Novel Adaptive Clustering Algorithms Based on a Probabilistic Similarity Measure Over Atanassov Intuitionistic Fuzzy Set

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This paper presents a novel probabilistic similarity measure (PSM) for Atanassov intuitionistic fuzzy sets. It then exploits PSM to propose an adaptive probabilistic similarity degree and develops the novel probabilistic $\lambda$ -cutting algorithm for clustering. Further, the probabilistic distance measure (obtained from the PSM) is used to develop a new clustering technique, which we have named “probabilistic intuitionistic fuzzy c-mean (PIFCM) algorithm”. Simulation experiments have been conducted over a variety of datasets including UCI machine learning datasets and real-world car dataset. The results obtained have been thoroughly compared with other well-known clustering techniques such as fuzzy c-mean (FCM), intuitionistic fuzzy c-mean, association coefficient method, and $\lambda$ -cutting method. Based upon the experimental results, it can be concluded that our probabilistic $\lambda$ -cutting algorithm and PIFCM algorithm outperform their existing counterparts.