A novel intuitionistic fuzzy similarity measure based on double sequence by using modulus function with application in pattern recognition
Mohd Shoaib Khan, Q. M. Danish Lohani, Mohammad Mursaleen · Cogent Mathematics · 2017
In the field of pattern recognition, clustering is used to group the data into different clusters based on the similarity among them. There are a number of clustering techniques developed in the past using different distance/similarity measure. Due to the high versatility in data, researchers have used various distance measure like Hamming distance, Euclidean distance etc. to solve the clustering problems. In this paper, we proposed a novel similarity measure based on the double sequence space and modulus function. Also, to handle the uncertainty of data, Atanassov intuitionistic fuzzy set were used. Experimental simulation is performed on the real-world problems viz. car data and medical diagnosis problems and shows that the results are outperformed.