Package elki.clustering

Interface ClusteringAlgorithm<C extends Clustering<? extends Model>>

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 a Clustering as Result. in general, clustering algorithms are supposed to implement the Algorithm-Interface. The more specialized interface ClusteringAlgorithm 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

Nested Class Summary

Nested classes/interfaces inherited from interface elki.Algorithm

Algorithm.Utils

Method Summary

Modifier and Type	Method	Description
default C	autorun(Database database)	Try to auto-run the algorithm on a database by calling a method called run, with an optional Database first, and with data relations as specified by Algorithm.getInputTypeRestriction().

Methods inherited from interface elki.Algorithm

getInputTypeRestriction

Method Detail

autorun

default C autorun(Database database)

Description copied from interface: Algorithm

Try to auto-run the algorithm on a database by calling a method called run, with an optional Database first, and with data relations as specified by Algorithm.getInputTypeRestriction().

Specified by:

autorun in interface Algorithm

Parameters:

database - the database to run the algorithm on

Returns:

the Result computed by this algorithm