Package de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain

Clustering algorithms for uncertain data.

See: Description

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.Parameterizer<C extends Clustering<?>>	Parameterization class.
CKMeans	Run k-means on the centers of each uncertain object.
CKMeans.Parameterizer	Parameterization class, based on k-means.
FDBSCAN	FDBSCAN is an adaption of DBSCAN for fuzzy (uncertain) objects.
FDBSCAN.Parameterizer	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.Parameterizer	Parameterizer class.
RepresentativeUncertainClustering	Representative clustering of uncertain data.
RepresentativeUncertainClustering.Parameterizer	Parameterization class.
RepresentativeUncertainClustering.RepresentativenessEvaluation	Representativeness evaluation result.
UKMeans	Uncertain K-Means clustering, using the average deviation from the center.
UKMeans.Parameterizer	Parameterization class.

Package de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain Description

Clustering algorithms for uncertain data.