• DocumentCode
    3646111
  • Title

    Rough Set Methods for Large and Spare Data in EAV Format

  • Author

    Wojciech Swieboda;Hung Son Nguyen

  • Author_Institution
    Inst. of Math., Univ. of Warsaw, Warsaw, Poland
  • fYear
    2012
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    In this article we discuss a computationally effective method for computing approximate decision reducts of large data sets. We consider the EAV (entity-attribute-value) which efficiently stores sparse data sets and we propose new implementations of Maximum Discernibility heuristic for data sets represented in this format.
  • Keywords
    "Data mining","Information systems","Approximation methods","Set theory","Databases","Partitioning algorithms","Data models"
  • Publisher
    ieee
  • Conference_Titel
    Computing and Communication Technologies, Research, Innovation, and Vision for the Future (RIVF), 2012 IEEE RIVF International Conference on
  • Print_ISBN
    978-1-4673-0307-1
  • Type

    conf

  • DOI
    10.1109/rivf.2012.6169830
  • Filename
    6169830
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3646111