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In this paper a new method is presented for finding data clusters centroids. This method, called Force, is based on the concepts of electrostatic fields in which the centroids are positioned at locations where an electrostatic equilibrium or balance could be achieved. After determining the centroids locations, criteria such as minimum distance to centroid can be used for clustering data points. The performance of the proposed method is compared against the k-means algorithm through simulation experiments. Experimental results show that the Force algorithm does not suffer from problems associated with k-means, such as sensitivity to noise and initial selection of centroids, and tendency to converge to poor local optimum. In fact, we show that this algorithm always converges to global equilibrium points, regardless of the initial guesses, and even in presence of high levels of noise.