Clustering algorithms for uncertain data.
|Class and Description|
Center-of-mass meta clustering reduces uncertain objects to their center of mass, then runs a vector-oriented clustering algorithm on this data set.
Run k-means on the centers of each uncertain object.
FDBSCAN is an adaption of DBSCAN for fuzzy (uncertain) objects.
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.
Instance of the neighbor predicate.
Representative clustering of uncertain data.
Uncertain K-Means clustering, using the average deviation from the center.
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