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Business Intelligence from Web Usage Mining

Author

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  • Ajith Abraham

    (Department of Computer Science, Oklahoma State University, 700 N Greenwood Avenue, Tulsa, Oklahoma 74106-0700, USA)

Abstract

The rapid e-commerce growth has made both business community and customers face a new situation. Due to intense competition on the one hand and the customer's option to choose from several alternatives, the business community has realized the necessity of intelligent marketing strategies and relationship management. Web usage mining attempts to discover useful knowledge from the secondary data obtained from the interactions of the users with the Web. Web usage mining has become very critical for effective Web site management, creating adaptive Web sites, business and support services, personalization, network traffic flow analysis and so on. This paper presents the important concepts of Web usage mining and its various practical applications. Further a novel approach called "intelligent-miner" (i-Miner) is presented.i-Minercould optimize the concurrent architecture of a fuzzy clustering algorithm (to discover web data clusters) and a fuzzy inference system to analyze the Web site visitor trends. A hybrid evolutionary fuzzy clustering algorithm is proposed to optimally segregate similar user interests. The clustered data is then used to analyze the trends using a Takagi-Sugeno fuzzy inference system learned using a combination of evolutionary algorithm and neural network learning. Proposed approach is compared with self-organizing maps (to discover patterns) and several function approximation techniques like neural networks, linear genetic programming and Takagi–Sugeno fuzzy inference system (to analyze the clusters). The results are graphically illustrated and the practical significance is discussed in detail. Empirical results clearly show that the proposed Web usage-mining framework is efficient.

Suggested Citation

  • Ajith Abraham, 2003. "Business Intelligence from Web Usage Mining," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 2(04), pages 375-390.
    • Handle: RePEc:wsi:jikmxx:v:02:y:2003:i:04:n:s0219649203000565
    DOI: 10.1142/S0219649203000565
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    References listed on IDEAS

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    1. Rivkin, Steven G., 2003. "A Notion at Risk: Edited by Richard D. Kahlenberg. New York, NY: A Century Foundation Book. 2000. pp. x+356. Price: $15.95 (paper)," Economics of Education Review, Elsevier, vol. 22(6), pages 643-644, December.
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    Cited by:

    1. David P. Donohue & Peter M. Murphy, 2016. "Supporting Competitive Intelligence at DuPont by Controlling Information Overload and Cutting Through the Noise," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 15(01), pages 1-14, March.
    2. Zahid A. Ansari & Syed Abdul Sattar & A. Vinaya Babu, 2017. "A fuzzy neural network based framework to discover user access patterns from web log data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 11(3), pages 519-546, September.

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