Search Results for author: Zhun-Ga Liu

Found 7 papers, 0 papers with code

TECM: Transfer Evidential C-means Clustering

no code implementations19 Dec 2021 Lianmeng Jiao, Feng Wang, Zhun-Ga Liu, Quan Pan

Clustering is widely used in text analysis, natural language processing, image segmentation, and other data mining fields.

Semantic Segmentation Transfer Learning

EGMM: an Evidential Version of the Gaussian Mixture Model for Clustering

no code implementations3 Oct 2020 Lianmeng Jiao, Thierry Denoeux, Zhun-Ga Liu, Quan Pan

The Gaussian mixture model (GMM) provides a convenient yet principled framework for clustering, with properties suitable for statistical inference.

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.

Adaptive imputation of missing values for incomplete pattern classification

no code implementations8 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.

General Classification Imputation

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.

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

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