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Cluster analysis using a validated self‐organizing method: cases of problem identification

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  • Shouhong Wang

Abstract

Kohonen's self‐organizing feature maps (SOFM) are commonly used in cluster analysis for problem solving. However, given a set of sample data, the cluster analysis results obtained by using the standard SOFM method can vary depending on the setting of the parameters of the SOFM. To validate the cluster analysis results, information in addition to the data for the SOFM is required. This paper reports practical cases of cluster analysis using SOFM in conjunction with a measure of validation. In these cases, multivariate data of survey were used to identify problems through validated SOFM cluster analyses. Copyright © 2001 John Wiley & Sons, Ltd.

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  • Shouhong Wang, 2001. "Cluster analysis using a validated self‐organizing method: cases of problem identification," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 10(2), pages 127-138, June.
    • Handle: RePEc:wly:isacfm:v:10:y:2001:i:2:p:127-138
    DOI: 10.1002/isaf.198
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    1. Masson, Egill & Wang, Yih-Jeou, 1990. "Introduction to computation and learning in artificial neural networks," European Journal of Operational Research, Elsevier, vol. 47(1), pages 1-28, July.
    2. Kulkarni, Uday R. & Kiang, Melody Y., 1995. "Dynamic grouping of parts in flexible manufacturing systems -- a self-organizing neural networks approach," European Journal of Operational Research, Elsevier, vol. 84(1), pages 192-212, July.
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    Cited by:

    1. Azarnoush Ansari & Arash Riasi, 2016. "Customer Clustering Using a Combination of Fuzzy C-Means and Genetic Algorithms," International Journal of Business and Management, Canadian Center of Science and Education, vol. 11(7), pages 1-59, June.
    2. Daniel E. O'Leary, 2009. "Downloads and citations in Intelligent Systems in Accounting, Finance and Management," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 16(1‐2), pages 21-31, January.

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