An extended self-organizing map network for market segmentation: a telecommunication example

Authors:
Melody Y. Kiang;Michael Y. Hu;Dorothy M. Fisher
Affiliations:
Information Systems Department, College of Business Administration, California State University, Long Beach, CA;Graduate School of Management, Kent State University;Information Systems Department, School of Business and Public Administration, California State University, Dominguez Hills
Venue:
Decision Support Systems
Year:
2006

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Abstract

Kohonen's self-organizing map (SOM) network is an unsupervised learning neural network that maps an n-dimensional input data to a lower dimensional output map while maintaining the original topological relations. The extended SOM network further groups the nodes on the output map into a user specified number of clusters. In this research effort, we applied this extended version of SOM networks to a consumer data set from American Telephone and Telegraph Company (AT&T). Results using the AT&T data indicate that the extended SOM network performs better than the two-step procedure that combines factor analysis and K-means cluster analysis in uncovering market segments.