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

Abstract

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. Scope and purpose The general idea of segmentation, or clustering, is to group items that are similar. A commonly used method is the multivariate analysis [4]. These methods consist of hierarchical methods, like Ward's minimum variance method, and the non-hierarchical methods, such as the K-means method. Owing to increase in computer power and decrease in computer costs, artificial neural networks (ANNs), which are distributed and parallel information processing systems successfully applied in the area of engineering, have recently been employed to solve the marketing problems. This study aims to discuss the possibility of integrating ANN and multivariate analysis. A two-stage method, which first uses the self-organizing feature maps to determine the number of clusters and the starting point and then employs the K-means method to find the final solution, is proposed. This method provides the marketing analysts a more sophisticated way to analyze the consumer behavior and determine the marking strategy. A case study is also employed to demonstrate the validity of the proposed method.