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. | Journal of Computer Science and Cybernetics, , (2015), 215–229 DOI: IMPROVING THE QUALITY OF SELF-ORGANIZING MAP BY “DIFFERENT ELEMENTS” COMPETITIVE STRATEGY LE ANH TU Thai Nguyen University of Information and Communication Technology; anhtucntt@ 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 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 datasets. 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. Keywords. self-organizing map, competitive learning, different elements, quantization error, topographic error. 1. INTRODUCTION The SOM neural network was proposed by Teuvo Kohonen in 1980s [16]. This is a feedforward neural network model, using an unsupervised competitive learning algorithm. The SOM allows mapping data from multi-dimensional space to less dimensional one (normally 2 dimensions), which makes up the feature map of the data. So far, there have been many different variations of SOM proposed [5] and there are many studies showing that feature map’s quality of SOM .
