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Web Usage Mining: Discovering Usage Patterns for Web Applications

  • Chapter
Advanced Techniques in Web Intelligence-2

Part of the book series: Studies in Computational Intelligence ((SCI,volume 452))

Abstract

The heterogeneous nature of the Web combined with the rapid diffusion of Web-based applications have made Web browsing an intricate activity for users. This has given rise to an urgent need for developing systems capable to assist and guide users during their navigational activity in the Web. Web Usage Mining (WUM) refers to the application of Data Mining techniques for the automatic discovery of meaningful usage patterns characterizing the browsing behavior of users, starting from access data collected from interactions of users with sites. The discovered patterns may be conveniently exploited in order to implement functionalities offering useful assistance to users. This chapter is mainly intended to provide an overview of the different stages involved in a general WUM process. As an example, a WUM approach is presented which is based on the use of fuzzy clustering to discovery user categories starting from usage patterns.

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Authors and Affiliations

  1. Department of Informatics, University of Bari “A. Moro”, Via Orabona, 4, 70125, Bari, Italy

    Giovanna Castellano, Anna M. Fanelli & Maria A. Torsello

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  1. Giovanna Castellano
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  2. Anna M. Fanelli
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  3. Maria A. Torsello
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Correspondence to Giovanna Castellano .

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  1. School of Engineering & Science, Dept. Industrial Engineering, University of Chile Republica, Santiago, Chile

    Juan D. Velásquez

  2. (OUCL), Oxford University Computing Laboratory, Parks Road, Oxford, OX1 3QD, United Kingdom

    Vasile Palade

  3. , School of Electrical and Information, University of South Australia, Mawson Lakes Campus, Adelaide, 5095, Australia

    Lakhmi C. Jain

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Castellano, G., Fanelli, A.M., Torsello, M.A. (2013). Web Usage Mining: Discovering Usage Patterns for Web Applications. In: Velásquez, J., Palade, V., Jain, L. (eds) Advanced Techniques in Web Intelligence-2. Studies in Computational Intelligence, vol 452. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33326-2_4

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