The self-organizing map (SOM) is an unsupervised neural network which projects high-dimensional data onto a low-dimensional grid and preserving its 'topological' structure. In this paper, we propose an efficient self-organizing map method to improve the performance of the SOM. The proposed algorithm reduces the time complexity dramatically in finding the initial neurons. Finally, we use visualization methods to show the mapping from clustered results of SOM to a 3-D Euclidian space. By means of the assistance of graphic presentation, the outcome of SOM clustering process will achieve more reliability.