Clustering algorithms for uncertain data.
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. CKMeansRun k-means on the centers of each uncertain object. CKMeans.ParParameterization class, based on k-means. FDBSCANFDBSCAN is an adaption of DBSCAN for fuzzy (uncertain) objects. FDBSCAN.ParParameterizer class. FDBSCANNeighborPredicateDensity-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.InstanceInstance of the neighbor predicate. FDBSCANNeighborPredicate.ParParameterizer class. RepresentativeUncertainClusteringRepresentative clustering of uncertain data. RepresentativeUncertainClustering.ParParameterization class. RepresentativeUncertainClustering.RepresentativenessEvaluationRepresentativeness evaluation result. UKMeansUncertain K-Means clustering, using the average deviation from the center. UKMeans.ParParameterization class.