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
Link To Document