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

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

Type Parameters:

C - Clustering type

All Superinterfaces:

Algorithm, InspectionUtilFrequentlyScanned, Parameterizable

All Known Subinterfaces:

SubspaceClusteringAlgorithm<M>

All Known Implementing Classes:

AbstractKMeans, AbstractProjectedClustering, AbstractProjectedDBSCAN, ByLabelClustering, ByLabelHierarchicalClustering, ByLabelOrAllInOneClustering, ByModelClustering, CASH, CLIQUE, COPAC, DBSCAN, DiSH, EM, ERiC, FourC, GeneralizedDBSCAN, KMeansLloyd, KMeansMacQueen, KMediansLloyd, KMedoidsEM, KMedoidsPAM, NaiveMeanShiftClustering, OPTICSXi, ORCLUS, PreDeCon, PROCLUS, SameSizeKMeansAlgorithm, SNNClustering, SUBCLU, TrivialAllInOne, TrivialAllNoise

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.

Method Summary

Methods

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