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Found 7 papers, 0 papers with code
Median evidential c-means algorithm and its application to community detection
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.
Evidential relational clustering using medoids
Medoid-based clustering algorithms, which assume the prototypes of classes are objects, are of great value for partitioning relational data sets.
Adaptive imputation of missing values for incomplete pattern classification
We propose a credal classification method for incomplete pattern with adaptive imputation of missing values based on belief function theory.
ECMdd: Evidential c-medoids clustering with multiple prototypes
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.
Evidential Label Propagation Algorithm for Graphs
With the increasing size of social networks in real world, community detection approaches should be fast and accurate.
EGMM: an Evidential Version of the Gaussian Mixture Model for Clustering
The Gaussian mixture model (GMM) provides a simple yet principled framework for clustering, with properties suitable for statistical inference.
TECM: Transfer Learning-based Evidential C-Means Clustering
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.
