Evidential Clustering
Generation of "bananas" datasets
Generating a credal partition by bootstraping Gaussian Mixture Models
Belief Peak Evidential Clustering (BPEC)
Constrained Evidential c-means algorithm
Creation of a "credpart" object from a from a fuzzy or possibilistic p...
Creation of a "credpart" object from a vector of class labels
Random generation of Must-Link and Cannot-Link constraints
Computation of a Euclidean distance matrix
Finding overlapping pairs of clusters
Credal Rand indices
Delta-Bel graph for Belief Peak Evidential Clustering (BPEC)
Evidential c-means algorithm
EkNNclus algorithm
evclust: A package for evidential clustering
Expansion of must-link and cannot-link constraints
Creates an object of class "credpart"
Harris gradient-based optimization algorithm
k-CEVCLUS algorithm
k-EVCLUS algorithm
K nearest neighbors in a dissimilarity matrix
Kernel Pairwise Constrained Component Analysis (KPCCA)
Creation of a matrix of focal sets
NN-EVCLUS algorithm
NN-EVCLUS algorithm (minibatch version)
Nonspecificity of the relational representation of a credal partition
Normalization of a credal partition
Computes the relational representation
Pairwise Constrained Component Analysis (PCCA)
Plotting a credal partition
Computation of a credal partition for new data
Relational Evidential c-means algorithm
Summary of a credal partition
Various clustering algorithms that produce a credal partition, i.e., a set of Dempster-Shafer mass functions representing the membership of objects to clusters. The mass functions quantify the cluster-membership uncertainty of the objects. The algorithms are: Evidential c-Means, Relational Evidential c-Means, Constrained Evidential c-Means, Evidential Clustering, Constrained Evidential Clustering, Evidential K-nearest-neighbor-based Clustering, Bootstrap Model-Based Evidential Clustering, Belief Peak Evidential Clustering, Neural-Network-based Evidential Clustering.