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The comparative ability of self-organizing neural networks to define cluster structure

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  • Chen, S. K.
  • Mangiameli, P.
  • West, D.

Abstract

The ability to determine clusters or similarity in large, multivariate data sets is critical to many business decisions. Unfortunately, current cluster algorithms are sensitive to dispersion that occurs naturally in empirical data. As the level of relative cluster dispersion in the data increases, current clustering techniques fail to accurately identify cluster membership. An improved clustering methodology is needed that produces more accurate cluster definitions than the methods commonly used today. Our research investigates the ability of specific neural network architectures utilizing unsupervised learning to recover cluster structure from multivariate data sets with various levels of relative cluster dispersion. The results demonstrate that the Self Organizing Map network is a superior clustering technique and that its relative advantage over conventional techniques increases with higher levels of relative cluster dispersion in the data.

Suggested Citation

  • Chen, S. K. & Mangiameli, P. & West, D., 1995. "The comparative ability of self-organizing neural networks to define cluster structure," Omega, Elsevier, vol. 23(3), pages 271-279, June.
    • Handle: RePEc:eee:jomega:v:23:y:1995:i:3:p:271-279
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    References listed on IDEAS

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    1. Giuliano, Genevieve & Small, Kenneth A., 1991. "Subcenters in the Los Angeles region," Regional Science and Urban Economics, Elsevier, vol. 21(2), pages 163-182, July.
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    4. Glenn Milligan, 1980. "An examination of the effect of six types of error perturbation on fifteen clustering algorithms," Psychometrika, Springer;The Psychometric Society, vol. 45(3), pages 325-342, September.
    5. Glenn Milligan, 1985. "An algorithm for generating artificial test clusters," Psychometrika, Springer;The Psychometric Society, vol. 50(1), pages 123-127, March.
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    1. James R. Coakley & Carol E. Brown, 2000. "Artificial neural networks in accounting and finance: modeling issues," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 9(2), pages 119-144, June.
    2. Niels Waller & Heather Kaiser & Janine Illian & Mike Manry, 1998. "A comparison of the classification capabilities of the 1-dimensional kohonen neural network with two pratitioning and three hierarchical cluster analysis algorithms," Psychometrika, Springer;The Psychometric Society, vol. 63(1), pages 5-22, March.
    3. Gupta, V. K. & Chen, J. G. & Murtaza, M. B., 1997. "A learning vector quantization neural network model for the classification of industrial construction projects," Omega, Elsevier, vol. 25(6), pages 715-727, December.

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