In this paper, the authors address the issue of automatic tag attachment to the documents distributed over a P2P network aiming at improving the efficiency of file search in such networks. The proposed scheme combines text clustering with a modified tag extraction algorithm, and is executed in a fully distributed manner. Meanwhile, the optimal cluster number can also be fixed automatically through a distance cost function. We have conducted experiments to evaluate the accuracy of the proposed scheme. The result of experiments indicates that the proposed approach is capable of making effective and efficient tag attachment in real scenarios; i.e., for more than 90% of documents, it attaches the same tags as the ones attached by human reviewers. Moreover, it proofs by the experiments that the optimal cluster number is almost the same as the number of topics from the website.

P2P system, text clustering, automatic tag attachment, K-DMeans, TFIDCF