Density peaks clustering (DPC) is an efficient and effective algorithm due to its outstanding performance in discovering clusters with varying densities. However, the quality of this method is highly dependent on the cutoff distance. To improve the performance of DPC, the gravitation-based clustering (GDPC) algorithm is proposed. However, it cannot identify the clusters of varying densities. We developed a novel density peaks clustering algorithm based on the magnitude and direction of the resultant force acting on a data point (RFDPC). RFDPC is based on the idea that the resultant forces acting on the data points in the same cluster are more likely to point towards the cluster center. The cluster centers are selected based on the force directional factor and distance in the decision graph. Experimental results indicate superior performance of the proposed algorithm in detecting clusters of different densities, irregular shapes, and numbers of clusters.