Similarity Based Decision-Making and Clustering Algorithm on a Neutrosophic Soft Set Framework

Information as well as queries in the physical world are very often imprecise or ambiguous, and may even be inconsistent in certain circumstances. A neutrosophic soft set is a mathematical structure that can efficiently handle such imperfect or inconsistent data. The notion of similarity measure takes a dominant part in choosing the most appropriate alternative in uncertain situations and is often deployed in decision-making problems. In this article, a novel similarity measure is introduced on a neutrosophic soft set that generates logically correct similarity values in special situations when some of the existing measures fail. The utility as well as efficacy of the predicted measure is illustrated through its applicability in decision-making as well as in clustering analysis.