Intelligent Web Mining

  1. Menasalvas, Ernestina 1
  2. Marbán, Oscar 2
  3. Millán, Socorro 3
  4. Peña, Jose M. 1
  1. 1 Universidad Politécnica de Madrid
    info

    Universidad Politécnica de Madrid

    Madrid, España

    ROR https://ror.org/03n6nwv02

  2. 2 Universidad Carlos III de Madrid
    info

    Universidad Carlos III de Madrid

    Madrid, España

    ROR https://ror.org/03ths8210

  3. 3 Universidad del Valle (Colombia)
    info

    Universidad del Valle (Colombia)

    Santiago de Cali, Colombia

    ROR https://ror.org/00jb9vg53

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Libro:
Intelligent Exploration of the Web. Studies in Fuzziness and Soft Computing, vol 111

Editorial: Physica

ISSN: 1434-9922,  1860-0808

ISBN: 9783790825190,  9783790817720

Año de publicación: 2003

Páginas: 363-388

Tipo: Capítulo de Libro

DOI: 10.1007/978-3-7908-1772-0_22 GOOGLE SCHOLAR lock_openAcceso abierto editor

Resumen

Explosive growth in size and usage of the World Wide Web has made it Necessary for Web site administrators to track and analyze the navigation patterns of Web site visitors. However, data mining techniques are not easily applicable to Web data due to problems both related with the technology underlying the Web and the lack of standards in the design and implementation of Web pages. Information collected by Web servers and kept in the server log is the main source of data for analyzing user navigation patterns.Once logs have been preprocessed and sessions have been obtained there are several kinds of access pattern mining that can be performed depending on the needs of the analyst. It is important to mention that most efforts have relied on relatively simple techniques which can be inadequate for real user profile data since noise in the data has to be firstly tacked. Thus, there is a need for robust methods that integrates different intelligent techniques that are free of any assumptions about the noise contamination rate.In this paper, the problem of mining behavior patterns on the Web is studied in detail and different approaches to solve the problem are analyzed. An algorithm is given to calculate frequent access patterns. This algorithm is based on a model structure that has been called WPC-Tree that stores in each node relevant information about pages that make it possible to apply data mining techniques to obtain useful patterns.

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