The effect of sample size on the extended self-organizing map network—A market segmentation application

Abstract

Kohonen's self-organizing map (SOM) network maps input data to a lower dimensional output map. The extended SOM network further groups the nodes on the output map into a user specified number of clusters. Kiang, Hu and Fisher used the extended SOM network for market segmentation and showed that the extended SOM provides better results than the statistical approach that reduces the dimensionality of the problem via factor analysis and then forms segments with cluster analysis. In this study, we examined the effect of sample size on the extended SOM compared to that on the factor/cluster approach. Two sampling schemes, one with random sampling and the other one with proportionate sampling were used. Comparisons were made using the correct classification rates between the two approaches at various sample sizes. Unlike statistical models, neural networks are not dependent on statistical assumptions. Thus, the results for neural network models are stable across sample sizes but sensitive to initial weights and model specifications.