Environment for
DeveLoping
KDD-Applications
Supported by Index-Structures

Uses of Class
de.lmu.ifi.dbs.elki.data.Clustering

Packages that use 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.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
 

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering with type parameters of type Clustering
 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
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
 Clustering<Model> ProjectedDBSCAN.getResult()
           
 Clustering<Model> SNNClustering.getResult()
           
 Clustering<Model> ByLabelClustering.getResult()
          Return clustering result
 Clustering<Model> DBSCAN.getResult()
           
 Clustering<EMModel<V>> EM.getResult()
           
 Clustering<Model> KMeans.getResult()
           
 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)
           
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
 

Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation declared as Clustering
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
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
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()
           
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
 ClusteringAlgorithm<Clustering<Model>,V> COPAC.getPartitionAlgorithm()
          Returns the partition algorithm.
 

Uses of Clustering in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace
 

Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace declared as Clustering
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
 Clustering<AxesModel> DiSH.getResult()
          Returns the result of the algorithm.
 Clustering<Model> ProjectedClustering.getResult()
           
 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
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
 

Fields in de.lmu.ifi.dbs.elki.data.cluster.naming declared as Clustering
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
SimpleEnumeratingScheme(Clustering<?> clustering)
          Constructor.
 

Uses of Clustering in de.lmu.ifi.dbs.elki.database
 

Methods in de.lmu.ifi.dbs.elki.database with type parameters of type Clustering
<O extends DatabaseObject,R extends Clustering<M>,M extends Model,L extends ClassLabel>
Database<O>
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.
<O extends DatabaseObject,R extends Clustering<M>,M extends Model>
Map<Cluster<M>,Database<O>>
PartitionsFromClustering.makeDatabasesFromClustering(Database<O> olddb, R clustering)
          Use an existing clustering to partition a database.
<O extends DatabaseObject,R extends Clustering<M>,M extends Model,L extends ClassLabel>
Map<L,Database<O>>
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
 

Methods in de.lmu.ifi.dbs.elki.evaluation.paircounting with type parameters of type Clustering
static
<R extends Clustering<M>,M extends Model,S extends Clustering<N>,N extends Model>
double
PairCountingFMeasure.compareClusterings(R result1, S result2)
          Compare two clustering results.
static
<R extends Clustering<M>,M extends Model,S extends Clustering<N>,N extends Model>
double
PairCountingFMeasure.compareClusterings(R result1, S result2)
          Compare two clustering results.
static
<R extends Clustering<M>,M extends Model,S extends Clustering<N>,N extends Model>
double
PairCountingFMeasure.compareClusterings(R result1, S result2, double beta)
          Compare two clustering results.
static
<R extends Clustering<M>,M extends Model,S extends Clustering<N>,N extends Model>
double
PairCountingFMeasure.compareClusterings(R result1, S result2, double beta)
          Compare two clustering results.
static
<R extends Clustering<M>,M extends Model>
PairSortedGeneratorInterface
PairCountingFMeasure.getPairGenerator(R clusters)
          Get a pair generator for the given Clustering
 

Uses of Clustering in de.lmu.ifi.dbs.elki.result
 

Methods in de.lmu.ifi.dbs.elki.result that return types with arguments of type Clustering
static List<Clustering<?>> ResultUtil.getClusteringResults(Result r)
          Collect all clustering results from a Result
 


Release 0.2 (2009-07-06_1820)