A novel similarity measure in intuitionistic fuzzy sets and its applications

Intuitionistic fuzzy set (IFS) is a classical branch of fuzzy set, which has advantage to deal with uncertain problems. In IFS, similarity measure is an important fundamental concept, it is used to measure consistency between different intuitionistic fuzzy sets (IFSs) and becomes a key parameter in fuzzy decision system. However, the previous methods of similarity measure do not take enough account the effect of hesitancy degree on membership degree and non-membership degree, so that produce counterintuitive results when measuring similarity. Hence, in this paper, a new similarity measure of IFS is presented. The effect of hesitancy degree on similarity measure is fully considered in the proposed method and some properties also haven been discussed to prove the reasonable of proposed method. Meanwhile, some numerical examples are analyzed to illustrate characteristics of proposed similarity measure in detail. Further, the experiments of target classification and clustering problem demonstrate effectiveness and superiority of proposed similarity measure in the environment of expert assessments and data set.