  Evidential clustering is an approach to clustering based on the use of
Dempster-Shafer mass functions to represent cluster-membership uncertainty. In
this paper, we introduce a neural-network based evidential clustering
algorithm, called NN-EVCLUS, which learns a mapping from attribute vectors to
mass functions, in such a way that more similar inputs are mapped to output
mass functions with a lower degree of conflict. The neural network can be
paired with a one-class support vector machine to make it robust to outliers
and allow for novelty detection. The network is trained to minimize the
discrepancy between dissimilarities and degrees of conflict for all or some
object pairs. Additional terms can be added to the loss function to account for
pairwise constraints or labeled data, which can also be used to adapt the
metric. Comparative experiments show the superiority of N-EVCLUS over
state-of-the-art evidential clustering algorithms for a range of unsupervised
and constrained clustering tasks involving both attribute and dissimilarity
data.
