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 :
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