Article

Information Technology and Management

, Volume 13, Issue 4, pp 403-409

Distributed data mining: a survey

  • Li ZengAffiliated withInstitute of Computing Technology, Chinese Academy of Sciences
  • , Ling LiAffiliated withOld Dominion University
  • , Lian DuanAffiliated withInstitute of Computing Technology, Chinese Academy of SciencesNew Jersey Institute of Technology Email author 
  • , Kevin LuAffiliated withBrunel University
  • , Zhongzhi ShiAffiliated withInstitute of Computing Technology, Chinese Academy of Sciences
  • , Maoguang WangAffiliated withInstitute of Computing Technology, Chinese Academy of Sciences
  • , Wenjuan WuAffiliated withSchool of Information, Remin University of China
  • , Ping LuoAffiliated withInstitute of Computing Technology, Chinese Academy of Sciences

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Abstract

Most data mining approaches assume that the data can be provided from a single source. If data was produced from many physically distributed locations like Wal-Mart, these methods require a data center which gathers data from distributed locations. Sometimes, transmitting large amounts of data to a data center is expensive and even impractical. Therefore, distributed and parallel data mining algorithms were developed to solve this problem. In this paper, we survey the-state-of-the-art algorithms and applications in distributed data mining and discuss the future research opportunities.

Keywords

Data mining Business intelligence Business analytics Decision support systems Distributed systems Literature review