This paper addresses the problem of fitting mixture model based-clustering to imprecise data using the CEM algorithm. Imprecise data are modelled by multivariate uncertainty zones, which constitute a generalization of multivariate interval-valued data. To estimate simultaneously the mixture model parameters and the partition from uncertainty zone data, we propose an adapted version of the CEM algorithm. The paper concludes with a brief description of an application of this approach to flaw diagnosis, on pressure equipments, using acoustic emission, in the context of imprecise bivariate measurements of localization of acoustic emission signals
['Hani Hamdan', 'Gérard Govaert']
CEM algorithm for imprecise data. Application to flaw diagnosis using acoustic emission