Package elki

Interface Algorithm

All Known Subinterfaces:

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

All Known Implementing Classes:

ABOD, AbstractAggarwalYuOutlier, AbstractBiclustering, AbstractClassifier, AbstractCutDendrogram, AbstractDBOutlier, AbstractDistanceBasedSpatialOutlier, AbstractFrequentItemsetAlgorithm, AbstractHDBSCAN, AbstractKMeans, AbstractNeighborhoodOutlier, AbstractOPTICS, AbstractProjectedClustering, AbstractProjectionAlgorithm, AddSingleScale, AddUniformScale, AffinityPropagation, AggarwalYuEvolutionary, AggarwalYuNaive, AGNES, ALOCI, AlternatingKMedoids, Anderberg, AnnulusKMeans, APRIORI, AssociationRuleGeneration, AveragePrecisionAtK, BarnesHutTSNE, BestOfMultipleKMeans, BetulaGMM, BetulaGMMWeighted, BetulaLeafPreClustering, BetulaLloydKMeans, BIRCHLeafClustering, BIRCHLloydKMeans, BisectingKMeans, ByLabelClustering, ByLabelHierarchicalClustering, ByLabelOrAllInOneClustering, ByLabelOutlier, ByModelClustering, CanopyPreClustering, CASH, CBLOF, CenterOfMassMetaClustering, CFSFDP, CFSFDP, ChengAndChurch, CKMeans, CLARA, CLARANS, CLINK, CLIQUE, ClustersWithNoiseExtraction, COF, CompareMeans, COP, COPAC, CTLuGLSBackwardSearchAlgorithm, CTLuMeanMultipleAttributes, CTLuMedianAlgorithm, CTLuMedianMultipleAttributes, CTLuMoranScatterplotOutlier, CTLuRandomWalkEC, CTLuScatterplotOutlier, CTLuZTestOutlier, CutDendrogramByHeight, CutDendrogramByNumberOfClusters, DBOutlierDetection, DBOutlierScore, DBSCAN, DBSCANOutlierDetection, DeLiClu, DependencyDerivator, DiSH, DistanceQuantileSampler, DistanceStatisticsWithClasses, DistanceStddevOutlier, DOC, DWOF, EagerPAM, Eclat, ElkanKMeans, EM, EMOutlier, ERiC, EuclideanSphericalElkanKMeans, EuclideanSphericalHamerlyKMeans, EuclideanSphericalSimplifiedElkanKMeans, EvaluateRankingQuality, EvaluateRetrievalPerformance, ExponionKMeans, ExternalClustering, ExternalDoubleOutlierScore, FastABOD, FastCLARA, FastCLARANS, FastDOC, FasterCLARA, FasterMSC, FasterPAM, FastMSC, FastOPTICS, FastPAM, FastPAM1, FDBSCAN, FeatureBagging, FlexibleLOF, FourC, FPGrowth, FuzzyCMeans, GaussianModel, GaussianUniformMixture, GeneralizedDBSCAN, GLOSH, GMeans, GreedyKCenter, GriDBSCAN, HACAM, HamerlyKMeans, HartiganWongKMeans, HDBSCANHierarchyExtraction, HDBSCANLinearMemory, HiCO, HiCS, HilOut, HiSC, HopkinsStatisticClusteringTendency, HySortOD, IDOS, INFLO, IsolationForest, ISOS, KDEOS, KDTreeEM, KDTreeFilteringKMeans, KDTreePruningKMeans, KMeansMinusMinus, KMeansMinusMinusOutlierDetection, KMeansOutlierDetection, KMediansLloyd, KNNClassifier, KNNDD, KNNDistancesSampler, KNNJoin, KNNKernelDensityMinimaClustering, KNNOutlier, KNNSOS, KNNWeightOutlier, LBABOD, LDF, LDOF, Leader, LibSVMOneClassOutlierDetection, LID, LinearMemoryNNChain, LloydKMeans, LMCLUS, LocalIsolationCoefficient, LOCI, LOF, LoOP, LSDBC, MacQueenKMeans, MedoidLinkage, MiniMax, MiniMaxAnderberg, MiniMaxNNChain, NaiveAgglomerativeHierarchicalClustering1, NaiveAgglomerativeHierarchicalClustering2, NaiveAgglomerativeHierarchicalClustering3, NaiveAgglomerativeHierarchicalClustering4, NaiveMeanShiftClustering, NNChain, NoiseAsOutliers, NullAlgorithm, OCSVM, ODIN, ODIN, OfflineChangePointDetectionAlgorithm, OnlineLOF, OPTICSHeap, OPTICSList, OPTICSOF, OPTICSToHierarchical, OPTICSXi, ORCLUS, OutRankS1, OUTRES, P3C, PAM, PAMMEDSIL, PAMSIL, ParallelGeneralizedDBSCAN, ParallelKNNOutlier, ParallelKNNWeightOutlier, ParallelLloydKMeans, ParallelLOF, ParallelSimplifiedLOF, PreDeCon, PriorProbabilityClassifier, PROCLUS, RankingQualityHistogram, ReferenceBasedOutlierDetection, RepresentativeUncertainClustering, RescaleMetaOutlierAlgorithm, ReynoldsPAM, SameSizeKMeans, ShallotKMeans, SigniTrendChangeDetection, SilhouetteOutlierDetection, SimpleCOP, SimpleKernelDensityLOF, SimpleOutlierEnsemble, SimplifiedElkanKMeans, SimplifiedHierarchyExtraction, SimplifiedLOF, SingleAssignmentKMeans, SingleAssignmentKMedoids, SLINK, SLINKHDBSCANLinearMemory, SLOM, SNE, SNNClustering, SOD, SOF, SortMeans, SOS, SphericalElkanKMeans, SphericalHamerlyKMeans, SphericalKMeans, SphericalSimplifiedElkanKMeans, SphericalSimplifiedHamerlyKMeans, SphericalSingleAssignmentKMeans, SUBCLU, SupportVectorClustering, SVDD, TrimmedMeanApproach, TrivialAllInOne, TrivialAllNoise, TrivialAllOutlier, TrivialAverageCoordinateOutlier, TrivialGeneratedOutlier, TrivialNoOutlier, TSNE, UKMeans, VarianceOfVolume, XMeans, YinYangKMeans

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.

Since:

0.1

Author:

Arthur Zimek

Nested Class Summary

Nested Classes

Modifier and Type	Interface	Description
static class	Algorithm.Utils	Shared functionality

Method Summary

Modifier and Type	Method	Description
default java.lang.Object	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 getInputTypeRestriction().
TypeInformation[]	getInputTypeRestriction()	Get the input type restriction used for negotiating the data query.

Method Detail

autorun

default java.lang.Object 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 getInputTypeRestriction().

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