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    Effective web log mining and online navigational pattern prediction

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    Publication date
    2013-09
    Author
    Guerbas, A.
    Addam, O.
    Zaarour, O.
    Nagi, Mohamad
    Elhajj, Ahmad
    Ridley, Mick J.
    Alhajj, R.
    Keyword
    Web mining
    ; Weblog mining
    ;Pattern analysis
    ; Prediction
    ; Navigation
    ; Indexing
    ; Reconstruction
    ; Discovery
    Peer-Reviewed
    Yes
    
    Metadata
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    Abstract
    Accurate web log mining results and efficient online navigational pattern prediction are undeniably crucial for tuning up websites and consequently helping in visitors' retention. Like any other data mining task, web log mining starts with data cleaning and preparation and it ends up discovering some hidden knowledge which cannot be extracted using conventional methods. In order for this process to yield good results it has to rely on some good quality input data. Therefore, more focus in this process should be on data cleaning and pre-processing. On the other hand, one of the challenges facing online prediction is scalability. As a result any improvement in the efficiency of online prediction solutions is more than necessary. As a response to the aforementioned concerns we are proposing an enhancement to the web log mining process and to the online navigational pattern prediction. Our contribution contains three different components. First, we are proposing a refined time-out based heuristic for session identification. Second, we are suggesting the usage of a specific density based algorithm for navigational pattern discovery. Finally, a new approach for efficient online prediction is also suggested. The conducted experiments demonstrate the applicability and effectiveness of the proposed approach. (C) 2013 Elsevier B.V. All rights reserved.
    URI
    http://hdl.handle.net/10454/9660
    Version
    No full-text available in the repository
    Citation
    Guerbas A, Addam O, Zaarour O et al (2013) Effective web log mining and online navigational pattern prediction. Knowledge-Based Systems. 49: 50-62.
    Link to publisher’s version
    http://dx.doi.org/10.1016/j.knosys.2013.04.014
    Type
    Article
    Collections
    Engineering and Informatics Publications

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