A robust clustering method based on blind, naked mole-rats (BNMR) algorithm

One of most popular data clustering algorithms is K-means algorithm that uses the distance criterion for measuring the correlation among data. To do that, we should know in advance the number of classes (K) and choose K data point as an initial set to run the algorithm. However, the choice of initial points is a main problem in this algorithm, which may cause that the algorithm converges to local optima. So, some other clustering algorithms have been proposed to overcome this problem such as the methods based on K-means (SBKM), Genetic Algorithm (GAPS and VGAPS), Particle Swarm Optimization (PSO), Ant Colony Optimization (Dynamic ants), Simulated Annealing (SA) and Artificial Bee Colony (ABC) algorithm. In this paper, we employ a new meta-heuristic algorithm. We called it blind, naked mole-rats (BNMR) algorithm, for data clustering. The algorithm was inspired by social behavior of the blind, naked mole-rats colony in searching the food and protecting the colony against invasions. We developed a new data clustering based on this algorithm, which has the advantages such as high speed of convergence. The experimental results obtained by using the new algorithm on different well-known datasets compared with those obtained using other mentioned methods showed the better accuracy and high speed of the new algorithm.

► The paper proposes conceptually a novel method based on new optimization algorithm. ► BNMR algorithm increases the speed of its convergence. ► BNMR algorithm used in our method not only prevents data clustering algorithm from being trapped in local optima. ► The results obtained by applying the proposed method on various datasets demonstrate the superiority of our method.