New multiparametric similarity measure for neutrosophic set with big data industry evaluation

In the age of big data, we often face the huge Volume, high Velocity, rich Variety, accuracy Veracity and high Value (5Vs) with complicated structures. Big data industrial decision making is crucial for a country or society to improve the effectiveness of leadership, which can remarkably promote scale development and industrialization. Under such circumstance of big data industrial decision assessment, the intrinsic issue involves serious indeterminacy. Single-valued neutrosophic set, depicted by truth membership, indeterminacy membership and falsity membership, is a highly effective way to grasp uncertainty. The dominating aim is to explore multiparametric similarity measure and distance measure with their properties. Then, the objective weight is computed by deviation-based method and the combination weight is presented. Moreover, we propose a novel neutrosophic decision making method based on multiparametric similarity measure with combination weight, which is stated by a big data industry decision making issue, along with the impact of various parameters on the final ranking. In the end, a comparison between the proposed algorithm and some existing neutrosophic algorithms has been built by the antilogarithm by zero problem, counter-intuitive phenomena and division by zero problem for showing their validity.