MPI process placement is an important step to achieve scalable performance on modern non-uniform memory access (NUMA) systems. A recent study on NUMA architectures has shown that, on modern NUMA systems, the memory congestion problem could cause more severe performance degradation than the data locality problem because heavy congestion on memory controllers could cause long latencies. However, conventional work on MPI process placement has focused on locality to minimize the remote-access communication. Moreover, maximizing the locality may actually degrade performance because the load imbalance among nodes in a modern NUMA system may increase. Thus, a process placement algorithm must be designed to consider memory congestion. In this paper, a method to reconcile both the locality and the memory congestion on modern NUMA systems is proposed. This method statically analyzes the application communication pattern to optimize the process placement. A data clustering method is applied to the time-series data of the MPI communications in order to identify data traffics that potentially cause memory congestion. The proposed method has been evaluated with the NPB kernels on a real NUMA system and a simulation environment. Experimental results show that the proposed method can achieve 1.6x performance improvement compared with the current state-of-the-art strategy.