Relative relation matrix based approaches for updating approximations in neighborhood multigranulation rough sets

With the revolution of computing and biology technology, data sets con-taining information could be huge and complex that sometimes are difficult to handle. Dynamic computing is an efficient approach to solve problems. Since neighborhood multigranulation rough sets (NMGRS) have been proposed, few papers focused on how to calculate approximations in NMGRS and how to update approximations in NMGRS dynamically. The purpose of this study is try to propose relative relation matrix based approaches for computing approximations in NMGRS and updating them dynamically. First, static approaches for computing approximations in NMGRS were proposed. Sec-ond, relative relation matrix based approaches for updating approximations in NMGRS while decreasing and increasing neighborhood classes were proposed. Third, incremen-tal algorithms for updating approximations in NMGRS while decreasing and increasing neighborhood classes were designed. Finally, the efficiency and the validity of the de-signed algorithms were verified by experiments.