Dissimilarity measure between intuitionistic Fuzzy sets and its applications in pattern recognition and clustering analysis

In this study, in order to prevent information loss, we propose two dissimilarity measures between intuitionistic fuzzy sets (IFSs), which consider membership and non-membership degree and IFSs is farther extension of Fuzzy sets (FSs). Additionally, we have examined the characteristics of the proposed metrics to confirm their validity. We then conducted a series of experiments, including numerical experimentation, pattern recognition, and clustering analysis, to evaluate the efficacy of these metrics. The comparative outcomes illustrate that our dissimilarity metrics are more straightforward, easy to understand, and superior to the majority of the existing methods.