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Communication Dans Un Congrès Année : 2021

Dynamic clustering and modeling of temporal data subject to common regressive effects

Résumé

Clustering is used in many applicative fields to summarize information into a small number of groups. Motivated by behavioral extraction issues from urban data, the interest of this paper is to propose a classification method that allows modeling the evolution of cluster profiles over time while considering common regressive effects. The parameters of the proposed model are estimated using variational approximation because maximum likelihood estimation is not suitable in this case. The ability of the model to estimate parameters is evaluated using various simulated data and compared with two other models.
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Dates et versions

hal-03601786 , version 1 (08-03-2022)

Identifiants

  • HAL Id : hal-03601786 , version 1

Citer

Louise Bonfils, Allou Same, Latifa Oukhellou. Dynamic clustering and modeling of temporal data subject to common regressive effects. ESANN 2021, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Oct 2021, Bruges, Belgium. 6p. ⟨hal-03601786⟩
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