%P 3071-3113
%T A quasi-Bayesian perspective to online clustering
%O This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
%D 2018
%A L Li
%A B Guedj
%A S Loustau
%X When faced with high frequency streams of data, clustering raises theoretical and algorithmic pitfalls. We introduce a new and adaptive online clustering algorithm relying on a quasi-Bayesian approach, with a dynamic (i.e., time-dependent) estimation of the (unknown and changing) number of clusters. We prove that our approach is supported by minimax regret bounds. We also provide an RJMCMC-flavored implementation (called PACBO, see https://cran.r-project.org/web/packages/PACBO/index.html) for which we give a convergence guarantee. Finally, numerical experiments illustrate the potential of our procedure.
%N 2
%J Electronic Journal of Statistics
%V 12
%L discovery10064561
%K Online clustering, quasi-Bayesian learning, minimax regret bounds, reversible jump Markov chain Monte Carlo