Using Mining@Home for distributed ensemble learning

Mining@Home was recently designed as a distributed architecture for running data mining applications according to the "volunteer computing" paradigm. Mining@Home already proved its efficiency and scalability when used for the discovery of frequent itemsets from a transactional database. However, it can also be adopted in several different scenarios, especially in those where the overall application can be divided into distinct jobs that may be executed in parallel, and input data can be reused, which naturally leads to the use of data cachers. This paper describes the architecture and implementation of the Mining@Home system and evaluates its performance for the execution of ensemble learning applications. In this scenario, multiple learners are used to compute models from the same input data, so as to extract a final model with stronger statistical accuracy. Performance evaluation on a real network, reported in the paper, confirms the efficiency and scalability of the framework. © 2012 Springer-Verlag.