A similarity measure for atanassov intuitionistic fuzzy sets and its application to clustering

The pattern recognition problem are mostly dealt through the process of clustering. Many important techniques used for clustering are based on similarity measures. The similarity measures are derived from distance measures. Therefore, for measuring the similarity between objects (Atanassov intuitionistic fuzzy set(AIFS)) researchers have applied several distance measures like normalized Euclidean distance measure, Hamming distance measure, etc. For a problem, the distance measure has to be judiciously selected in accordance with existing underlying nature. Hence, there does not exist any procedure of selecting a distance measure which globally works for all kind of problems. So in this paper, we have given a new similarity measure based on the distance measure of double sequence of bounded variation. We have compared results obtained through our similarity measure with the results of other similarity measure. Our results clearly depicts the efficacy of our similarity measure over other similarity measures.