Recent advances on distributed unsupervised learning

Abstract

Distributed machine learning is a problem of inferring a desired relation when the training data is distributed throughout a network of agents (e.g. sensor networks, robot swarms, etc.). A typical problem of unsupervised learning is clustering, that is grouping patterns based on some similarity/dissimilarity measures. Provided they are highly scalable, fault-tolerant and energy efficient, clustering algorithms can be adopted in large-scale distributed systems. This work surveys the state-of-the-art in this field, presenting algorithms that solve the distributed clustering problem efficiently, with particular attention to the computation and clustering criteria.