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The Big Bang–Big Crunch (BB–BC) algorithm is a new optimization method that is based on one of the theories of the evolution of the universe namely the Big Bang and Big Crunch theory. According to this method, in the Big Bang phase some candidate solutions to the optimization problem are randomly generated and spread all over the search space. In the Big Crunch phase, randomly distributed candidate solutions are drawn into a single representative point via a center of population or minimal cost approach. This paper presents BB-BC based novel approach for data clustering. The simulation results indicate the applicability and potential of this algorithm on data clustering.