Abstract
To analyze both the operational mechanism of current distributed data mining and the characteristics of the P2P technology: non-centralized peer and asynchronism, by extending the iterative process of classical K-mean algorithm, a distributed data mining algorithm was designed in this paper to implement k-mean thinking in a P2P networks. This algorithm exchanges information only between directly connected nodes, and can cluster local data on each peer in a global view. Finally, simulation experiments show that the algorithm is effective and accurate.
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