A quasi-Bayesian perspective to online clustering

Le Li, Benjamin Guedj, Sébastien Loustau
2018 Electronic Journal of Statistics  
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
more » ... O/index.html) for which we give a convergence guarantee. Finally, numerical experiments illustrate the potential of our procedure.
doi:10.1214/18-ejs1479 fatcat:6tnzzhvcufelnlmon63e6ibicu