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Journal Articles Electronic Journal of Statistics Year : 2018

A Quasi-Bayesian Perspective to Online Clustering

Le Li
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Benjamin Guedj
Sébastien Loustau
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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.
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Dates and versions

hal-01264233 , version 1 (28-01-2016)
hal-01264233 , version 2 (07-04-2017)
hal-01264233 , version 3 (08-04-2017)
hal-01264233 , version 4 (25-05-2018)


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Le Li, Benjamin Guedj, Sébastien Loustau. A Quasi-Bayesian Perspective to Online Clustering. Electronic Journal of Statistics , 2018, ⟨10.1214/18-EJS1479⟩. ⟨hal-01264233v4⟩
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