Integration of self-organizing feature map and K-means algorithm for market segmentation

Cluster analysis is a common tool for market segmentation. Conventional research usually employs the multivariate analysis procedures. In recent years, due to their high performance in engineering, artificial neural networks have also been applied in the area of management. Thus this study aims to compare three clustering methods: (1) the conventional two-stage method, (2) the self-organizing feature maps and (3) our proposed two-stage method, via both simulated and real-world data. The proposed two-stage method is a combination of the self-organizing feature maps and the K-means method. The simulation results indicate that the proposed scheme is slightly better than the conventional two-stage method with respect to the rate of misclassification, and the real-world data on the basis of Wilk's Lambda and discriminant analysis.