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An Evaluation of Self-Organizing Map Networks as a Robust Alternative to Factor Analysis in Data Mining Applications

Information Systems Department, College of Business Administration, California State University at Long Beach, Long Beach, California 90840
Department of Marketing, College of Business, Arizona State University, Tempe, Arizona 85287
mkiang{at}csulb.edu
ajith.kumar{at}asu.edu

Kohonen's self-organizing map (SOM) network is one of the most important network architectures developed during the 1980s. The main function of SOM networks is to map the input data from an n-dimensional space to a lower dimensional (usually one- or two-dimensional) plot while maintaining the original topological relations. Therefore, it can be viewed as an analog of factor analysis. In this research, we evaluate the feasibility of using SOM networks as a robust alternative to factor analysis and clustering for data mining applications. Specifically, we compare SOM network solutions to factor analytic and K-Means clustering solutions on simulated data sets with known underlying factor and cluster structures.

The comparisons indicate that the SOM networks provide solutions superior to unrotated factor solutions in general and provide more accurate recovery of underlying cluster structures when the input data are skewed. Our findings suggest that SOM networks can provide robust alternatives to traditional factor analysis and clustering techniques in data mining applications.

Key Words: Data Mining; Kohonen Networks; Factor Analysis; Data Reductive; Clustering Analysis
History: This paper was received on March 13, 1998.