Environment for
DeveLoping
KDD-Applications
Supported by Index-Structures

Uses of Interface
de.lmu.ifi.dbs.elki.data.model.Model

Packages that use Model
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.algorithm.clustering.subspace.clique Helper classes for the CLIQUE algorithm. 
de.lmu.ifi.dbs.elki.data Basic classes for different data types, database object types and label types. 
de.lmu.ifi.dbs.elki.data.cluster Cluster classes. 
de.lmu.ifi.dbs.elki.data.model Cluster models classes for various algorithms. 
de.lmu.ifi.dbs.elki.database ELKI database layer - loading, storing, indexing and accessing data 
de.lmu.ifi.dbs.elki.evaluation Functionality for the evaluation of algorithms. 
de.lmu.ifi.dbs.elki.evaluation.paircounting Evaluation of clustering results via pair counting. 
 

Uses of Model in de.lmu.ifi.dbs.elki.algorithm.clustering
 

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering with type parameters of type Model
 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 with type parameters of type Model
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<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 types with arguments of type Model
 Clustering<Model> ProjectedDBSCAN.getResult()
           
 Clustering<Model> SNNClustering.getResult()
           
 Clustering<Model> ByLabelClustering.getResult()
          Return clustering result
 Clustering<Model> DBSCAN.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<Model> KMeans.runInTime(Database<V> database)
          Performs the k-means algorithm on the given database.
 

Uses of Model in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation
 

Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation with type parameters of type Model
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.
private  Clustering<Model> CASH.result
          The result.
private  Clustering<Model> COPAC.result
          Holds the result.
 

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation that return types with arguments of type Model
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.
 ClusteringAlgorithm<Clustering<Model>,V> COPAC.getPartitionAlgorithm()
          Returns the partition 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<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.
 

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

Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace with type parameters of type Model
private  Clustering<Model> ProjectedClustering.result
          The result.
 

Methods in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace that return types with arguments of type Model
 Clustering<Model> ProjectedClustering.getResult()
           
protected  Clustering<Model> PROCLUS.runInTime(Database<V> database)
          Performs the PROCLUS algorithm on the given database.
 

Method parameters in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace with type arguments of type Model
protected  void ProjectedClustering.setResult(Clustering<Model> result)
          Sets the result of this algorithm.
 

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

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.clique that implement Model
 class CLIQUESubspace<V extends RealVector<V,?>>
          Represents a subspace of the original dataspace in the CLIQUE algorithm.
 

Uses of Model in de.lmu.ifi.dbs.elki.data
 

Classes in de.lmu.ifi.dbs.elki.data with type parameters of type Model
 class Clustering<M extends Model>
          Result class for clusterings.
 

Uses of Model in de.lmu.ifi.dbs.elki.data.cluster
 

Classes in de.lmu.ifi.dbs.elki.data.cluster with type parameters of type Model
 class Cluster<M extends Model>
          Generic cluster class, that may or not have hierarchical information.
 

Fields in de.lmu.ifi.dbs.elki.data.cluster declared as Model
private  M Cluster.model
          Cluster model.
 

Uses of Model in de.lmu.ifi.dbs.elki.data.model
 

Classes in de.lmu.ifi.dbs.elki.data.model that implement Model
 class AxesModel
          Simple model for Axis-Parallel Subspace Clusters.
 class BaseModel
          Abstract base class for Cluster Models.
 class Bicluster<V extends RealVector<V,Double>>
          Wrapper class to provide the basic properties of a bicluster.
 class BiclusterWithInverted<V extends RealVector<V,Double>>
          This code was factored out of the Bicluster class, since not all biclusters have inverted rows.
 class ClusterModel
          Generic cluster model.
 class CorrelationAnalysisSolution<V extends RealVector<V,?>>
          A solution of correlation analysis is a matrix of equations describing the dependencies.
 class CorrelationModel<V extends RealVector<V,?>>
          Cluster model using a filtered PCA result and an centroid.
 class DimensionModel
          Cluster model just providing a cluster dimensionality.
 class EMModel<V extends RealVector<V,?>>
          Cluster model of an EM cluster, providing a mean and a full covariance Matrix.
 class LinearEquationModel
          Cluster model containing a linear equation system for the cluster.
 

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

Methods in de.lmu.ifi.dbs.elki.database with type parameters of type Model
<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 Model in de.lmu.ifi.dbs.elki.evaluation
 

Methods in de.lmu.ifi.dbs.elki.evaluation that return types with arguments of type Model
private  Cluster<Model> ComputeROCCurve.getReferenceCluster(Database<O> database, String class_name)
          Find the "positive" reference cluster using a by label clustering.
 

Uses of Model in de.lmu.ifi.dbs.elki.evaluation.paircounting
 

Methods in de.lmu.ifi.dbs.elki.evaluation.paircounting with type parameters of type Model
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
 


Release 0.2.1 (2009-07-13_1605)