A novel approach to predictive analysis using attribute-oriented rough fuzzy sets. (15th December 2020) - Yu, Bin; Cai, Mingjie; Dai, Jianhua; Li, Qingguo
Highlights: With data mining's point of view, we propose a novel rough fuzzy sets of fuzzy information systems. Based on δ -clusters, ( γ, δ )-rough fuzzy sets inherit the ability that predict it with growth trend. We use ( γ, δ )-rough fuzzy set theory to predictive analysis. The randomness of the algorithm is analyzed. Abstract: In this study, a forecasting decision-making method is put forward to deal with multi-attribute decision-making problems. On the basis of rough set theory, ( γ, δ )-rough fuzzy sets are presented using δ -clusters in data mining. Furthermore, several characteristics of the upper and lower ( γ, δ )-approximations are obtained. Lastly, the difference between the fuzzy set A of the object and the upper and lower rough ( γ, δ )-approximation operators on A is analyzed. We also design a novel algorithm to forecast decision making and provide a related example illustrating the new method.