Package elki.clustering
Interface ClusteringAlgorithm<C extends Clustering<? extends Model>>
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- Type Parameters:
C
- Clustering type
- All Superinterfaces:
Algorithm
- All Known Subinterfaces:
KMeans<V,M>
,KMedoidsClustering<O>
,SubspaceClusteringAlgorithm<M>
- All Known Implementing Classes:
AbstractBiclustering
,AbstractCutDendrogram
,AbstractKMeans
,AbstractProjectedClustering
,AffinityPropagation
,AlternatingKMedoids
,AnnulusKMeans
,BestOfMultipleKMeans
,BetulaGMM
,BetulaGMMWeighted
,BetulaLeafPreClustering
,BetulaLloydKMeans
,BIRCHLeafClustering
,BIRCHLloydKMeans
,BisectingKMeans
,ByLabelClustering
,ByLabelHierarchicalClustering
,ByLabelOrAllInOneClustering
,ByModelClustering
,CanopyPreClustering
,CASH
,CenterOfMassMetaClustering
,CFSFDP
,CFSFDP
,ChengAndChurch
,CKMeans
,CLARA
,CLARANS
,CLIQUE
,ClustersWithNoiseExtraction
,CompareMeans
,COPAC
,CutDendrogramByHeight
,CutDendrogramByNumberOfClusters
,DBSCAN
,DiSH
,DOC
,EagerPAM
,ElkanKMeans
,EM
,ERiC
,EuclideanSphericalElkanKMeans
,EuclideanSphericalHamerlyKMeans
,EuclideanSphericalSimplifiedElkanKMeans
,ExponionKMeans
,ExternalClustering
,FastCLARA
,FastCLARANS
,FastDOC
,FasterCLARA
,FasterMSC
,FasterPAM
,FastMSC
,FastPAM
,FastPAM1
,FDBSCAN
,FourC
,FuzzyCMeans
,GeneralizedDBSCAN
,GMeans
,GreedyKCenter
,GriDBSCAN
,HamerlyKMeans
,HartiganWongKMeans
,HDBSCANHierarchyExtraction
,KDTreeEM
,KDTreeFilteringKMeans
,KDTreePruningKMeans
,KMeansMinusMinus
,KMediansLloyd
,KNNKernelDensityMinimaClustering
,Leader
,LloydKMeans
,LMCLUS
,LSDBC
,MacQueenKMeans
,NaiveMeanShiftClustering
,OPTICSXi
,ORCLUS
,P3C
,PAM
,PAMMEDSIL
,PAMSIL
,ParallelGeneralizedDBSCAN
,ParallelLloydKMeans
,PreDeCon
,PROCLUS
,RepresentativeUncertainClustering
,ReynoldsPAM
,SameSizeKMeans
,ShallotKMeans
,SimplifiedElkanKMeans
,SimplifiedHierarchyExtraction
,SingleAssignmentKMeans
,SingleAssignmentKMedoids
,SNNClustering
,SortMeans
,SphericalElkanKMeans
,SphericalHamerlyKMeans
,SphericalKMeans
,SphericalSimplifiedElkanKMeans
,SphericalSimplifiedHamerlyKMeans
,SphericalSingleAssignmentKMeans
,SUBCLU
,SupportVectorClustering
,TrivialAllInOne
,TrivialAllNoise
,UKMeans
,XMeans
,YinYangKMeans
public interface ClusteringAlgorithm<C extends Clustering<? extends Model>> extends Algorithm
Interface for Algorithms that are capable to provide aClustering
as Result. in general, clustering algorithms are supposed to implement theAlgorithm
-Interface. The more specialized interfaceClusteringAlgorithm
requires an implementing algorithm to provide a special result class suitable as a partitioning of the database. More relaxed clustering algorithms are allowed to provide a result that is a fuzzy clustering, does not partition the database complete or is in any other sense a relaxed clustering result.- Since:
- 0.1
- Author:
- Arthur Zimek
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Nested Class Summary
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Nested classes/interfaces inherited from interface elki.Algorithm
Algorithm.Utils
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Method Summary
All Methods Instance Methods Default Methods Modifier and Type Method Description default C
autorun(Database database)
Try to auto-run the algorithm on a database by calling a method calledrun
, with an optionalDatabase
first, and with data relations as specified byAlgorithm.getInputTypeRestriction()
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Methods inherited from interface elki.Algorithm
getInputTypeRestriction
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Method Detail
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autorun
default C autorun(Database database)
Description copied from interface:Algorithm
Try to auto-run the algorithm on a database by calling a method calledrun
, with an optionalDatabase
first, and with data relations as specified byAlgorithm.getInputTypeRestriction()
.
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