Information Technology and Management

, Volume 13, Issue 4, pp 403–409

Distributed data mining: a survey

Authors

  • Li Zeng
    • Institute of Computing TechnologyChinese Academy of Sciences
  • Ling Li
    • Old Dominion University
    • Institute of Computing TechnologyChinese Academy of Sciences
    • New Jersey Institute of Technology
  • Kevin Lu
    • Brunel University
  • Zhongzhi Shi
    • Institute of Computing TechnologyChinese Academy of Sciences
  • Maoguang Wang
    • Institute of Computing TechnologyChinese Academy of Sciences
  • Wenjuan Wu
    • School of InformationRemin University of China
  • Ping Luo
    • Institute of Computing TechnologyChinese Academy of Sciences
Article

DOI: 10.1007/s10799-012-0124-y

Cite this article as:
Zeng, L., Li, L., Duan, L. et al. Inf Technol Manag (2012) 13: 403. doi:10.1007/s10799-012-0124-y

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 miningBusiness intelligenceBusiness analyticsDecision support systemsDistributed systemsLiterature review

Copyright information

© Springer Science+Business Media, LLC 2012