Modified weight initialization in the self-organizing map using Nguyen-Widrow initialization algorithm

Abstract

Self-organizing map (SOM) is a neural network trained using unsupervised learning for clustering. The clustering result and learning speed of SOM, however, is dependent on the initial weights which are randomly initialized with a low close to zero value from the range of vectors within the given input space data negatively affecting training speed and clustering result. The study used the Nguyen-Widrow initialization algorithm to initialize the weights of SOM and speed up the training process. The cluster error rate and the number of iterations to converge to final clustering is recorded and compared with the traditional SOM to determine the performance of the modified SOM. The result reveals that the modified SOM algorithm produces better cluster results and improved training speed as compared to traditional SOM. Hence, Nguyen-Widrow algorithm for initialization of weights in the SOM yields better cluster result and improved training speed of the algorithm in terms of the number of iterations.