no code implementations • 19 Dec 2021 • Lianmeng Jiao, Feng Wang, Zhun-Ga Liu, Quan Pan
As a representative evidential clustering algorithm, evidential c-means (ECM) provides a deeper insight into the data by allowing an object to belong not only to a single class, but also to any subset of a collection of classes, which generalizes the hard, fuzzy, possibilistic, and rough partitions.
no code implementations • 3 Oct 2020 • Lianmeng Jiao, Thierry Denoeux, Zhun-Ga Liu, Quan Pan
The Gaussian mixture model (GMM) provides a simple yet principled framework for clustering, with properties suitable for statistical inference.
no code implementations • 13 Jun 2016 • Kuang Zhou, Arnaud Martin, Quan Pan, Zhun-Ga Liu
With the increasing size of social networks in real world, community detection approaches should be fast and accurate.
no code implementations • 3 Jun 2016 • Kuang Zhou, Arnaud Martin, Quan Pan, Zhun-Ga Liu
In the application of FCMdd and original ECMdd, a single medoid (prototype), which is supposed to belong to the object set, is utilized to represent one class.
no code implementations • 8 Feb 2016 • Zhun-Ga Liu, Quan Pan, Jean Dezert, Arnaud Martin
We propose a credal classification method for incomplete pattern with adaptive imputation of missing values based on belief function theory.
no code implementations • 15 Jul 2015 • Kuang Zhou, Arnaud Martin, Quan Pan, Zhun-Ga Liu
Medoid-based clustering algorithms, which assume the prototypes of classes are objects, are of great value for partitioning relational data sets.
no code implementations • 7 Jan 2015 • Kuang Zhou, Arnaud Martin, Quan Pan, Zhun-Ga Liu
In this paper, a new prototype-based clustering method, called Median Evidential C-Means (MECM), which is an extension of median c-means and median fuzzy c-means on the theoretical framework of belief functions is proposed.