Data clustering is one of the most popular techniques of information management, which is used in many applications of science and engineering such as machine learning, pattern reorganization, image processing, data mining, and web mining. Different algorithms have been suggested by researchers, where the evolutionary algorithms are the best in data clustering and especially in big datasets. It is illustrated that GSA-KM, which is a combination of the gravitational search algorithm (GSA) and K-means (KM), is superior over some other comparative evolutionary methods. One of the drawbacks of this approach is dependency on the initial seeds. In this paper, a combination method of GSA and K-harmonic means, called GSA-KHM, has been proposed, in which the dependency on the initialization has been improved. The proposed GSA-KHM method has been applied to data clustering. As a special application, it has also been used on the text document clustering application. The simulation results show that the proposed method works better than the GSA-KM and other comparative methods in both data clustering and text document clustering applications.
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