Affinity propagation based on intuitionistic fuzzy similarity measure

Abstract

Affinity Propagation (AP) is a recently proposed clustering technique, widely used in literature, which finds the cluster center by exchanging real messages between pairs of data. These messages are calculated on the basis of similarity matrix. Accordingly, the similarity matrix is considered an essential procedure of the AP. Negative Euclidean distance is used as a similarity measure, in order to construct the similarity matrix of the AP. However, most data points lie in the non-Euclidean space which becomes difficult for the Euclidean distance to acquire the real data structure. The performance of AP might be degraded if this drawback occurs. A clustering method is proposed here called Intuitionistic Fuzzy Affinity Propagation (IFAP) that uses an intuitionistic fuzzy similarity measure to construct the similarity matrix among data points. Subsequently, the similarity matrix is fed into the AP procedure, and the cluster center will emerge after a couple of iterations. The numerical experiment is demonstrated. Results show that the IFAP outperforms the other clustering method.