| Package | Description | 
|---|---|
| de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical | 
 Hierarchical agglomerative clustering (HAC). 
 | 
| de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.extraction | 
 Extraction of partitional clusterings from hierarchical results. 
 | 
| de.lmu.ifi.dbs.elki.evaluation.clustering.extractor | 
 Classes to extract clusterings from hierarchical clustering. 
 | 
| tutorial.clustering | 
 Classes from the tutorial on implementing a custom k-means variation 
 | 
| Modifier and Type | Class and Description | 
|---|---|
class  | 
PointerDensityHierarchyRepresentationResult
Extended pointer representation useful for HDBSCAN. 
 | 
class  | 
PointerPrototypeHierarchyRepresentationResult
Hierarchical clustering with prototypes (used by  
MiniMax). | 
| Modifier and Type | Method and Description | 
|---|---|
PointerHierarchyRepresentationResult | 
PointerHierarchyRepresentationBuilder.complete()
Finalize the result. 
 | 
PointerHierarchyRepresentationResult | 
HierarchicalClusteringAlgorithm.run(Database db)  | 
PointerHierarchyRepresentationResult | 
NNChain.run(Database db,
   Relation<O> relation)
Run the algorithm 
 | 
PointerHierarchyRepresentationResult | 
AGNES.run(Database db,
   Relation<O> relation)
Run the algorithm 
 | 
PointerHierarchyRepresentationResult | 
SLINK.run(Database database,
   Relation<O> relation)
Performs the SLINK algorithm on the given database. 
 | 
PointerHierarchyRepresentationResult | 
MiniMaxAnderberg.run(Database db,
   Relation<O> relation)
Run the algorithm 
 | 
PointerHierarchyRepresentationResult | 
AnderbergHierarchicalClustering.run(Database db,
   Relation<O> relation)
Run the algorithm 
 | 
| Modifier and Type | Field and Description | 
|---|---|
protected PointerHierarchyRepresentationResult | 
SimplifiedHierarchyExtraction.Instance.pointerresult
The hierarchical result to process. 
 | 
protected PointerHierarchyRepresentationResult | 
HDBSCANHierarchyExtraction.Instance.pointerresult
The hierarchical result to process. 
 | 
protected PointerHierarchyRepresentationResult | 
ClustersWithNoiseExtraction.Instance.pointerresult
The hierarchical result to process. 
 | 
protected PointerHierarchyRepresentationResult | 
AbstractCutDendrogram.Instance.pointerresult
The hierarchical result to process. 
 | 
| Modifier and Type | Method and Description | 
|---|---|
Clustering<DendrogramModel> | 
CutDendrogramByNumberOfClusters.run(PointerHierarchyRepresentationResult pointerresult)  | 
Clustering<DendrogramModel> | 
CutDendrogramByHeight.run(PointerHierarchyRepresentationResult pointerresult)  | 
Clustering<DendrogramModel> | 
SimplifiedHierarchyExtraction.run(PointerHierarchyRepresentationResult pointerresult)
Process an existing result. 
 | 
Clustering<DendrogramModel> | 
HDBSCANHierarchyExtraction.run(PointerHierarchyRepresentationResult pointerresult)
Process an existing result. 
 | 
Clustering<Model> | 
ClustersWithNoiseExtraction.run(PointerHierarchyRepresentationResult pointerresult)
Process an existing result. 
 | 
abstract Clustering<DendrogramModel> | 
AbstractCutDendrogram.run(PointerHierarchyRepresentationResult pointerresult)
Process a pointer hierarchy result. 
 | 
| Constructor and Description | 
|---|
Instance(PointerHierarchyRepresentationResult pointerresult)
Constructor. 
 | 
Instance(PointerHierarchyRepresentationResult pointerresult)
Constructor. 
 | 
Instance(PointerHierarchyRepresentationResult pointerresult)
Constructor. 
 | 
Instance(PointerHierarchyRepresentationResult pointerresult)
Constructor. 
 | 
Instance(PointerHierarchyRepresentationResult pointerresult)
Constructor. 
 | 
Instance(PointerHierarchyRepresentationResult pointerresult)
Constructor. 
 | 
| Modifier and Type | Method and Description | 
|---|---|
PointerHierarchyRepresentationResult | 
CutDendrogramByHeightExtractor.DummyHierarchicalClusteringAlgorithm.run(Database db)  | 
| Modifier and Type | Method and Description | 
|---|---|
PointerHierarchyRepresentationResult | 
NaiveAgglomerativeHierarchicalClustering4.run(Database db,
   Relation<O> relation)
Run the algorithm 
 | 
Copyright © 2019 ELKI Development Team. License information.