Abstract
With the explosive growth of the Internet, it has entered the age led by ICP (internet content provider). Helping users to locate relevant information in an efficient manner is very important both do the person and to the ICPs. As such, it is highly desired to have a systematic system for extracting user features effectively, and subsequently, analyzing user orientations quantitatively. The experimental results of this clustering technique show the promise of our system. 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 user clustering method should be performed prior to ICPs as the basis to provide personalized service. The experimental results of this clustering technique show the promise of our system. The scheme could be used in local data management application, digital library, and so on.
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References
Brooks, C.: Linear and non-Linear forecastability of high-frequency exchange rates. J Forecast 16, 125–145 (1997)
Chen, C.H., Khoo, L.P.: Multicultural factors evaluation on elicited user requirements for product concept development. In: Proceedings of 16th Interneational Conference for Production Research (ICPR 16), July 29-August 3, pp. 15–23 (2001)
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)
Cotrell, M., Girard, B.: Forcasting of curves using a Kohonen classification. J Forecast 17, 429–439 (1998)
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)
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)
Rangarajan, S.K., Pboba, V.V.: Adaptive Neural Network Clustering of Web Users. Computer 4, 34–40 (2004)
Hu, T.-L., Sheu, J.-B.: A fuzzy-based user classification method for demand-responsive logistical distribution operations. Fuzzy Sets and System 139, 431–450 (2003)
Carpenter, G.A.: Fuzzy ART: fast stable learning and categorization of analog patterns by an adaptive resonance system. Neural Networks 4, 759–771 (1991)
Cinque, L., Foresti, G.: A clustering fuzzy approach for image segmentation. Pattern Recognition 37, 1797–1807 (2004)
Baraldi, A., Alpaydm, E.: Simplified ART: a new class of ART algorithms. International Computer Science Institute, Berkeley CA (1998)
Kohonen, T.: Self-Organizing Maps, 2nd Extended edn., Berlin, Heidelberg, New York. Springer Series in Information Sciences, vol. 30 (1997)
Haykin, S.: Neural Networks: A Comprehensive Foundation, 2nd edn. Prentice-Hall, Englewood Cliffs (1999)
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Li, Y., Cao, Y. (2005). A User Classification for Internet Content Provider Based Modified Fuzzy Neural Network. In: Fox, E.A., Neuhold, E.J., Premsmit, P., Wuwongse, V. (eds) Digital Libraries: Implementing Strategies and Sharing Experiences. ICADL 2005. Lecture Notes in Computer Science, vol 3815. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11599517_21
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DOI: https://doi.org/10.1007/11599517_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30850-8
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