The size, complexity and dimensionality of data collections are ever increasing from the beginning of the computer era. Clustering is used to reveal structures and to reduce large amounts of raw data. There are two main issues when clustering based on unsupervised learning, such as Growing Neural Gas (GNG) [9], is performed on vast high dimensional data collection – the fast growth of computational complexity with respect to growing data dimensionality, and the specific similarity measurement in a high-dimensional space. These two factors reduce the effectiveness of clustering algorithms in many real applications. The growth of computational complexity can be partially solved using the parallel computation facilities, such as High Performance Computing (HPC) cluster with MPI. An effective parallel implementation of GNG is discussed in this paper, while the main focus is on minimizing of interprocess communication. The achieved speed-up was better than previous approach and the results from the standard and parallel version of GNG are same.