Synergy of two mutations based immune multi-objective automatic fuzzy clustering algorithm.

In this paper, a synergy of two mutation based immune multi-objective automatic fuzzy clustering algorithm (STMIMAFC) is proposed for the task of automatically evolving the number of clusters as well as a proper partitioning of data set. In the proposed algorithm, firstly, two new mutation operators, which are designed for the different structures of chromosomes respectively, are cooperated with each other to generate the new individuals. Secondly, we propose an exponential function based compactness validity index. The proposed method has been extensively compared with a synergy of genetic algorithm and multi-objective differential evolution, multi-objective modified differential evolution based fuzzy clustering, multi-objective clustering with automatic $$k$$ -determination over a test suit of several real life data sets and synthetic data sets. Experimental results indicate the superiority of the STMIMAFC over other three compared clustering algorithms on clustering accuracy and running time. [ABSTRACT FROM AUTHOR]