In this paper, a clustering algorithm is proposed that dynamically determines the locations of fog nodes in 5G wireless networks, which are upgraded from small cells, in order to maximize throughput assuming the number of fog nodes and small cells are given as a priori information. The proposed algorithm dynamically clusters the small cells around the fog nodes. The approach is based on a soft clustering model where one small cell can be connected to many fog nodes. The numerical results demonstrate that the proposed clustering algorithm significantly enhances the throughput and lowers the latency with respect to the distance-based K- means hard clustering algorithm or Voronoi tessellation model.