A two-stage meta-heuristic approach to general type-ii fuzzy clustering for microarray data analysis

This paper considers the microarray gene expression data clustering using a novel two stage meta-heuristic algorithm based on the concept of a-planes in general type-2 fuzzy sets. The main aim of this research is to present a powerful data clustering approach capable of dealing with highly uncertain environments. In this regards, first, a new developed objective function using a-planes in general type-2 fuzzy c-means clustering algorithm is represented. Then, based on the philosophy of the meta-heuristic optimization framework Simulated Annealing, a two stage optimization meta-heuristic algorithm is proposed. The first stage of the proposed approach is devoted to the annealing process accompanied by the proposed perturbation mechanisms. After termination of the first stage, its output is inserted to the second stage where it is checked with other possible global optima through a heuristic algorithm. The output of this stage is then reentered to the first stage until no better solution is obtained. The proposed approach has been evaluated using three real microarray dataset.