A hybrid algorithm for fuzzy clustering based on global and local membership degree

The clustering task has challenges that change according to the data, thus different algorithms have been proposed where each one has a bias on the data. In the fuzzy clustering approach, the most popular algorithm is the Fuzzy C-Means (FCM), which uses a global view of variables to calculate the degree of membership. On the other hand, the Multivariate Fuzzy C-Means (MFCM) uses a local view of variables to calculate the degree of membership. In this work, we proposed a new hybrid algorithm to use a combined local and global view approaches. For this, a new objective function based on the hybridization parameter is introduced. The experiments show the robustness and superiority of the proposed algorithm in real and synthetic datasets in most of the analyzed scenarios.