Package elki.clustering.uncertain
Clustering algorithms for uncertain data.
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Class Summary Class Description CenterOfMassMetaClustering<C extends Clustering<?>> Center-of-mass meta clustering reduces uncertain objects to their center of mass, then runs a vector-oriented clustering algorithm on this data set.CenterOfMassMetaClustering.Par<C extends Clustering<?>> Parameterization class.CKMeans Run k-means on the centers of each uncertain object.CKMeans.Par Parameterization class, based on k-means.FDBSCAN FDBSCAN is an adaption of DBSCAN for fuzzy (uncertain) objects.FDBSCAN.Par Parameterizer class.FDBSCANNeighborPredicate Density-based Clustering of Applications with Noise and Fuzzy objects (FDBSCAN) is an Algorithm to find sets in a fuzzy database that are density-connected with minimum probability.FDBSCANNeighborPredicate.Instance Instance of the neighbor predicate.FDBSCANNeighborPredicate.Par Parameterizer class.RepresentativeUncertainClustering Representative clustering of uncertain data.RepresentativeUncertainClustering.Par Parameterization class.RepresentativeUncertainClustering.RepresentativenessEvaluation Representativeness evaluation result.UKMeans Uncertain K-Means clustering, using the average deviation from the center.UKMeans.Par Parameterization class.