• DocumentCode
    2421250
  • Title

    Rough Sets Approximations to Possibilistic Information

  • Author

    Nakata, Michinori ; Sakai, Hiroshi

  • Author_Institution
    Josai Int. Univ., Chiba
  • fYear
    0
  • fDate
    0-0 0
  • Firstpage
    2343
  • Lastpage
    2350
  • Abstract
    Rough sets are applied to data tables containing possibilistic information. A family of weighted equivalence classes is obtained, in which each equivalence class is accompanied by a possibilistic degree to which it is an actual one. By using the family of weighted equivalence classes we can derive a lower approximation and an upper approximation. The lower approximation and the upper approximation coincide with those obtained from methods of possible worlds. Therefore, the method of weighted equivalence classes is justified.
  • Keywords
    data handling; data mining; equivalence classes; possibility theory; rough set theory; data tables; lower approximation; possibilistic information; possibility distributions; rough sets approximations; upper approximation; weighted equivalence classes; Data mining; Databases; Information management; Information science; Mathematics; Rough sets; Technology management; Uncertainty;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Fuzzy Systems, 2006 IEEE International Conference on
  • Conference_Location
    Vancouver, BC
  • Print_ISBN
    0-7803-9488-7
  • Type

    conf

  • DOI
    10.1109/FUZZY.2006.1682026
  • Filename
    1682026