title: Fast self-organizing feature map algorithm.
abstract: We present an efficient approach to forming feature maps. The method involves three stages. In the first stage, we use the K-means algorithm to select N2 (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 the N2 selected data points into an N x N neural array so as to form an initial feature map. If the initial map is not good enough, then it will be fine-tuned by the traditional Kohonen self-organizing feature map (SOM) algorithm under a fast cooling regime in the third stage. By our three-stage method, a topologically ordered feature map would be formed very quickly instead 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.
authors.affiliation: Department of Electrical Engineering, Tamkang University, Tamkang, Taiwan, R.O.C. muchun@ee.tku.edu.tw
authors.forename: M C
authors.initials: MC
authors.lastname: Su
authors.forename: H T
authors.initials: HT
authors.lastname: Chang