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Packages that use Model | |
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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.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 |
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Classes in de.lmu.ifi.dbs.elki.algorithm.clustering with type parameters of type Model | |
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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 with type parameters of type Model | |
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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<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 | |
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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<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<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 |
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Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.correlation with type parameters of type Model | |
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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 . |
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 | |
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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. |
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 |
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Fields in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace with type parameters of type Model | |
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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 | |
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Clustering<Model> |
ProjectedClustering.getResult()
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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 | |
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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 |
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Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.clique that implement Model | |
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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 |
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Classes in de.lmu.ifi.dbs.elki.data with type parameters of type Model | |
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class |
Clustering<M extends Model>
Result class for clusterings. |
Uses of Model in de.lmu.ifi.dbs.elki.data.cluster |
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Classes in de.lmu.ifi.dbs.elki.data.cluster with type parameters of type Model | |
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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 | |
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private M |
Cluster.model
Cluster model. |
Uses of Model in de.lmu.ifi.dbs.elki.data.model |
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Classes in de.lmu.ifi.dbs.elki.data.model that implement Model | |
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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 |
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Methods in de.lmu.ifi.dbs.elki.database with type parameters of type Model | ||
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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 Model in de.lmu.ifi.dbs.elki.evaluation |
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Methods in de.lmu.ifi.dbs.elki.evaluation that return types with arguments of type Model | |
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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 |
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Methods in de.lmu.ifi.dbs.elki.evaluation.paircounting with type parameters of type Model | ||
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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 |
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