Using an Extended Self-Organizing Map Network to Forecast Market Segment Membership
Melody Y. Kiang (California State University-Long Beach, USA), Dorothy M. Fisher (California State University-Dominguez Hills, USA), Michael Y. Hu (Kent State University, USA) and Robert T. Chi (California State University, Long Beach, USA)
Copyright: © 2004
This chapter presents an extended Self-Organizing Map (SOM) network and demonstrates how it can be used to forecast market segment membership. The Kohonen’s SOM network is an unsupervised learning neural network that maps n-dimensional input data to a lower dimensional (usually one- or two-dimensional) output map while maintaining the original topological relations. We apply an extended version of SOM networks that further groups the nodes on the output map into a user-specified number of clusters to a residential market data set from AT&T. Specifically, the extended SOM is used to group survey respondents using their attitudes towards modes of communication. We then compare the extended SOM network solutions with a two-step procedure that uses the factor scores from factor analysis as inputs to K-means cluster analysis. Results using AT&T data indicate that the extended SOM network performs better than the two-step procedure.