Turkish Journal of Electrical Engineering and Computer Sciences




Nature-inspired optimization algorithms have become popular in the past decade. They have been applied to solve various kinds of problems. Among these would be data clustering, which has become popular in data mining in recent times due to the data explosion. In the last decade, many metaheuristic algorithms have been used to obtain improved data clustering optimization for solving data mining problems. In this paper, we applied the seed disperser ant algorithm (SDAA), which mimics the evolution of an Aphaenogaster senilis ant colony, and we introduced a modified SDAA that is a hybrid of K-means and SDAA for solving data clustering problems. The solutions obtained for the data clustering are very promising in terms of quality of solutions and convergence speed of the algorithm.

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