Clustering is a very important technique in knowledge discovery. It has been widely used in data mining, image processing, machine learning, bioinformatics, marketing and other fields. Clustering discern the objects into groups called clusters, based on certain criteria. The similarity of objects is high within the clusters, but low between the clusters. In this work, we investigate a hybridization of the gravitational search algorithm (GSA) and big bang-big crunch algorithm (BB-BC) on data clustering. In the proposed approach, namely GSA-BB, GSA is used to explore the search space for finding the optimal locations of the clusters centroids. Whenever GSA loses its exploration, BB-BC algorithm is used to diversify the population. The performance of the proposed method is compared with GSA, BB-BC and K-means algorithms using six standard and real datasets taken from the UCI machine learning repository. Experimental results indicate that there is significant improvement in the quality of the clusters obtained by the proposed hybrid method over the non-hybrid methods.