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<item>
  <id>05621552</id>
  <dt>j</dt>
  <an>05621552</an>
  <augroup>
    <au>Liu, Tian-Peng</au>
    <au>Zhou, Ya</au>
  </augroup>
  <ti>Data mining in P2P networks.</ti>
  <so>J. Comput. Appl. 28, No. 1, 162-164 (2008).</so>
  <py>2008</py>
  <pu>Science Press, Beijing</pu>
  <lagroup>
    <la>ZH</la>
  </lagroup>
  <ccgroup>
  </ccgroup>
  <utgroup>
    <ut>$k$-mean</ut>
    <ut>distributed data mining</ut>
    <ut>P2P</ut>
    <ut>clustering</ut>
  </utgroup>
  <cigroup>
  </cigroup>
  <ligroup>
    <li>doi:10.3724/SP.J.1087.2008.00162</li>
    <li>http://www.computerapplications.com.cn/EN/abstract/abstract10847.shtml</li>
  </ligroup>
  <abgroup>
    <ab>Summary: 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 is designed in this paper to implement $k$-mean thinking in 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.</ab>
    <rv></rv>
  </abgroup>
</item>