Information Technology and Management

, Volume 13, Issue 4, pp 403–409

Distributed data mining: a survey

  • Li Zeng
  • Ling Li
  • Lian Duan
  • Kevin Lu
  • Zhongzhi Shi
  • Maoguang Wang
  • Wenjuan Wu
  • Ping Luo
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

Authors and Affiliations

  • Li Zeng
    • 1
  • Ling Li
    • 2
  • Lian Duan
    • 1
    • 3
  • Kevin Lu
    • 4
  • Zhongzhi Shi
    • 1
  • Maoguang Wang
    • 1
  • Wenjuan Wu
    • 5
  • Ping Luo
    • 1
  1. 1.Institute of Computing TechnologyChinese Academy of SciencesBeijingChina
  2. 2.Old Dominion UniversityNorfolkUSA
  3. 3.New Jersey Institute of TechnologyNewarkUSA
  4. 4.Brunel UniversityUxbridgeUK
  5. 5.School of InformationRemin University of ChinaBeijingChina