Distributed data clustering over networks

In this paper, we consider the problem of distributed unsupervised clustering, where training data is partitioned over a set of agents, whose interaction happens over a sparse, but connected, communication network. To solve this problem, we recast the well known Expectation Maximization method in a distributed setting, exploiting a recently proposed algorithmic framework for in-network non-convex optimization. The resulting algorithm, termed as Expectation Maximization Consensus, exploits successive local convexifications to split the computation among agents, while hinging on dynamic consensus to diffuse information over the network in real-time. Convergence to local solutions of the distributed clustering problem is then established. Experimental results on well-known datasets illustrate that the proposed method performs better than other distributed Expectation-Maximization clustering approaches, while the method is faster than a centralized Expectation-Maximization procedure and achieves a comparable performance in terms of cluster validity indexes. The latter ones achieve good values in absolute range scales and prove the quality of the obtained clustering results, which compare favorably with other methods in the literature.