Search Results for author: Kuang Zhou

Found 11 papers, 0 papers with code

Evidential community detection based on density peaks

no code implementations28 Sep 2018 Kuang Zhou, Quan Pan, Arnaud Martin

Credal partitions in the framework of belief functions can give us a better understanding of the analyzed data set.

Community Detection

The Advantage of Evidential Attributes in Social Networks

no code implementations26 Jul 2017 Salma Ben Dhaou, Kuang Zhou, Mouloud Kharoune, Arnaud Martin, Boutheina Ben Yaghlane

In this paper, we will present how we detect communities in graphs with uncertain attributes in the first step.

Evidence combination for a large number of sources

no code implementations25 Jul 2017 Kuang Zhou, Arnaud Martin, Quan Pan

It will keep the spirit of the conjunctive rule to reinforce the belief on the focal elements with which the sources are in agreement.

Semi-supervised evidential label propagation algorithm for graph data

no code implementations29 Jul 2016 Kuang Zhou, Arnaud Martin, Quan Pan

In the task of community detection, there often exists some useful prior information.

Community Detection

Evidential Label Propagation Algorithm for Graphs

no code implementations13 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.

Community Detection

ECMdd: Evidential c-medoids clustering with multiple prototypes

no code implementations3 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.

The belief noisy-or model applied to network reliability analysis

no code implementations3 Jun 2016 Kuang Zhou, Arnaud Martin, Quan Pan

In this paper, an extension of NOR model based on the theory of belief functions, named Belief Noisy-OR (BNOR), is proposed.

Evidential relational clustering using medoids

no code implementations15 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.

Belief Hierarchical Clustering

no code implementations12 Jan 2015 Wiem Maalel, Kuang Zhou, Arnaud Martin, Zied Elouedi

In the data mining field many clustering methods have been proposed, yet standard versions do not take into account uncertain databases.

Median evidential c-means algorithm and its application to community detection

no code implementations7 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.

Community Detection Graph Clustering +1

Evidential-EM Algorithm Applied to Progressively Censored Observations

no code implementations7 Jan 2015 Kuang Zhou, Arnaud Martin, Quan Pan

Evidential-EM (E2M) algorithm is an effective approach for computing maximum likelihood estimations under finite mixture models, especially when there is uncertain information about data.

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