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Article Dans Une Revue Information Sciences Année : 2021

NN-EVCLUS: Neural network-based evidential clustering

Résumé

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 capable of detecting previously unseen clusters when applied to new data. 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 NN-EVCLUS over state-of-the-art evidential clustering algorithms for a range of unsupervised and constrained clustering tasks involving both attribute and dissimilarity data.
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Dates et versions

hal-03235749 , version 1 (26-05-2021)

Identifiants

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Thierry Denoeux. NN-EVCLUS: Neural network-based evidential clustering. Information Sciences, 2021, 572, pp.297-330. ⟨10.1016/j.ins.2021.05.011⟩. ⟨hal-03235749⟩
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