Interval multidimensional scaling for group decision using rough set concept
Multidimensional scaling (MDS) is a statistical tool for constructing a low-dimension configuration to represent the relationships among objects. In order to extend the conventional MDS analysis to consider the situation of uncertainty under group decision making, in this paper the interval-valued data is considered to represent the dissimilarity matrix in MDS and the rough sets concept is used for dealing with the problems of group decision making and uncertainty simultaneously. In addition, two numerical examples are used to demonstrate the proposed method in both the situation of individual differences scaling and the conventional MDS analysis with the interval-valued data, respectively. On the basis of the results, we can conclude that the proposed method is more suitable for the real-world problems.
rough sets multidimensional scaling (mds) uncertainty interval-valued data individual differences scaling