Fast self-organizing feature map algorithm

We present an efficient approach to forming feature maps. The method involv es three stages. In the first stage, we use the K-means algorithm to select N-2 (i.e., the size of the feature map to be formed) cluster centers from a data set, Then a heuristic assignment strategy is employed to organize th e N-2 selected data points into an N x N neural array so as to form an init ial feature map. If the initial map is not good enough, then it will be fin e-tuned by the traditional Kohonen self-organizing feature map (SOM) algori thm under a fast cooling regime in the third stage. By our three-stage meth od, a topologically ordered feature map would be formed very quickly instea d of requiring a huge amount of iterations to fine-tune the weights toward the density distribution of the data points, which usually happened in the conventional SOM algorithm, Three data sets are utilized to illustrate the proposed method.