An Efficient Multi-criteria Decision-making Approach Based on Hybridizing Data Mining Techniques

Multiple-criteria decision-making (MCDM) that deals with multiple criteria in decision-making environments has been explicitly applied to various decision-making fields. Nevertheless, the critical issues of uncertainty and inaccuracy generally and gradually exists in the majority of the MCDM processes because of (1) prejudice and preference of decision-makers or experts as well as (2) the insufficiency information of the input and output. Therefore, this research efficiently proposed a novel method, FVM-index method, to resolve the limitations happened when MCDM is applied. The FVM-index approach, which consists of the fuzzy set theory (FST), the variable precision rough set (VPRS), and the cluster validity index (CVI) function, not only provides optimized classification results for the datasets but also filters out the uncertainty and inaccuracy instances from surveyed datasets by VPRS theory. Because the datasets are refined by the proposed FVM-index method, the decision makers will be able to effectively obtain the suitable results of MCDM. Keyword: Multi-Criteria Decision Making, Variable Precision Rough Set, Fuzzy Set Theory, FVM-index method.