de.lmu.ifi.dbs.elki.algorithm

Interface Algorithm

All Known Subinterfaces:

Classifier<O>, ClusteringAlgorithm<C>, DistanceBasedAlgorithm<O>, HierarchicalClusteringAlgorithm, KMeans<V,M>, OPTICSTypeAlgorithm, OutlierAlgorithm, SubspaceClusteringAlgorithm<M>

All Known Implementing Classes:

ABOD, AbstractAggarwalYuOutlier, AbstractAlgorithm, AbstractBiclustering, AbstractClassifier, AbstractCutDendrogram, AbstractDBOutlier, AbstractDistanceBasedAlgorithm, AbstractDistanceBasedSpatialOutlier, AbstractFrequentItemsetAlgorithm, AbstractHDBSCAN, AbstractKMeans, AbstractNeighborhoodOutlier, AbstractNumberVectorDistanceBasedAlgorithm, AbstractOPTICS, AbstractPrimitiveDistanceBasedAlgorithm, AbstractProjectedClustering, AbstractProjectionAlgorithm, AddSingleScale, AddUniformScale, AffinityPropagationClusteringAlgorithm, AggarwalYuEvolutionary, AggarwalYuNaive, AGNES, ALOCI, AnderbergHierarchicalClustering, APRIORI, AssociationRuleGeneration, AveragePrecisionAtK, BarnesHutTSNE, BestOfMultipleKMeans, BIRCHLeafClustering, ByLabelClustering, ByLabelHierarchicalClustering, ByLabelOrAllInOneClustering, ByLabelOutlier, ByModelClustering, CanopyPreClustering, CASH, CBLOF, CenterOfMassMetaClustering, ChengAndChurch, CKMeans, CLARA, CLARANS, CLINK, CLIQUE, ClustersWithNoiseExtraction, COF, COP, COPAC, CTLuGLSBackwardSearchAlgorithm, CTLuMeanMultipleAttributes, CTLuMedianAlgorithm, CTLuMedianMultipleAttributes, CTLuMoranScatterplotOutlier, CTLuRandomWalkEC, CTLuScatterplotOutlier, CTLuZTestOutlier, CutDendrogramByHeight, CutDendrogramByHeightExtractor.DummyHierarchicalClusteringAlgorithm, CutDendrogramByNumberOfClusters, DBOutlierDetection, DBOutlierScore, DBSCAN, DeLiClu, DependencyDerivator, DiSH, DistanceQuantileSampler, DistanceStatisticsWithClasses, DistanceStddevOutlier, DOC, DummyAlgorithm, DWOF, Eclat, EM, EMOutlier, ERiC, EstimateIntrinsicDimensionality, EvaluateRankingQuality, EvaluateRetrievalPerformance, ExternalClustering, ExternalDoubleOutlierScore, FastABOD, FastCLARA, FastCLARANS, FastDOC, FastOPTICS, FDBSCAN, FeatureBagging, FlexibleLOF, FourC, FPGrowth, GaussianModel, GaussianUniformMixture, GeneralizedDBSCAN, GeneralizedOPTICS, GriDBSCAN, HDBSCANHierarchyExtraction, HDBSCANLinearMemory, HiCO, HiCS, HilOut, HiSC, HopkinsStatisticClusteringTendency, IDOS, INFLO, IntrinsicDimensionalityOutlier, ISOS, KDEOS, KMeansAnnulus, KMeansBisecting, KMeansCompare, KMeansElkan, KMeansExponion, KMeansHamerly, KMeansLloyd, KMeansMacQueen, KMeansMinusMinus, KMeansOutlierDetection, KMeansSimplifiedElkan, KMeansSort, KMediansLloyd, KMedoidsFastPAM, KMedoidsFastPAM1, KMedoidsPAM, KMedoidsPAMReynolds, KMedoidsPark, KNNBenchmarkAlgorithm, KNNClassifier, KNNDD, KNNDistancesSampler, KNNJoin, KNNKernelDensityMinimaClustering, KNNOutlier, KNNSOS, KNNWeightOutlier, LBABOD, LDF, LDOF, Leader, LibSVMOneClassOutlierDetection, LMCLUS, LocalIsolationCoefficient, LOCI, LOF, LoOP, LSDBC, MiniMax, MiniMaxAnderberg, MiniMaxNNChain, NaiveAgglomerativeHierarchicalClustering1, NaiveAgglomerativeHierarchicalClustering2, NaiveAgglomerativeHierarchicalClustering3, NaiveAgglomerativeHierarchicalClustering4, NaiveMeanShiftClustering, NNChain, NullAlgorithm, ODIN, ODIN, OfflineChangePointDetectionAlgorithm, OnlineLOF, OPTICSHeap, OPTICSList, OPTICSOF, OPTICSXi, ORCLUS, OutRankS1, OUTRES, P3C, ParallelGeneralizedDBSCAN, ParallelKNNOutlier, ParallelKNNWeightOutlier, ParallelLloydKMeans, ParallelLOF, ParallelSimplifiedLOF, PreDeCon, PriorProbabilityClassifier, PROCLUS, RangeQueryBenchmarkAlgorithm, RangeQuerySelectivity, RankingQualityHistogram, ReferenceBasedOutlierDetection, RepresentativeUncertainClustering, RescaleMetaOutlierAlgorithm, SameSizeKMeansAlgorithm, SigniTrendChangeDetection, SilhouetteOutlierDetection, SimpleCOP, SimpleKernelDensityLOF, SimpleOutlierEnsemble, SimplifiedHierarchyExtraction, SimplifiedLOF, SingleAssignmentKMeans, SLINK, SLINKHDBSCANLinearMemory, SLOM, SNE, SNNClustering, SOD, SOF, SOS, SUBCLU, TrimmedMeanApproach, TrivialAllInOne, TrivialAllNoise, TrivialAllOutlier, TrivialAverageCoordinateOutlier, TrivialGeneratedOutlier, TrivialNoOutlier, TSNE, UKMeans, ValidateApproximativeKNNIndex, VarianceOfVolume, XMeans

public interface Algorithm

Specifies the requirements for any algorithm that is to be executable by the main class.

Any implementation needs not to take care of input nor output, parsing and so on. Those tasks are performed by the framework. An algorithm simply needs to ask for parameters that are algorithm specific.

Note: Any implementation is supposed to provide a constructor without parameters (default constructor).

Since:

0.1

Author:

Arthur Zimek

Method Summary

Modifier and Type	Method and Description
TypeInformation[]	getInputTypeRestriction() Get the input type restriction used for negotiating the data query.
Result	run(Database database) Runs the algorithm.

Method Detail

run

Result run(Database database)

Runs the algorithm.

Parameters:

database - the database to run the algorithm on

Returns:

the Result computed by this algorithm

getInputTypeRestriction

TypeInformation[] getInputTypeRestriction()

Get the input type restriction used for negotiating the data query.

Returns:

Type restriction