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

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  • Melody Y. Kiang

    (Information Systems Department, College of Business Administration, California State University at Long Beach, Long Beach, California 90840)

  • Ajith Kumar

    (Department of Marketing, College of Business, Arizona State University, Tempe, Arizona 85287)

Abstract

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.

Suggested Citation

  • Melody Y. Kiang & Ajith Kumar, 2001. "An Evaluation of Self-Organizing Map Networks as a Robust Alternative to Factor Analysis in Data Mining Applications," Information Systems Research, INFORMS, vol. 12(2), pages 177-194, June.
    • Handle: RePEc:inm:orisre:v:12:y:2001:i:2:p:177-194
    DOI: 10.1287/isre.12.2.177.9696
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    References listed on IDEAS

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    2. Lee G. Cooper & Giovanni Giuffrida, 2000. "Turning Datamining into a Management Science Tool: New Algorithms and Empirical Results," Management Science, INFORMS, vol. 46(2), pages 249-264, February.
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    2. R Setiono & S-L Pan & M-H Hsieh & A Azcarraga, 2005. "Automatic knowledge extraction from survey data: learning M-of-N constructs using a hybrid approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(1), pages 3-14, January.
    3. Francesca Fortunato & Laura Anderlucci & Angela Montanari, 2020. "One‐class classification with application to forensic analysis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(5), pages 1227-1249, November.
    4. Kiang, Melody Y. & Hu, Michael Y. & Fisher, Dorothy M., 2007. "The effect of sample size on the extended self-organizing map network--A market segmentation application," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 5940-5948, August.
    5. Mostafa, Mohamed M. & El-Masry, Ahmed A., 2016. "Oil price forecasting using gene expression programming and artificial neural networks," Economic Modelling, Elsevier, vol. 54(C), pages 40-53.
    6. Nai-Hua Chen & Stephen Huang & Shih-Tung Shu & Tung-Sheng Wang, 2013. "Market segmentation, service quality, and overall satisfaction: self-organizing map and structural equation modeling methods," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(2), pages 969-987, February.

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