Résumé : In the framework of heavy-tailed time series, extremal observations cluster : an extreme value triggers a short period with numerous large observations. This behaviour is known to perturb classical inference procedures tailored for independent observations like high quantile inference. We aim to infer properties of the clustering effect by applying functions to consecutive observations with extremal behaviour. We recover classical statistics like the extremal index and cluster size probabilities with cluster inference. In this talk, we discuss the asymptotics of block estimators for cluster inference based on consecutive observations with large lα-norm, where α>0 is the tail index of the series. Interestingly, in the case of ARMA models, our computations show that many cluster statistics have null asymptotic variance, as first conjectured in Hsing T. (1996).