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Distributed Bayesian clustering

by   Hanyu Song, et al.
Duke University

In many modern applications, there is interest in analyzing enormous data sets that cannot be easily moved across computers or loaded into memory on a single computer. In such settings, it is very common to be interested in clustering. Existing distributed clustering algorithms are mostly distance or density based without a likelihood specification, precluding the possibility of formal statistical inference. We introduce a nearly embarrassingly parallel algorithm using a Bayesian finite mixture of mixtures model for distributed clustering, which we term distributed Bayesian clustering (DIB-C). DIB-C can flexibly accommodate data sets with various shapes (e.g. skewed or multi-modal). With data randomly partitioned and distributed, we first run Markov chain Monte Carlo in an embarrassingly parallel manner to obtain local clustering draws and then refine across nodes for a final cluster estimate based on any loss function on the space of partitions. DIB-C can also provide a posterior predictive distribution, estimate cluster densities, and quickly classify new subjects. Both simulation studies and real data applications show superior performance of DIB-C in terms of robustness and computational efficiency.


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