Dynamic maintenance strategy for approximations in set-valued ordered information systems under the attribute generalization

Rough set theory has been one of the major mathematical tools in data mining and knowledge discovery. The basic concepts of rough set theory are a pair of non-numerical operators, i.e., lower and upper approximation operators that are exported from the approximation spaces. Set-valued ordered information systems are generalized models of single-valued information systems. The attribute set in an information system may vary due to the arrival of new information. In this paper, we focus on the incremental approach for dynamically updating approximations in the set-valued ordered information systems when the attribute set varies over time.