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Distributed data mining come from two different objectives: the first is the desire to scale up algorithms to large data sets where the data are distributed by the algorithm in order to increase the overall efficiency; the second objective is the processing of data which are inherently distributed and autonomous. Ensemble learning methods as very promising techniques in terms of accuracy, and also providing a 'distributed' aspect, can be adapted to the distributed data mining. The authors present in this paper various approaches to use these methods in distributed data mining.
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