Roughness and similarity measure of rough neutrosophic multisets using vectorial model of information

Abstract

The roughness and similarity measure for two different information in the same universal set is useful in explaining the strength and completeness of the information given. Then, for rough neutrosophic multisets environment, the lower and upper approximation was a concerned property to study in explaining the roughness of the information needed. Meanwhile, the vectorial models of information which are cosine measure and dice measure represent the result for the similarity measure of rough neutrosophic multisets. The finding of this set theory gives a new generalization about similarity measure for multiple information involving indeterminacy information in the same environment. Besides that, the rough neutrosophic multisets theory also applicable set-in decision making for medical diagnosis. The comparison result showed that the roughness approximation of information is essential to get the best result in a close similarity measure.