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Proceedings/Recueil Des Communications Année : 2023

Mixture of stochastic block models for multiview clustering

Résumé

In this work, we propose an original method for aggregating multiple clustering coming from different sources of information. Each partition is encoded by a co-membership matrix between observations. Our approach uses a mixture of Stochastic Block Models (SBM) to group co-membership matrices with similar information into components and to partition observations into different clusters, taking into account their specificities within the components. The parameters are estimated using a Variational Bayesian EM algorithm. The Bayesian framework allows for selecting an optimal numbers of clusters and components.
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Dates et versions

hal-04363422 , version 1 (24-12-2023)

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Kylliann De Santiago, Marie Szafranski, Christophe Ambroise. Mixture of stochastic block models for multiview clustering. ESANN 2023 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pp.151-156, 2023, ⟨10.14428/esann/2023.ES2023-54⟩. ⟨hal-04363422⟩
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