A large-scale MAGDM model based on SKNN and weighted clustering under incomplete information

Abstract

Due to the complexity of multi-attribute group decision-making(MAGDM) problems, decision-makers frequently provide incomplete and hesitant evaluation information. How to make a valid decision based on incomplete and hesitant information is an important issue in MAGDM problems. Firstly, we novelly introduce a sequential K-nearest neighbor(SKNN) interpolation method to estimate the missing values based on the order of missing information proportions. When seeking the k-nearest neighbors of a decision-maker, an improved similarity measurement is proposed by considering the fuzzy-value similarity and the hesitation similarity between decision-makers simultaneously. Secondly, a K-means clustering algorithm based on attribute weighting is proposed to make the collective evaluation information more representative, and the attribute weights are obtained by constructing a mathematical model based on the minimum discrimination principle which can synthesize the subjective and objective weights information. Finally, an illustrative example and comparison analysis are demonstrated to show the validity and superiority of the proposed model.