• DocumentCode
    3624030
  • Title

    Multi-Agent Architecture for Knowledge Discovery

  • Author

    Daniel Pop;Viorel Negru;Calin Sandru

  • Author_Institution
    West University of Timisoara, Romania
  • fYear
    2006
  • Firstpage
    217
  • Lastpage
    226
  • Abstract
    Knowledge discovery from databases (KDD) is a complex process composed of several phases: business understanding, data understanding, data preparation, modeling, evaluation and deployment. For each of the phases, there are many algorithms and methods available, the end-user having to select one of them. The AgentDiscover is a multi-agent based intelligent recommendation system for selection of the most appropriate solving method for each phase. This brings added value for both novice and experienced users
  • Keywords
    "Multiagent systems","Data mining","Computer architecture","Intelligent agent","Databases","Scalability","Problem-solving","Ontologies","Engines","Computer science"
  • Publisher
    ieee
  • Conference_Titel
    Symbolic and Numeric Algorithms for Scientific Computing, 2006. SYNASC ´06. Eighth International Symposium on
  • Print_ISBN
    0-7695-2740-X
  • Type

    conf

  • DOI
    10.1109/SYNASC.2006.55
  • Filename
    4090322
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3624030