Competitive Neural Networks for Customer Choice Models

  • Walter A. Kosters
  • Michiel C. van Wezel
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 105)


In this paper we propose and examine two different models for customer choices in for instance a wholesale department, given the actual sales. Both customers and products are modeled by points in a k-dimensional real vector space. Two possible strategies are discussed: in one model the customer buys the nearest option from categories of products, in the other he/she buys all products within a certain radius of his/her position. Now we deal with the following problem: given only the sales list, how can we retrieve the relative positions corresponding to customers and products? In particular we are interested in the dimension k of the space: we are looking for low dimensional solutions with a good “fit” to the real sales list. Theoretical complexity of these problems is addressed: they are very hard to solve exactly; special cases are shown to be NP-complete. We use competitive neural network techniques for both artificial and real life data, and report the results.


Neural Network Span Tree Weight Vector Product Category Product Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Anderson, J.A., Rosenfeld, E. (Eds.) (1988): Neurocomputing: Foundations of Research. MIT Press, CambridgeGoogle Scholar
  2. 2.
    Bishop, C.M. (1995): Neural Networks for Pattern Recognition. Clarendon-Press, OxfordGoogle Scholar
  3. 3.
    Davison, M.L. (1983): Multidimensional Scaling. John Wiley and Sons, New YorkGoogle Scholar
  4. 4.
    Fayyad, U., Uthurusamy, R. (1996): Data Mining and Knowledge Discovery in Databases. Communications of the ACM 39, 24–27CrossRefGoogle Scholar
  5. 5.
    Carey, M.R.., Johnson, D.S. (1979): Computers and Intractability: A Guide to the Theory of NP-Completeness. W.H. Freeman and Company, New YorkGoogle Scholar
  6. 6.
    Grossberg, S. (1976): Adaptive Pattern Classifications and Universal Recording, I: Parallel Development and Coding of Neural Feature Detectors. Biological Cybernetics 23, 121–134Google Scholar
  7. 7.
    Grossberg, S. (1980): How Does a Brain Build a Cognitive Code? Psychological Review 87, 1–51; reprinted in [1]Google Scholar
  8. 8.
    Haykin, S. (1999): Neural Networks: A Comprehensive Foundation, 2nd edition. Prentice Hall, Upper Saddle River, New JerseyGoogle Scholar
  9. 9.
    Hertz, J., Krogh, A., Palmer, E.G. (1991): Introduction to the Theory of Neural Computation. Addison-Wesley, Reading, MassachusettsGoogle Scholar
  10. 10.
    Kosters, W.A., La Poutré, H., Wezel, M.C. van (1997): Understanding Customer Choice Processes Using Neural Networks. In: Arner Jr, H.F. (Ed.): Proceedings of the First International Conference on the Practical Application of Knowledge Discovery and Data Mining (PADD’97), London, 167–178Google Scholar
  11. 11.
    Kotler, P. (1999): Marketing Management: Analysis, Planning, Implementation and Control, 9th edition. Prentice Hall, Upper Saddle River, New JerseyGoogle Scholar
  12. 12.
    Krzanowski, W.J. (1988): Principles of Multivariate Analysis. Oxford Statistical Science Series, Oxford University Press, OxfordGoogle Scholar
  13. 13.
    Leeuw, J. de, Heiser, W. (1982): Theory of Multidimensional Scaling. In: Krishnaiah, P.R,., Kanal, L.N. (Eds.): Handbook of Statistics 2: Classification, Pattern Recognition and Reduction of Dimensionality. North-Holland, Amsterdam, 285–316Google Scholar
  14. 14.
    Oyang, Y.-J., Chen, C.-Y., Yang, T.-W. (2001): A Study on the Hierarchical Data Clustering Algorithm Based on Gravity Theory. In: De Raedt, L., Siebes, A. (Eds.): Proceedings PKDD 2001 (Principles of Data Mining and Knowledge Discovery), Lecture Notes in Artificial Intelligence 2168, Springer, Berlin Heidelberg New York, 350–361Google Scholar
  15. 15.
    Wezel, M.C. van, Kok, J.N., Sere, K. (1996): Determining the Number of Dimensions Underlying Customer-Choices with a Competitive Neural Network. In: Proceedings of the IEEE International Conference on Neural Networks (ICNN’96), Washington D.C., 484–490CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Walter A. Kosters
    • 1
  • Michiel C. van Wezel
    • 1
  1. 1.Leiden Institute of Advanced Computer ScienceUniversiteit LeidenLeidenThe Netherlands

Personalised recommendations