Improving the quality of Self-Organizing Map by "Different elements" competitive strategy

Abstract

A Self-Organizing Map (SOM) has good quality when both of its measures, quantization error (QE) and topographic error (TE), are small. Many researchers have tried to reduce these measures by improving SOM's learning algorithm, however, most results only decrease either QE or TE. In this paper, a method to improve the quality of the map obtained when the SOM's learning algorithm ended is proposed. The proposed method re-adjusts weight vector of each neuron according to the cluster's center that neuron represents and optimizes clusters by "different elements'' competitive strategy. In this method, QE always decreases each time the competition "different elements'' occurs between all neurons, TE may reduce when the competition "different elements'' occurs between adjacent neighbors. The experiments are performed on assumed datasets and real data sets. As the results, the average reduction ratio of QE is from 50% to 60%, TE gets the average reduction ratio from 10% to 20%. This reduction ratio is larger than some other solutions but does not need to adjust the parameters for each specific dataset.