… feature clusterings, a graded possibilistic medoid meta clustering algorithm is proposed in
… to possibilistic memberships in a way that produces more compact and separated clusters …

… graded possibilistic medoid meta-clustering algorithm, exploiting its ability to perform a soft
transition from probabilistic to possibilistic … partition in all meta-clusters identifies observations …

… In this paper the use of meta–clustering is proposed for the selection of lowest energy …
limitation, meta–clustering has been proposed in this paper using the Rough Graded Possibilistic c…

… clustering, the graded possibilistic model allows the soft transition from probabilistic to
possibilistic … The integration of rough sets principles in the graded possibilistic clustering aims to …

… as a quality criterion for clustering has been questioned in [5], the analysis by Ben-David,
Von Luxburg and Pál does not fully apply for fuzzy and possibilistic clustering; we provide …

… When compared to crisp clustering, fuzzy clustering provides … case for our Graded Possibilistic
c-Means clustering method, which … Building on our own previous work on fuzzy clustering …

… a possibilistic partition in which a possibilistic membership is … cluster. The larger the distance
between an object to a centroid (prototype) is, the lower the possibilistic membership grade …

… Rough-Graded-Possibilistic Metaclustering algorithm has … metaclustering has been proposed
in this paper. The method is built on top of the Rough-GradedPossibilistic metaclustering …

… The effect size of the i th method in j th function is calculated by the membership grade of
the i th method divided by the sum of the membership grades of all methods in the j th cluster. …

… hidden process, a robust clustering method is required. In this paper, adaptive interval
type2-possibilistic C-means and adaptive interval type2-possibilistic fuzzy C-means clustering …