Advertisement

Applying Modified Fuzzy Neural Network to Customer Classification of E-Business

  • Yukun Cao
  • Yunfeng Li
  • Xiaofeng Liao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3828)

Abstract

With the increasing interest and emphasis on customer demands in e-commerce, customer classification is in a crucial position for the development of e-commerce in response to the growing complexity in Internet commerce logistical markets. As such, it is highly desired to have a systematic system for extracting customer features effectively, and subsequently, analyzing customer orientations quantitatively. This paper presents a new approach that employs a modified fuzzy neural network based on adaptive resonance theory to group users dynamically based on their Web access patterns. Such a customer clustering method should be performed prior to Internet bookstores as the basis to provide personalized service. The experimental results of this clustering technique show the promise of our system.

Keywords

Radial Basis Function Neural Network User Interest Customer Behavior Adaptive Resonance Theory Vigilance Parameter 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Brooks, C.: Linear and non-Linear forecastability of high-frequency exchange rates. J. Forecast 16, 125–145 (1997)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Chen, C.H., Khoo, L.P.: Multicultural factors evaluation on elicited customer requirements for product concept development. In: Proceedings of 16th Interneational Conference for Production Research (ICPR-16), July 29-August 3, pp. 15–23 (2001)Google Scholar
  3. 3.
    Yan, W., Chen, C.H.: A radial basis function neural network multicultural factors evaluation engine for product concept development. Expert System 18(5), 219–232 (2001)zbMATHCrossRefGoogle Scholar
  4. 4.
    Cotrell, M., Girard, B.: Forcasting of curves using a Kohonen classification. J. Forecast 17, 429–439 (1998)CrossRefGoogle Scholar
  5. 5.
    Curry, B., Davies, F.: The Kohonen self-organizing map: an application to the study of strategic groups in the UK hotel industry. Expert System 18(1), 19–30 (2002)CrossRefGoogle Scholar
  6. 6.
    Lee, S.C., Suh, Y.H.: A cross-national market segmentation of online game industry using SOM. Expert Systems with Application 27, 559–570 (2004)CrossRefGoogle Scholar
  7. 7.
    Rangarajan, S.K., Pboba, V.V.: Adaptive Neural Network Clustering of Web Users. Computer 4, 34–40 (2004)CrossRefGoogle Scholar
  8. 8.
    Hu, T.-L., Sheu, J.-B.: A fuzzy-based customer classification method for demand-responsive logistical distribution operations. Fuzzy Sets and System 139, 431–450 (2003)zbMATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Carpenter, G.A.: Fuzzy ART: fast stable learning and categorization of analog patterns by an adaptive resonance system. Neural Networks 4, 759–771 (1991)CrossRefGoogle Scholar
  10. 10.
    Cinque, L., Foresti, G.: A clustering fuzzy approach for image segmentation. Pattern Recognition 37, 1797–1807 (2004)zbMATHCrossRefGoogle Scholar
  11. 11.
    Baraldi, A., Alpaydm, E.: Simplified ART: a new class of ART algorithms, International Computer Science Institute, Berkeley CA,1998Google Scholar
  12. 12.
    Kohonen, T.: Self-Organizing Maps, 2nd Extended edn., Springer Series in Information Sciences, vol. 30, Berlin, Heidelberg, New York (1997)Google Scholar
  13. 13.
    Haykin, S.: Neural Networks: A Comprehensive Foundation, 2nd edn. Prentice Hall, Englewood Cliffs (1999)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Yukun Cao
    • 1
  • Yunfeng Li
    • 1
  • Xiaofeng Liao
    • 1
  1. 1.Department of Computer ScienceChongqing UniversityChongqingP.R.China

Personalised recommendations