@article{discovery10064561,
           month = {January},
          number = {2},
         journal = {Electronic Journal of Statistics},
           title = {A quasi-Bayesian perspective to online clustering},
            year = {2018},
            note = {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.},
          volume = {12},
           pages = {3071--3113},
             url = {https://doi.org/10.1214/18-EJS1479},
          author = {Li, L and Guedj, B and Loustau, S},
            issn = {1935-7524},
        abstract = {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.},
        keywords = {Online clustering, quasi-Bayesian learning, minimax regret bounds, reversible jump Markov chain Monte Carlo}
}