
double beta)
Compare two clustering results. |
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static
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PairCountingFMeasure.compareClusterings(R result1,
S result2,
double beta)
Compare two clustering results. |
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static
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PairCountingFMeasure.getPairGenerator(R clusters)
Get a pair generator for the given Clustering |
Uses of Clustering in de.lmu.ifi.dbs.elki.result |
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Methods in de.lmu.ifi.dbs.elki.result that return types with arguments of type Clustering | |
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static List<Clustering<?>> |
ResultUtil.getClusteringResults(Result r)
Collect all clustering results from a Result |
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