We present a general Multi-Agent System framework for\ud distributed data mining based on a Peer-to-Peer model. Agent\ud protocols are implemented through message-based asynchronous\ud communication. The framework adopts a dynamic load balancing\ud policy that is particularly suitable for irregular search algorithms. A modular design allows a separation of the general-purpose system protocols and software components from the specific data mining algorithm. The experimental evaluation has been carried out on a parallel frequent subgraph mining algorithm, which has shown good scalability performances
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