de.lmu.ifi.dbs.elki.algorithm.clustering

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

Type Parameters:

C - Clustering type

All Superinterfaces:

Algorithm

All Known Subinterfaces:

KMeans<V,M>, SubspaceClusteringAlgorithm<M>

All Known Implementing Classes:

AbstractBiclustering, AbstractCutDendrogram, AbstractKMeans, AbstractProjectedClustering, AffinityPropagationClusteringAlgorithm, BestOfMultipleKMeans, BIRCHLeafClustering, ByLabelClustering, ByLabelHierarchicalClustering, ByLabelOrAllInOneClustering, ByModelClustering, CanopyPreClustering, CASH, CenterOfMassMetaClustering, ChengAndChurch, CKMeans, CLARA, CLARANS, CLIQUE, ClustersWithNoiseExtraction, COPAC, CutDendrogramByHeight, CutDendrogramByNumberOfClusters, DBSCAN, DiSH, DOC, EM, ERiC, ExternalClustering, FastCLARA, FastCLARANS, FastDOC, FDBSCAN, FourC, GeneralizedDBSCAN, GriDBSCAN, HDBSCANHierarchyExtraction, KMeansAnnulus, KMeansBisecting, KMeansCompare, KMeansElkan, KMeansExponion, KMeansHamerly, KMeansLloyd, KMeansMacQueen, KMeansMinusMinus, KMeansSimplifiedElkan, KMeansSort, KMediansLloyd, KMedoidsFastPAM, KMedoidsFastPAM1, KMedoidsPAM, KMedoidsPAMReynolds, KMedoidsPark, KNNKernelDensityMinimaClustering, Leader, LMCLUS, LSDBC, NaiveMeanShiftClustering, OPTICSXi, ORCLUS, P3C, ParallelGeneralizedDBSCAN, ParallelLloydKMeans, PreDeCon, PROCLUS, RepresentativeUncertainClustering, SameSizeKMeansAlgorithm, SimplifiedHierarchyExtraction, SingleAssignmentKMeans, SNNClustering, SUBCLU, TrivialAllInOne, TrivialAllNoise, UKMeans, XMeans

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

Method Summary

Modifier and Type	Method and Description
C	run(Database database) Runs the algorithm.

Methods inherited from interface de.lmu.ifi.dbs.elki.algorithm.Algorithm

getInputTypeRestriction

Method Detail

run

C run(Database database)

Description copied from interface: Algorithm

Runs the algorithm.

Specified by:

run in interface Algorithm

Parameters:

database - the database to run the algorithm on

Returns:

the Result computed by this algorithm