A B C D E F G H I J K L M N O P Q R S T U V W X Y
All Classes All Packages

M

m - Variable in class elki.clustering.kmeans.FuzzyCMeans

Weight exponent

m - Variable in class elki.clustering.kmeans.FuzzyCMeans.Par

Weight exponent

m - Variable in class elki.clustering.kmeans.initialization.KMC2.Instance

Number of sampling attempts.

m - Variable in class elki.clustering.kmeans.initialization.KMC2

Number of sampling attempts.

m - Variable in class elki.clustering.kmeans.initialization.KMC2.Par

Number of sampling attempts.

m_i - Variable in class elki.clustering.subspace.PROCLUS

Multiplier for the initial number of medoids.

m_i - Variable in class elki.clustering.subspace.PROCLUS.Par

Multiplier for the initial number of medoids.

M_I_ID - Static variable in class elki.clustering.subspace.PROCLUS.Par

Parameter to specify the multiplier for the initial number of medoids, must be an integer greater than 0.

M_ID - Static variable in class elki.clustering.kmeans.FuzzyCMeans.Par

Parameter to set weight exponent

M_ID - Static variable in class elki.clustering.kmeans.initialization.KMC2.Par

Parameter m of the AFK-MC² method.

m1 - Variable in class elki.clustering.silhouette.FastMSC.Record

Nearest medoid

m2 - Variable in class elki.clustering.silhouette.FastMSC.Record

Second nearest medoid

m3 - Variable in class elki.clustering.silhouette.FastMSC.Record

Third nearest medoid

MacQueenKMeans<V extends elki.data.NumberVector> - Class in elki.clustering.kmeans

The original k-means algorithm, using MacQueen style incremental updates; making this effectively an "online" (streaming) algorithm.

MacQueenKMeans(NumberVectorDistance<? super V>, int, int, KMeansInitialization) - Constructor for class elki.clustering.kmeans.MacQueenKMeans

Constructor.

MacQueenKMeans.Instance - Class in elki.clustering.kmeans

Inner instance, storing state for a single data set.

MacQueenKMeans.Par<V extends elki.data.NumberVector> - Class in elki.clustering.kmeans

Parameterization class.

mahalanobisDistance(double[]) - Method in class elki.clustering.em.models.DiagonalGaussianModel

Compute the Mahalanobis distance from the centroid for a given vector.

mahalanobisDistance(double[]) - Method in class elki.clustering.em.models.MultivariateGaussianModel

Compute the Mahalanobis distance from the centroid for a given vector.

mahalanobisDistance(double[]) - Method in class elki.clustering.em.models.SphericalGaussianModel

Compute the Mahalanobis distance from the centroid for a given vector.

mahalanobisDistance(double[]) - Method in class elki.clustering.em.models.TextbookSphericalGaussianModel

Compute the Mahalanobis distance from the centroid for a given vector.

mahalanobisDistance(NumberVector) - Method in class elki.clustering.em.models.DiagonalGaussianModel

Compute the Mahalanobis distance from the centroid for a given vector.

mahalanobisDistance(NumberVector) - Method in class elki.clustering.em.models.MultivariateGaussianModel

Compute the Mahalanobis distance from the centroid for a given vector.

mahalanobisDistance(NumberVector) - Method in class elki.clustering.em.models.SphericalGaussianModel

Compute the Mahalanobis distance from the centroid for a given vector.

mahalanobisDistance(NumberVector) - Method in class elki.clustering.em.models.TextbookMultivariateGaussianModel

Compute the Mahalanobis distance from the centroid for a given vector.

mahalanobisDistance(NumberVector) - Method in class elki.clustering.em.models.TextbookSphericalGaussianModel

Compute the Mahalanobis distance from the centroid for a given vector.

mahalanobisDistance(NumberVector) - Method in class elki.clustering.em.models.TwoPassMultivariateGaussianModel

Compute the Mahalanobis distance from the centroid for a given vector.

make() - Method in class elki.clustering.BetulaLeafPreClustering.Par

make() - Method in class elki.clustering.CFSFDP.Par

make() - Method in class elki.clustering.correlation.COPAC.Par

make() - Method in class elki.clustering.correlation.ERiC.Par

make() - Method in class elki.clustering.correlation.FourC.Par

make() - Method in class elki.clustering.correlation.FourC.Settings.Par

make() - Method in class elki.clustering.correlation.HiCO.Par

make() - Method in class elki.clustering.correlation.LMCLUS.Par

make() - Method in class elki.clustering.correlation.ORCLUS.Par

make() - Method in class elki.clustering.dbscan.DBSCAN.Par

make() - Method in class elki.clustering.dbscan.GeneralizedDBSCAN.Par

make() - Method in class elki.clustering.dbscan.LSDBC.Par

make() - Method in class elki.clustering.dbscan.parallel.ParallelGeneralizedDBSCAN.Par

make() - Method in class elki.clustering.dbscan.predicates.COPACNeighborPredicate.Par

make() - Method in class elki.clustering.dbscan.predicates.ERiCNeighborPredicate.Par

make() - Method in class elki.clustering.dbscan.predicates.FourCCorePredicate.Par

make() - Method in class elki.clustering.dbscan.predicates.FourCNeighborPredicate.Par

make() - Method in class elki.clustering.dbscan.predicates.MinPtsCorePredicate.Par

make() - Method in class elki.clustering.dbscan.predicates.PreDeConCorePredicate.Par

make() - Method in class elki.clustering.dbscan.predicates.PreDeConNeighborPredicate.Par

make() - Method in class elki.clustering.em.BetulaGMM.Par

make() - Method in class elki.clustering.em.BetulaGMMWeighted.Par

make() - Method in class elki.clustering.em.EM.Par

make() - Method in class elki.clustering.em.KDTreeEM.Par

make() - Method in class elki.clustering.em.models.BetulaDiagonalGaussianModelFactory.Par

make() - Method in class elki.clustering.em.models.BetulaMultivariateGaussianModelFactory.Par

make() - Method in class elki.clustering.em.models.BetulaSphericalGaussianModelFactory.Par

make() - Method in class elki.clustering.hierarchical.birch.AverageInterclusterDistance.Par

make() - Method in class elki.clustering.hierarchical.birch.AverageIntraclusterDistance.Par

make() - Method in class elki.clustering.hierarchical.birch.BIRCHKMeansPlusPlus.Par

make() - Method in class elki.clustering.hierarchical.birch.BIRCHLeafClustering.Par

make() - Method in class elki.clustering.hierarchical.birch.BIRCHLloydKMeans.Par

make() - Method in class elki.clustering.hierarchical.birch.CentroidEuclideanDistance.Par

make() - Method in class elki.clustering.hierarchical.birch.CentroidManhattanDistance.Par

make() - Method in class elki.clustering.hierarchical.birch.CFTree.Factory.Par

make() - Method in class elki.clustering.hierarchical.birch.DiameterCriterion.Par

make() - Method in class elki.clustering.hierarchical.birch.RadiusCriterion.Par

make() - Method in class elki.clustering.hierarchical.birch.VarianceIncreaseDistance.Par

make() - Method in class elki.clustering.hierarchical.CLINK.Par

make() - Method in class elki.clustering.hierarchical.extraction.ClustersWithNoiseExtraction.Par

make() - Method in class elki.clustering.hierarchical.extraction.CutDendrogramByHeight.Par

make() - Method in class elki.clustering.hierarchical.extraction.CutDendrogramByNumberOfClusters.Par

make() - Method in class elki.clustering.hierarchical.extraction.HDBSCANHierarchyExtraction.Par

make() - Method in class elki.clustering.hierarchical.extraction.SimplifiedHierarchyExtraction.Par

make() - Method in class elki.clustering.hierarchical.linkage.CentroidLinkage.Par

make() - Method in class elki.clustering.hierarchical.linkage.CompleteLinkage.Par

make() - Method in class elki.clustering.hierarchical.linkage.FlexibleBetaLinkage.Par

make() - Method in class elki.clustering.hierarchical.linkage.GroupAverageLinkage.Par

make() - Method in class elki.clustering.hierarchical.linkage.MedianLinkage.Par

make() - Method in class elki.clustering.hierarchical.linkage.MinimumVarianceLinkage.Par

make() - Method in class elki.clustering.hierarchical.linkage.SingleLinkage.Par

make() - Method in class elki.clustering.hierarchical.linkage.WardLinkage.Par

make() - Method in class elki.clustering.hierarchical.linkage.WeightedAverageLinkage.Par

make() - Method in class elki.clustering.hierarchical.OPTICSToHierarchical.Par

make() - Method in class elki.clustering.hierarchical.SLINK.Par

make() - Method in class elki.clustering.kcenter.GreedyKCenter.Par

make() - Method in class elki.clustering.kmeans.AbstractKMeans.Par

make() - Method in class elki.clustering.kmeans.AnnulusKMeans.Par

make() - Method in class elki.clustering.kmeans.BetulaLloydKMeans.Par

make() - Method in class elki.clustering.kmeans.CompareMeans.Par

make() - Method in class elki.clustering.kmeans.ElkanKMeans.Par

make() - Method in class elki.clustering.kmeans.ExponionKMeans.Par

make() - Method in class elki.clustering.kmeans.FuzzyCMeans.Par

make() - Method in class elki.clustering.kmeans.GMeans.Par

make() - Method in class elki.clustering.kmeans.HamerlyKMeans.Par

make() - Method in class elki.clustering.kmeans.HartiganWongKMeans.Parameterizer

make() - Method in class elki.clustering.kmeans.initialization.AFKMC2.Par

make() - Method in class elki.clustering.kmeans.initialization.betula.CFKPlusPlusLeaves.Par

make() - Method in class elki.clustering.kmeans.initialization.betula.CFKPlusPlusTree.Par

make() - Method in class elki.clustering.kmeans.initialization.betula.CFKPlusPlusTrunk.Par

make() - Method in class elki.clustering.kmeans.initialization.betula.CFRandomlyChosen.Par

make() - Method in class elki.clustering.kmeans.initialization.betula.CFWeightedRandomlyChosen.Par

make() - Method in class elki.clustering.kmeans.initialization.FarthestPoints.Par

make() - Method in class elki.clustering.kmeans.initialization.FarthestSumPoints.Par

make() - Method in class elki.clustering.kmeans.initialization.FirstK.Par

make() - Method in class elki.clustering.kmeans.initialization.KMC2.Par

make() - Method in class elki.clustering.kmeans.initialization.KMeansPlusPlus.Par

make() - Method in class elki.clustering.kmeans.initialization.Ostrovsky.Par

make() - Method in class elki.clustering.kmeans.initialization.Predefined.Par

make() - Method in class elki.clustering.kmeans.initialization.RandomlyChosen.Par

make() - Method in class elki.clustering.kmeans.initialization.RandomNormalGenerated.Par

make() - Method in class elki.clustering.kmeans.initialization.RandomUniformGenerated.Par

make() - Method in class elki.clustering.kmeans.initialization.SphericalAFKMC2.Par

make() - Method in class elki.clustering.kmeans.initialization.SphericalKMeansPlusPlus.Par

make() - Method in class elki.clustering.kmeans.KDTreeFilteringKMeans.Par

make() - Method in class elki.clustering.kmeans.KDTreePruningKMeans.Par

make() - Method in class elki.clustering.kmeans.KMeansMinusMinus.Par

make() - Method in class elki.clustering.kmeans.KMediansLloyd.Par

make() - Method in class elki.clustering.kmeans.LloydKMeans.Par

make() - Method in class elki.clustering.kmeans.MacQueenKMeans.Par

make() - Method in class elki.clustering.kmeans.ShallotKMeans.Par

make() - Method in class elki.clustering.kmeans.SimplifiedElkanKMeans.Par

make() - Method in class elki.clustering.kmeans.SingleAssignmentKMeans.Par

make() - Method in class elki.clustering.kmeans.SortMeans.Par

make() - Method in class elki.clustering.kmeans.spherical.EuclideanSphericalElkanKMeans.Par

make() - Method in class elki.clustering.kmeans.spherical.EuclideanSphericalHamerlyKMeans.Par

make() - Method in class elki.clustering.kmeans.spherical.EuclideanSphericalSimplifiedElkanKMeans.Par

make() - Method in class elki.clustering.kmeans.spherical.SphericalElkanKMeans.Par

make() - Method in class elki.clustering.kmeans.spherical.SphericalHamerlyKMeans.Par

make() - Method in class elki.clustering.kmeans.spherical.SphericalKMeans.Par

make() - Method in class elki.clustering.kmeans.spherical.SphericalSimplifiedElkanKMeans.Par

make() - Method in class elki.clustering.kmeans.spherical.SphericalSimplifiedHamerlyKMeans.Par

make() - Method in class elki.clustering.kmeans.spherical.SphericalSingleAssignmentKMeans.Par

make() - Method in class elki.clustering.kmeans.YinYangKMeans.Par

make() - Method in class elki.clustering.kmedoids.AlternatingKMedoids.Par

make() - Method in class elki.clustering.kmedoids.CLARA.Par

make() - Method in class elki.clustering.kmedoids.CLARANS.Par

make() - Method in class elki.clustering.kmedoids.EagerPAM.Par

make() - Method in class elki.clustering.kmedoids.FastCLARA.Par

make() - Method in class elki.clustering.kmedoids.FastCLARANS.Par

make() - Method in class elki.clustering.kmedoids.FasterCLARA.Par

make() - Method in class elki.clustering.kmedoids.FasterPAM.Par

make() - Method in class elki.clustering.kmedoids.FastPAM.Par

make() - Method in class elki.clustering.kmedoids.FastPAM1.Par

make() - Method in class elki.clustering.kmedoids.initialization.AlternateRefinement.Par

make() - Method in class elki.clustering.kmedoids.initialization.BUILD.Par

make() - Method in class elki.clustering.kmedoids.initialization.GreedyG.Par

make() - Method in class elki.clustering.kmedoids.initialization.KMedoidsKMedoidsInitialization.Par

make() - Method in class elki.clustering.kmedoids.initialization.LAB.Par

make() - Method in class elki.clustering.kmedoids.initialization.ParkJun.Par

make() - Method in class elki.clustering.kmedoids.PAM.Par

make() - Method in class elki.clustering.kmedoids.ReynoldsPAM.Par

make() - Method in class elki.clustering.kmedoids.SingleAssignmentKMedoids.Par

make() - Method in class elki.clustering.meta.ExternalClustering.Par

make() - Method in class elki.clustering.onedimensional.KNNKernelDensityMinimaClustering.Par

make() - Method in class elki.clustering.optics.OPTICSHeap.Par

make() - Method in class elki.clustering.optics.OPTICSList.Par

make() - Method in class elki.clustering.optics.OPTICSXi.Par

make() - Method in class elki.clustering.silhouette.FasterMSC.Par

make() - Method in class elki.clustering.silhouette.FastMSC.Par

make() - Method in class elki.clustering.silhouette.PAMMEDSIL.Par

make() - Method in class elki.clustering.silhouette.PAMSIL.Par

make() - Method in class elki.clustering.subspace.CLIQUE.Par

make() - Method in class elki.clustering.subspace.DOC.Par

make() - Method in class elki.clustering.subspace.FastDOC.Par

make() - Method in class elki.clustering.subspace.HiSC.Par

make() - Method in class elki.clustering.subspace.P3C.Par

make() - Method in class elki.clustering.subspace.PreDeCon.Par

make() - Method in class elki.clustering.subspace.PreDeCon.Settings.Par

make() - Method in class elki.clustering.subspace.PROCLUS.Par

make() - Method in class elki.clustering.subspace.SUBCLU.Par

make() - Method in class elki.clustering.trivial.ByLabelClustering.Par

make() - Method in class elki.datasource.parser.ClusteringVectorParser.Par

make() - Method in class elki.evaluation.clustering.EvaluateClustering.Par

make() - Method in class elki.evaluation.clustering.extractor.CutDendrogramByHeightExtractor.Par

make() - Method in class elki.evaluation.clustering.extractor.CutDendrogramByNumberOfClustersExtractor.Par

make() - Method in class elki.evaluation.clustering.extractor.HDBSCANHierarchyExtractionEvaluator.Par

make() - Method in class elki.evaluation.clustering.extractor.SimplifiedHierarchyExtractionEvaluator.Par

make() - Method in class elki.evaluation.clustering.internal.CIndex.Par

make() - Method in class elki.evaluation.clustering.internal.ClusterRadius.Par

make() - Method in class elki.evaluation.clustering.internal.ConcordantPairsGammaTau.Par

make() - Method in class elki.evaluation.clustering.internal.DaviesBouldinIndex.Par

make() - Method in class elki.evaluation.clustering.internal.DBCV.Par

make() - Method in class elki.evaluation.clustering.internal.PBMIndex.Par

make() - Method in class elki.evaluation.clustering.internal.Silhouette.Par

make() - Method in class elki.evaluation.clustering.internal.SimplifiedSilhouette.Par

make() - Method in class elki.evaluation.clustering.internal.SquaredErrors.Par

make() - Method in class elki.evaluation.clustering.internal.VarianceRatioCriterion.Par

make() - Method in class elki.index.preprocessed.fastoptics.RandomProjectedNeighborsAndDensities.Par

make() - Method in class elki.index.tree.betula.CFTree.Factory.Par

make() - Method in class elki.index.tree.betula.distance.AverageInterclusterDistance.Par

make() - Method in class elki.index.tree.betula.distance.AverageIntraclusterDistance.Par

make() - Method in class elki.index.tree.betula.distance.BIRCHAverageInterclusterDistance.Par

make() - Method in class elki.index.tree.betula.distance.BIRCHAverageIntraclusterDistance.Par

make() - Method in class elki.index.tree.betula.distance.BIRCHRadiusDistance.Par

make() - Method in class elki.index.tree.betula.distance.BIRCHVarianceIncreaseDistance.Par

make() - Method in class elki.index.tree.betula.distance.CentroidEuclideanDistance.Par

make() - Method in class elki.index.tree.betula.distance.CentroidManhattanDistance.Par

make() - Method in class elki.index.tree.betula.distance.RadiusDistance.Par

make() - Method in class elki.index.tree.betula.distance.VarianceIncreaseDistance.Par

make() - Method in class elki.index.tree.betula.features.BIRCHCF.Factory.Par

make() - Method in class elki.index.tree.betula.features.VIIFeature.Factory.Par

make() - Method in class elki.index.tree.betula.features.VVIFeature.Factory.Par

make() - Method in class elki.index.tree.betula.features.VVVFeature.Factory.Par

make() - Method in class elki.result.ClusteringVectorDumper.Par

make() - Method in class elki.similarity.cluster.ClusteringAdjustedRandIndexSimilarity.Par

make() - Method in class elki.similarity.cluster.ClusteringBCubedF1Similarity.Par

make() - Method in class elki.similarity.cluster.ClusteringFowlkesMallowsSimilarity.Par

make() - Method in class elki.similarity.cluster.ClusteringRandIndexSimilarity.Par

make() - Method in class elki.similarity.cluster.ClusterIntersectionSimilarity.Par

make() - Method in class elki.similarity.cluster.ClusterJaccardSimilarity.Par

make(int) - Method in class elki.index.tree.betula.features.BIRCHCF.Factory

make(int) - Method in interface elki.index.tree.betula.features.ClusterFeature.Factory

Make a new clustering feature of the given dimensionality.

make(int) - Method in class elki.index.tree.betula.features.VIIFeature.Factory

make(int) - Method in class elki.index.tree.betula.features.VVIFeature.Factory

make(int) - Method in class elki.index.tree.betula.features.VVVFeature.Factory

makeCluster(int, double, DBIDs) - Method in class elki.clustering.hierarchical.extraction.SimplifiedHierarchyExtraction.Instance

Make the cluster for the given object

makeCluster(int, DBIDs) - Method in class elki.clustering.hierarchical.extraction.AbstractCutDendrogram.Instance

Make the cluster for the given object

makeCluster(Relation<? extends NumberVector>, DBIDs, long[]) - Method in class elki.clustering.subspace.DOC

Utility method to create a subspace cluster from a list of DBIDs and the relevant attributes.

makeClusterMap(ArrayDBIDs, int[]) - Method in class elki.clustering.affinitypropagation.AffinityPropagation

Build an int to DBIDs lookup for the clusters.

makeOrUpdateSegment(int[], DBIDs, int) - Method in class elki.evaluation.clustering.pairsegments.Segments

makeStats(KDTreeEM.KDTree, int[], WritableDataStore<double[]>) - Method in class elki.clustering.em.KDTreeEM

Calculates the statistics on the kd-tree needed for the calculation of the new models

manhattanSegmentalDistance(NumberVector, double[], long[]) - Method in class elki.clustering.subspace.PROCLUS

Returns the Manhattan segmental distance between o1 and o2 relative to the specified dimensions.

manhattanSegmentalDistance(NumberVector, NumberVector, long[]) - Method in class elki.clustering.subspace.PROCLUS

Returns the Manhattan segmental distance between o1 and o2 relative to the specified dimensions.

map(DBIDRef) - Method in class elki.clustering.dbscan.parallel.ParallelGeneralizedDBSCAN.Instance.Mapper

map(DBIDRef) - Method in class elki.clustering.kmeans.parallel.KMeansProcessor.Instance

Mapper(NeighborPredicate.Instance<? extends T>) - Constructor for class elki.clustering.dbscan.parallel.ParallelGeneralizedDBSCAN.Instance.Mapper

Constructor.

markAsAssigned() - Method in class elki.clustering.subspace.clique.CLIQUEUnit

Marks this unit as assigned to a cluster.

maskMatrix(double[][], Distribution, Random) - Method in class elki.clustering.biclustering.ChengAndChurch.BiclusterCandidate

Updates the mask with replacement values for all data in the given rows and columns.

mat - Variable in class elki.clustering.hierarchical.AGNES.Instance

Cluster distance matrix

matrix - Variable in class elki.clustering.hierarchical.ClusterDistanceMatrix

Distance matrix (modifiable).

matrix - Variable in class elki.index.tree.betula.CFDistanceMatrix

Distance matrix (serialized)

matSelfInit(ClusterFeature) - Method in class elki.index.tree.betula.distance.AverageIntraclusterDistance

matSelfInit(ClusterFeature) - Method in interface elki.index.tree.betula.distance.CFDistance

Initialization for self measure for new Combinatorial clustering Methods (Podani 1989)

matSelfInit(ClusterFeature) - Method in class elki.index.tree.betula.distance.RadiusDistance

MAX_EM_ITERATIONS_ID - Static variable in class elki.clustering.subspace.P3C.Par

Maximum number of iterations for the EM step.

maxClusterSize(int[][], int, int) - Static method in class elki.evaluation.clustering.Entropy

Get the maximum cluster size of a contingency table.

maxdepth - Variable in class elki.clustering.kmeans.initialization.betula.CFKPlusPlusTree

Maximum depth to choose at.

maxdim - Variable in class elki.clustering.correlation.LMCLUS.Par

Maximum dimensionality to search for

MAXDIM_ID - Static variable in class elki.clustering.correlation.LMCLUS.Par

Parameter with the maximum dimension to search for

maxEmIterations - Variable in class elki.clustering.subspace.P3C

Maximum number of iterations for the EM step.

maxEmIterations - Variable in class elki.clustering.subspace.P3C.Par

Maximum number of iterations for the EM step.

maximum - Variable in class elki.clustering.optics.OPTICSXi.SteepArea

Maximum value

MaximumMatchingAccuracy - Class in elki.evaluation.clustering

Calculates the accuracy of a clustering based on the maximum set matching found by the Hungarian algorithm.

MaximumMatchingAccuracy(ClusterContingencyTable) - Constructor for class elki.evaluation.clustering.MaximumMatchingAccuracy

Calculate the maximum matching accuracy.

maxiter - Variable in class elki.clustering.affinitypropagation.AffinityPropagation

Maximum number of iterations.

maxiter - Variable in class elki.clustering.em.BetulaGMM

Maximum number of iterations.

maxiter - Variable in class elki.clustering.em.BetulaGMM.Par

Maximum number of iterations.

maxiter - Variable in class elki.clustering.em.EM

Maximum number of iterations to allow

maxiter - Variable in class elki.clustering.em.EM.Par

Maximum number of iterations.

maxiter - Variable in class elki.clustering.em.KDTreeEM

maximum amount of iterations

maxiter - Variable in class elki.clustering.em.KDTreeEM.Par

Maximum number of iterations.

maxiter - Variable in class elki.clustering.hierarchical.birch.BIRCHLloydKMeans

Maximum number of iterations.

maxiter - Variable in class elki.clustering.hierarchical.birch.BIRCHLloydKMeans.Par

Maximum number of iterations.

maxiter - Variable in class elki.clustering.kmeans.AbstractKMeans

Maximum number of iterations

maxiter - Variable in class elki.clustering.kmeans.AbstractKMeans.Par

Maximum number of iterations.

maxiter - Variable in class elki.clustering.kmeans.FuzzyCMeans

Maximum number of iterations to allow

maxiter - Variable in class elki.clustering.kmeans.FuzzyCMeans.Par

Maximum number of iterations.

maxiter - Variable in class elki.clustering.kmedoids.AlternatingKMedoids

Maximum number of iterations.

maxiter - Variable in class elki.clustering.kmedoids.AlternatingKMedoids.Par

The maximum number of iterations

maxiter - Variable in class elki.clustering.kmedoids.initialization.AlternateRefinement

Maximum number of refinement iterations.

maxiter - Variable in class elki.clustering.kmedoids.initialization.AlternateRefinement.Par

Maximum number of refinement iterations.

maxiter - Variable in class elki.clustering.kmedoids.PAM

The maximum number of iterations.

maxiter - Variable in class elki.clustering.kmedoids.PAM.Par

The maximum number of iterations.

MAXITER - Static variable in class elki.clustering.NaiveMeanShiftClustering

Maximum number of iterations.

MAXITER_ID - Static variable in class elki.clustering.em.EM.Par

Parameter to specify the maximum number of iterations.

MAXITER_ID - Static variable in class elki.clustering.em.KDTreeEM.Par

Parameter to specify a maximum number of iterations

MAXITER_ID - Static variable in interface elki.clustering.kmeans.KMeans

Parameter to specify the number of clusters to find, must be an integer greater or equal to 0, where 0 means no limit.

MAXITER_P - Static variable in class elki.clustering.kmedoids.initialization.AlternateRefinement.Par

Maximum number of refinement steps.

maxleaves - Variable in class elki.clustering.hierarchical.birch.CFTree.Factory

Maximum number of leaves (absolute or relative)

maxleaves - Variable in class elki.clustering.hierarchical.birch.CFTree.Factory.Par

Maximum number of leaves (absolute or relative)

maxleaves - Variable in class elki.index.tree.betula.CFTree.Factory

Maximum number of leaves (absolute or relative)

maxleaves - Variable in class elki.index.tree.betula.CFTree.Factory.Par

Maximum number of leaves (absolute or relative)

maxleaves - Variable in class elki.index.tree.betula.CFTree

Maximum number of leaves allowed

MAXLEAVES_ID - Static variable in class elki.clustering.hierarchical.birch.CFTree.Factory.Par

Maximum number of leaves.

MAXLEAVES_ID - Static variable in class elki.index.tree.betula.CFTree.Factory.Par

Maximum number of leaves.

maxLMDim - Variable in class elki.clustering.correlation.LMCLUS

Maximum cluster dimensionality

maxneighbor - Variable in class elki.clustering.kmedoids.CLARANS

Sampling rate.

maxneighbor - Variable in class elki.clustering.kmedoids.CLARANS.Par

Maximum neighbors to explore.

maxNMI() - Method in class elki.evaluation.clustering.Entropy

Get the max-normalized mutual information.

mbw - Variable in class elki.clustering.em.KDTreeEM

minimum leaf size

mbw - Variable in class elki.clustering.em.KDTreeEM.Par

construction threshold

MBW_ID - Static variable in class elki.clustering.em.KDTreeEM.Par

Parameter to specify the termination criterion for kd-tree construction.

mean - Variable in class elki.clustering.em.models.DiagonalGaussianModel

Mean vector.

mean - Variable in class elki.clustering.em.models.MultivariateGaussianModel

Mean vector.

mean - Variable in class elki.clustering.em.models.SphericalGaussianModel

Mean vector.

mean - Variable in class elki.clustering.em.models.TextbookMultivariateGaussianModel

Mean vector.

mean - Variable in class elki.clustering.em.models.TextbookSphericalGaussianModel

Mean vector.

mean - Variable in class elki.clustering.em.models.TwoPassMultivariateGaussianModel

Mean vector.

mean - Variable in class elki.index.tree.betula.features.VIIFeature

mean

mean - Variable in class elki.index.tree.betula.features.VVIFeature

mean

mean - Variable in class elki.index.tree.betula.features.VVVFeature

mean

MEAN - elki.index.tree.betula.CFTree.Threshold

Split halfway between minimum and maximum.

MeanModel - Class in elki.data.model

Cluster model that stores a mean for the cluster.

MeanModel(double[]) - Constructor for class elki.data.model.MeanModel

Constructor with mean

means - Variable in class elki.clustering.kmeans.AbstractKMeans.Instance

Cluster means.

means - Variable in class elki.clustering.kmeans.parallel.KMeansProcessor.Instance

Current mean vectors.

means - Variable in class elki.clustering.kmeans.parallel.KMeansProcessor

Mean vectors.

means(int[], double[][], ClusteringFeature[], int[]) - Method in class elki.clustering.hierarchical.birch.BIRCHLloydKMeans

Calculate means of clusters.

means(int[], double[][], ArrayList<? extends ClusterFeature>, int[]) - Method in class elki.clustering.kmeans.BetulaLloydKMeans

Calculate means of clusters.

means(List<? extends DBIDs>, double[][], Relation<? extends NumberVector>) - Static method in class elki.clustering.kmeans.AbstractKMeans

Returns the mean vectors of the given clusters in the given database.

means(List<? extends DBIDs>, double[][], Relation<? extends NumberVector>) - Static method in class elki.clustering.kmeans.spherical.SphericalKMeans.Instance

Returns the mean vectors of the given clusters in the given database.

meansFromSums(double[][], double[][], double[][]) - Method in class elki.clustering.kmeans.AbstractKMeans.Instance

Compute means from cluster sums by averaging.

meansFromSums(double[][], double[][], double[][]) - Method in class elki.clustering.kmeans.spherical.SphericalKMeans.Instance

Compute means from cluster sums by adding and normalizing.

meansWithTreshhold(double) - Method in class elki.clustering.kmeans.KMeansMinusMinus.Instance

Returns the mean vectors of the given clusters in the given database.

MEANVISITOR - Variable in class elki.clustering.biclustering.ChengAndChurch.BiclusterCandidate

Visitor for updating the means.

MEDIAN - elki.clustering.kmeans.KDTreePruningKMeans.Split

K-d-tree typical median split that guarantees minimal height, but tends to produce larger cells.

MEDIAN - elki.index.tree.betula.CFTree.Threshold

Split using the median.

MedianLinkage - Class in elki.clustering.hierarchical.linkage

Median-linkage — weighted pair group method using centroids (WPGMC).

MedianLinkage() - Constructor for class elki.clustering.hierarchical.linkage.MedianLinkage

Deprecated.

use the static instance MedianLinkage.STATIC instead.

MedianLinkage.Par - Class in elki.clustering.hierarchical.linkage

Class parameterizer.

medians(List<? extends DBIDs>, double[][], Relation<? extends NumberVector>) - Method in class elki.clustering.kmeans.KMediansLloyd.Instance

Returns the median vectors of the given clusters in the given database.

MedoidLinkage<O> - Class in elki.clustering.hierarchical

Medoid linkage uses the distance of medoids as criterion.

MedoidLinkage(Distance<? super O>) - Constructor for class elki.clustering.hierarchical.MedoidLinkage

Constructor.

MedoidLinkage.Instance - Class in elki.clustering.hierarchical

Main worker instance of AGNES.

MedoidModel - Class in elki.data.model

Cluster model that stores a mean for the cluster.

MedoidModel(DBID) - Constructor for class elki.data.model.MedoidModel

Constructor with medoid

medoids - Variable in class elki.clustering.kmedoids.CLARANS.Assignment

Medoids

medoidsilhouette(IntegerDataStore, DBIDArrayIter) - Method in class elki.clustering.silhouette.PAMMEDSIL.Instance

Evaluate the average medoid Silhouette of the current cluster assignment

medoidsilhouette(IntegerDataStore, DBIDArrayIter, int, DBIDRef) - Method in class elki.clustering.silhouette.PAMMEDSIL.Instance

Evaluate the average medoid Silhouette of the current cluster assignment

MedoidsInstance(DBIDs, DistanceQuery<?>, RandomFactory) - Constructor for class elki.clustering.kmeans.initialization.KMeansPlusPlus.MedoidsInstance

members - Variable in class elki.clustering.hierarchical.extraction.HDBSCANHierarchyExtraction.TempCluster

New ids, not yet in child clusters.

merge(double[], int, double[], int) - Method in class elki.clustering.hierarchical.linkage.CentroidLinkage

merge(double[], int, double[], int) - Method in interface elki.clustering.hierarchical.linkage.GeometricLinkage

Merge the aggregated vectors.

merge(double[], int, double[], int) - Method in class elki.clustering.hierarchical.linkage.MedianLinkage

merge(double[], int, double[], int) - Method in class elki.clustering.hierarchical.linkage.WardLinkage

merge(double, int, int) - Method in class elki.clustering.hierarchical.AGNES.Instance

Execute the cluster merge.

merge(double, int, int) - Method in class elki.clustering.hierarchical.Anderberg.Instance

merge(int, double[][], ClusterMergeHistoryBuilder, int[], double, int, int) - Method in class elki.clustering.hierarchical.LinearMemoryNNChain.Instance

Execute the cluster merge.

merge(int, int) - Method in class elki.clustering.hierarchical.HACAM.Instance

Execute the cluster merge.

merge(int, int) - Method in class elki.clustering.hierarchical.MedoidLinkage.Instance

Execute the cluster merge.

merge(int, int) - Method in class elki.clustering.hierarchical.MiniMax.Instance

Merges two clusters given by x, y, their points with smallest IDs, and y to keep

merge(int, int) - Method in class elki.clustering.hierarchical.MiniMaxAnderberg.Instance

merge(Relation<? extends NumberVector>, List<ORCLUS.ORCLUSCluster>, int, int, IndefiniteProgress) - Method in class elki.clustering.correlation.ORCLUS

Reduces the number of seeds to k_new

MERGE_NOISE - elki.evaluation.clustering.internal.NoiseHandling

Merge all noise into a cluster

mergeClusterCores(int, ArrayList<P3C.Signature>) - Method in class elki.clustering.subspace.P3C

Merge 1-signatures into p-signatures.

mergeClusterInformation(ModifiableDBIDs, WritableIntegerDataStore, WritableDataStore<Assignment>) - Method in class elki.clustering.dbscan.GriDBSCAN.Instance

Merge cluster information.

mergecount - Variable in class elki.clustering.hierarchical.ClusterMergeHistoryBuilder

Merge counter (for merge ordering).

mergeDistance - Variable in class elki.clustering.hierarchical.ClusterMergeHistoryBuilder

Distance to the parent object.

merges - Variable in class elki.clustering.hierarchical.ClusterMergeHistory

Store merge order (two cluster references per merge).

merges - Variable in class elki.clustering.hierarchical.ClusterMergeHistoryBuilder

Store merge order.

merges - Variable in class elki.clustering.hierarchical.extraction.AbstractCutDendrogram.Instance

The hierarchical result to process.

merges - Variable in class elki.clustering.hierarchical.extraction.ClustersWithNoiseExtraction.Instance

The hierarchical result to process.

merges - Variable in class elki.clustering.hierarchical.extraction.HDBSCANHierarchyExtraction.Instance

The hierarchical result to process.

merges - Variable in class elki.clustering.hierarchical.extraction.SimplifiedHierarchyExtraction.Instance

The hierarchical result to process.

mergeSignatures(P3C.Signature, P3C.Signature, int) - Method in class elki.clustering.subspace.P3C

Generates a merged signature of this and another one, where the other signature must be a 1-signature.

mergeWith(Core) - Method in class elki.clustering.dbscan.util.Core

Merge two cores.

meta - Variable in class elki.datasource.parser.ClusteringVectorParser

Metadata.

mfactory - Variable in class elki.clustering.em.EM

Factory for producing the initial cluster model.

mfactory - Variable in class elki.clustering.em.EM.Par

Cluster model factory.

mfactory - Variable in class elki.clustering.em.KDTreeEM

Factory for producing the initial cluster model.

mfactory - Variable in class elki.clustering.em.KDTreeEM.Par

Cluster model factory.

mi - Variable in class elki.clustering.hierarchical.MedoidLinkage.Instance

Iterators into medoid array.

mib - Variable in class elki.clustering.optics.OPTICSXi.SteepDownArea

Maximum in-between value (updated in OPTICSXi.updateFilterSDASet(double, java.util.List<elki.clustering.optics.OPTICSXi.SteepDownArea>, double)).

mid - Variable in class elki.clustering.kmeans.KDTreePruningKMeans.KDNode

Midpoint

midpoint - Variable in class elki.clustering.em.KDTreeEM.KDTree

Middle point of bounding box

MIDPOINT - elki.clustering.kmeans.KDTreePruningKMeans.Split

Split halfway between minimum and maximum.

MIN_CLUSTER_SIZE_ID - Static variable in class elki.clustering.subspace.P3C.Par

Minimum cluster size for noise flagging.

MIN_COLUMN_REMOVE_THRESHOLD - Static variable in class elki.clustering.biclustering.ChengAndChurch

The minimum number of columns that the database must have so that a removal of columns is performed in ChengAndChurch.multipleNodeDeletion(double[][], elki.clustering.biclustering.ChengAndChurch.BiclusterCandidate).

MIN_LOGLIKELIHOOD - Static variable in class elki.clustering.em.BetulaGMM

Minimum loglikelihood to avoid -infinity.

MIN_LOGLIKELIHOOD - Static variable in class elki.clustering.em.EM

Minimum loglikelihood to avoid -infinity.

MIN_ROW_REMOVE_THRESHOLD - Static variable in class elki.clustering.biclustering.ChengAndChurch

The minimum number of rows that the database must have so that a removal of rows is performed in ChengAndChurch.multipleNodeDeletion(double[][], elki.clustering.biclustering.ChengAndChurch.BiclusterCandidate).

minClSize - Variable in class elki.clustering.hierarchical.extraction.ClustersWithNoiseExtraction

Minimum cluster size.

minClSize - Variable in class elki.clustering.hierarchical.extraction.ClustersWithNoiseExtraction.Par

Minimum cluster size.

minClSize - Variable in class elki.clustering.hierarchical.extraction.HDBSCANHierarchyExtraction

Minimum cluster size.

minClSize - Variable in class elki.clustering.hierarchical.extraction.HDBSCANHierarchyExtraction.Par

Minimum cluster size.

minClSize - Variable in class elki.clustering.hierarchical.extraction.SimplifiedHierarchyExtraction

Minimum cluster size.

minClSize - Variable in class elki.clustering.hierarchical.extraction.SimplifiedHierarchyExtraction.Par

Minimum cluster size.

minclusters - Variable in class elki.clustering.hierarchical.extraction.CutDendrogramByNumberOfClusters

Minimum number of clusters to extract

minclusters - Variable in class elki.clustering.hierarchical.extraction.CutDendrogramByNumberOfClusters.Par

Number of clusters to extract.

MINCLUSTERS_ID - Static variable in class elki.clustering.hierarchical.extraction.CutDendrogramByNumberOfClusters.Par

The minimum number of clusters to extract.

minClusterSize - Variable in class elki.clustering.subspace.P3C

Minimum cluster size for noise flagging.

minClusterSize - Variable in class elki.clustering.subspace.P3C.Par

Minimum cluster size for noise flagging.

MINCLUSTERSIZE_ID - Static variable in class elki.clustering.hierarchical.extraction.ClustersWithNoiseExtraction.Par

The minimum size of clusters to extract.

MINCLUSTERSIZE_ID - Static variable in class elki.clustering.hierarchical.extraction.HDBSCANHierarchyExtraction.Par

The minimum size of clusters to extract.

MINCLUSTERSIZE_ID - Static variable in class elki.clustering.hierarchical.extraction.SimplifiedHierarchyExtraction.Par

The minimum size of clusters to extract.

mindim - Variable in class elki.clustering.subspace.SUBCLU

Minimum dimensionality.

mindim - Variable in class elki.clustering.subspace.SUBCLU.Par

Minimum dimensionality.

MINDIM_ID - Static variable in class elki.clustering.subspace.SUBCLU.Par

Minimum dimensionality to generate clusters.

mindist(double[], double[], double[]) - Method in class elki.clustering.kmeans.KDTreePruningKMeans.Instance

Get the smallest maximum distance for pruning.

minHeap - Variable in class elki.clustering.kmeans.KMeansMinusMinus.Instance

Heap of the noise candidates.

MiniMax<O> - Class in elki.clustering.hierarchical

Minimax Linkage clustering.

MiniMax(Distance<? super O>) - Constructor for class elki.clustering.hierarchical.MiniMax

Constructor.

MiniMax.Instance - Class in elki.clustering.hierarchical

Main worker instance of MiniMax.

MiniMaxAnderberg<O> - Class in elki.clustering.hierarchical

This is a modification of the classic MiniMax algorithm for hierarchical clustering using a nearest-neighbor heuristic for acceleration.

MiniMaxAnderberg(Distance<? super O>) - Constructor for class elki.clustering.hierarchical.MiniMaxAnderberg

Constructor.

MiniMaxAnderberg.Instance - Class in elki.clustering.hierarchical

Main worker instance of MiniMax.

MiniMaxNNChain<O> - Class in elki.clustering.hierarchical

MiniMax hierarchical clustering using the NNchain algorithm.

MiniMaxNNChain(Distance<? super O>) - Constructor for class elki.clustering.hierarchical.MiniMaxNNChain

Constructor.

MiniMaxNNChain.Instance - Class in elki.clustering.hierarchical

Main worker instance of MiniMaxNNChain.

MINIMUM_SUM - elki.clustering.hierarchical.HACAM.Variant

Minimum sum variant

MINIMUM_SUM_INCREASE - elki.clustering.hierarchical.HACAM.Variant

Minimum sum increase variant

MinimumVarianceLinkage - Class in elki.clustering.hierarchical.linkage

Minimum increase in variance (MIVAR) linkage.

MinimumVarianceLinkage() - Constructor for class elki.clustering.hierarchical.linkage.MinimumVarianceLinkage

Deprecated.

use the static instance MinimumVarianceLinkage.STATIC instead.

MinimumVarianceLinkage.Par - Class in elki.clustering.hierarchical.linkage

Class parameterizer.

miniter - Variable in class elki.clustering.em.EM

Minimum number of iterations to do

miniter - Variable in class elki.clustering.em.EM.Par

Minimum number of iterations.

miniter - Variable in class elki.clustering.em.KDTreeEM

minimum amount of iterations

miniter - Variable in class elki.clustering.em.KDTreeEM.Par

Minimum number of iterations.

miniter - Variable in class elki.clustering.kmeans.FuzzyCMeans

Minimum number of iterations to do

miniter - Variable in class elki.clustering.kmeans.FuzzyCMeans.Par

Minimum number of iterations.

MINITER_ID - Static variable in class elki.clustering.em.EM.Par

Parameter to specify a minimum number of iterations.

MINITER_ID - Static variable in class elki.clustering.em.KDTreeEM.Par

Parameter to specify a minimum number of iterations

MINITER_ID - Static variable in class elki.clustering.kmeans.FuzzyCMeans.Par

Parameter to specify a minimum number of iterations

minNMI() - Method in class elki.evaluation.clustering.Entropy

Get the min-normalized mutual information.

minpts - Variable in class elki.clustering.correlation.COPAC.Settings

MinPts parameter.

minpts - Variable in class elki.clustering.correlation.ERiC.Settings

Minimum neighborhood size (density).

minpts - Variable in class elki.clustering.correlation.FourC.Settings

MinPts / mu parameter.

minpts - Variable in class elki.clustering.dbscan.DBSCAN

Holds the minimum cluster size.

minpts - Variable in class elki.clustering.dbscan.DBSCAN.Par

Holds the minimum cluster size.

minpts - Variable in class elki.clustering.dbscan.GriDBSCAN.Instance

Holds the minimum cluster size.

minpts - Variable in class elki.clustering.dbscan.GriDBSCAN

Holds the minimum cluster size.

minpts - Variable in class elki.clustering.dbscan.predicates.MinPtsCorePredicate.Instance

The minpts parameter.

minpts - Variable in class elki.clustering.dbscan.predicates.MinPtsCorePredicate

The minpts parameter.

minpts - Variable in class elki.clustering.dbscan.predicates.MinPtsCorePredicate.Par

Minpts value

minpts - Variable in class elki.clustering.optics.AbstractOPTICS

The density threshold, in number of points.

minpts - Variable in class elki.clustering.SNNClustering

Minimum number of clusters for connectedness.

minpts - Variable in class elki.clustering.subspace.PreDeCon.Settings

DBSCAN Minpts parameter, aka "mu".

minpts - Variable in class elki.clustering.subspace.SUBCLU

Minimum number of points.

minpts - Variable in class elki.clustering.subspace.SUBCLU.Par

Minimum number of points.

minPts - Variable in class elki.clustering.hierarchical.AbstractHDBSCAN

MinPts parameter.

minPts - Variable in class elki.clustering.optics.FastOPTICS

MinPts parameter.

MINPTS_ID - Static variable in class elki.clustering.dbscan.DBSCAN.Par

Parameter to specify the threshold for minimum number of points in the epsilon-neighborhood of a point, must be an integer greater than 0.

MINPTS_ID - Static variable in class elki.clustering.subspace.SUBCLU.Par

Parameter to specify the threshold for minimum number of points in the epsilon-neighborhood of a point, must be an integer greater than 0.

MinPtsCorePredicate - Class in elki.clustering.dbscan.predicates

The DBSCAN default core point predicate -- having at least MinPtsCorePredicate.minpts neighbors.

MinPtsCorePredicate(int) - Constructor for class elki.clustering.dbscan.predicates.MinPtsCorePredicate

Default constructor.

MinPtsCorePredicate.Instance - Class in elki.clustering.dbscan.predicates

Instance for a particular data set.

MinPtsCorePredicate.Par - Class in elki.clustering.dbscan.predicates

Parameterization class

minsize - Variable in class elki.clustering.correlation.LMCLUS

Minimum cluster size

minsize - Variable in class elki.clustering.correlation.LMCLUS.Par

Minimum cluster size.

MINSIZE_ID - Static variable in class elki.clustering.correlation.LMCLUS.Par

Parameter for the minimum cluster size

minSplitSize - Variable in class elki.index.preprocessed.fastoptics.RandomProjectedNeighborsAndDensities

minimum size for which a point set is further partitioned (roughly corresponds to minPts in OPTICS)

minusEquals(double[], NumberVector) - Static method in class elki.clustering.kmeans.AbstractKMeans

Similar to VMath.minusEquals, but accepts a number vector.

minwindow - Variable in class elki.clustering.onedimensional.KNNKernelDensityMinimaClustering

Window width, for local minima criterions.

minwindow - Variable in class elki.clustering.onedimensional.KNNKernelDensityMinimaClustering.Par

Window width, for local minima criterions.

mirkin() - Method in class elki.evaluation.clustering.PairCounting

Computes the Mirkin index, aka Equivalence Mismatch Distance.

miter - Variable in class elki.clustering.kmedoids.CLARANS.Assignment

Medoid iterator

mj - Variable in class elki.clustering.hierarchical.MedoidLinkage.Instance

Iterators into medoid array.

mmacc - Variable in class elki.evaluation.clustering.ClusterContingencyTable

Maximum Matching Accuracy

mode - Variable in class elki.clustering.onedimensional.KNNKernelDensityMinimaClustering

Estimation modes.

mode - Variable in class elki.clustering.onedimensional.KNNKernelDensityMinimaClustering.Par

Estimation modes.

Mode() - Constructor for enum elki.clustering.onedimensional.KNNKernelDensityMinimaClustering.Mode

MODE_ID - Static variable in class elki.clustering.onedimensional.KNNKernelDensityMinimaClustering.Par

KDE mode.

model - Variable in class elki.data.Cluster

Cluster model.

Model - Interface in elki.data.model

Base interface for Model classes.

MODEL_ID - Static variable in class elki.clustering.em.EM.Par

Parameter to specify the EM cluster models to use.

models - Variable in class elki.clustering.em.KDTreeEM

Current clusters.

ModelUtil - Class in elki.data.model

Utility classes for dealing with cluster models.

ModelUtil() - Constructor for class elki.data.model.ModelUtil

Private constructor.

movedDistance(double[][], double[][], double[]) - Method in class elki.clustering.kmeans.AbstractKMeans.Instance

Maximum distance moved.

movedSimilarity(double[][], double[][], double[]) - Method in class elki.clustering.kmeans.spherical.SphericalKMeans.Instance

Similarity to previous locations.

mu - Variable in class elki.clustering.correlation.HiCO

Mu parameter.

mu - Variable in class elki.clustering.correlation.HiCO.Par

Mu parameter

MU_ID - Static variable in class elki.clustering.correlation.HiCO.Par

Parameter to specify the smoothing factor, must be an integer greater than 0.

MultiBorder - Class in elki.clustering.dbscan.util

Multiple border point assignment.

MultiBorder(Border, Border) - Constructor for class elki.clustering.dbscan.util.MultiBorder

Constructor.

multiple - Variable in class elki.clustering.trivial.ByLabelClustering

Allow multiple cluster assignment.

multiple - Variable in class elki.clustering.trivial.ByLabelClustering.Par

Allow multiple cluster assignment.

MULTIPLE_ID - Static variable in class elki.clustering.trivial.ByLabelClustering.Par

Flag to indicate that multiple cluster assignment is possible.

multipleAssignment(Relation<?>) - Method in class elki.clustering.trivial.ByLabelClustering

Assigns the objects of the database to multiple clusters according to their labels.

multipleNodeDeletion(double[][], ChengAndChurch.BiclusterCandidate) - Method in class elki.clustering.biclustering.ChengAndChurch

Algorithm 2 of Cheng and Church.

MultivariateGaussianModel - Class in elki.clustering.em.models

Model for a single multivariate Gaussian cluster with arbitrary rotation.

MultivariateGaussianModel(double, double[]) - Constructor for class elki.clustering.em.models.MultivariateGaussianModel

Constructor.

MultivariateGaussianModel(double, double[], double[][]) - Constructor for class elki.clustering.em.models.MultivariateGaussianModel

Constructor.

MultivariateGaussianModelFactory - Class in elki.clustering.em.models

Factory for EM with multivariate Gaussian models (with covariance; also known as Gaussian Mixture Modeling, GMM).

MultivariateGaussianModelFactory(KMeansInitialization) - Constructor for class elki.clustering.em.models.MultivariateGaussianModelFactory

Constructor.

mutualInformation - Variable in class elki.evaluation.clustering.Entropy

Mutual information (computed directly)

mutualInformation() - Method in class elki.evaluation.clustering.Entropy

Get the mutual information (not normalized, small values are good).

mvCorDim - Variable in class elki.clustering.dbscan.predicates.FourCNeighborPredicate

Tool to help with parameterization.

mvSize - Variable in class elki.clustering.dbscan.predicates.FourCNeighborPredicate

Tool to help with parameterization.

mvSize - Variable in class elki.clustering.dbscan.predicates.PreDeConNeighborPredicate

Tool to help with parameterization.

mvSize2 - Variable in class elki.clustering.dbscan.predicates.FourCNeighborPredicate

Tool to help with parameterization.

mvVar - Variable in class elki.clustering.dbscan.predicates.PreDeConNeighborPredicate

Tool to help with parameterization.

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