• DocumentCode
    2283436
  • Title

    A Multi-agents Approach to Knowledge Discovery

  • Author

    Tong, Cuong ; Sharma, Dharmendra ; Shadabi, Fariba

  • Author_Institution
    Fac. of Inf. Sci. & Eng., Univ. of Canberra, Canberra, ACT
  • Volume
    3
  • fYear
    2008
  • fDate
    9-12 Dec. 2008
  • Firstpage
    571
  • Lastpage
    574
  • Abstract
    Over the past few years, data mining and multi-agent approach has been used successfully in the development of large complex systems. Such a hybrid approach can be considered as an effective approach for the development of predictive modeling in complex e-health systems. We propose a real time Data Mining and Multi-Agent System called DMMAS, modeling chronic disease data. DMMAS approach employs data partitioning and multiple agents with option to employ heterogeneous or homogenous data mining techniques, distributing agent based processing for modeling and combining results from all the agents to improve the efficiency.
  • Keywords
    biology computing; data mining; distributed processing; medical computing; multi-agent systems; chronic disease data modeling; complex e-health system; data partitioning; distributing agent based processing; heterogeneous mining; homogenous data mining; knowledge discovery; multiagent system; predictive modeling; Aging; Australia; Blood; Data mining; Databases; Diabetes; Diseases; Intelligent agent; Multiagent systems; Partitioning algorithms; Data mining; chronic disease; multi-agent;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Web Intelligence and Intelligent Agent Technology, 2008. WI-IAT '08. IEEE/WIC/ACM International Conference on
  • Conference_Location
    Sydney, NSW
  • Print_ISBN
    978-0-7695-3496-1
  • Type

    conf

  • DOI
    10.1109/WIIAT.2008.418
  • Filename
    4740845