A Quasi-Bayesian Perspective to Online Clustering

TL;DR

This paper introduces an adaptive online clustering algorithm based on a quasi-Bayesian approach that dynamically estimates the number of clusters in high-frequency data streams, supported by theoretical guarantees and practical implementation.

Contribution

It presents a novel quasi-Bayesian online clustering method with dynamic cluster number estimation and provides convergence guarantees and empirical validation.

Findings

Supported by minimax regret bounds

Implementation with convergence guarantees

Numerical experiments demonstrate effectiveness

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.