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Since a large amount of information is added onto the internet on a daily basis, the efficiency of peer-to-peer (P2P) search has become increasingly important. However, how to quickly discover the right resource in a large-scale P2P network without generating too much network traffic and with minimum possible time remain highly challenging. In this paper, we propose a new P2P search method by applying the concept of data mining (decision tree) in order to improve search performance. We focus on routing queries to the right destination. Through a super-peer-based architecture, peers having similar interests are grouped together under a super-peer (SP) connected to a Meta-Super-Peer (MSP) operating with an index (decision tree) to predict the relevant domains (super-peers) to answer a given query. Compared with a super-peer based approach, our proposal architectures show the effect of the data mining with better performance with respect to response time and precision.