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Integrating Genetic Algorithms and Fuzzy Logic for Web Structure Optimization

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Data Mining for Social Network Data

Part of the book series: Annals of Information Systems ((AOIS,volume 12))

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Abstract

This chapter addresses the restructuring of Websites by an approach that integrates fuzziness weighted page rank (WPR) index and log rank index for pages of the considered Website. Fuzzy logic gives a degree of a membership to a problem and, hence, more adequately describes reasoning to a problem than a numeric deviation value does (the difference between the WPR index and log rank index), which does not give accurate human reasoning. Using fuzzy logic, the computational program translates a deviation value to a fuzzy representation by producing statements like “page A has a low restructuring factor by degree 0.8.” However, without well-defined membership functions, a fuzzy value can be as meaningless as or even worse than a deviation value. Accordingly, we have shown how genetic algorithms (GA) can be applied to optimize the fuzzy membership functions. This chapter demonstrates how fuzzy logic can be applied to a deviation value to better represent the degree of restructuring.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Calgary, Calgary, AB, Canada

    Iltae Lee, Negar Koochakzadeh, Keivan Kianmehr, Reda Alhajj & Jon Rokne

  2. Department of Computer Science, Global University, Beirut, Lebanon

    Reda Alhajj

Authors
  1. Iltae Lee
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  2. Negar Koochakzadeh
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  3. Keivan Kianmehr
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  4. Reda Alhajj
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  5. Jon Rokne
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Corresponding author

Correspondence to Reda Alhajj .

Editor information

Editors and Affiliations

  1. , Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Campusvej 55, Odense M, 5230, Denmark

    Nasrullah Memon

  2. , Dept. Computer Information Systems, Bentley University, Forest St. 175, Waltham, 02452, USA

    Jennifer Jie Xu

  3. , Dept. Computer Science & Engineering, Aalborg University Esbjerg, Niels Bohrs Vej 8, Esbjerg, 6700, Denmark

    David L. Hicks

  4. Eller College of Management, University of Arizona, E. Helen St. 1130, Tucson, 85721, Arizona, USA

    Hsinchun Chen

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Lee, I., Koochakzadeh, N., Kianmehr, K., Alhajj, R., Rokne, J. (2010). Integrating Genetic Algorithms and Fuzzy Logic for Web Structure Optimization. In: Memon, N., Xu, J., Hicks, D., Chen, H. (eds) Data Mining for Social Network Data. Annals of Information Systems, vol 12. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-6287-4_11

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