Effective web log mining and online navigational pattern prediction
Guerbas, A. Addam, O. Zaarour, O. Nagi, Mohamad Elhajj, Ahmad Ridley, Mick J. Alhajj, R.
Guerbas, A.
Addam, O.
Zaarour, O.
Nagi, Mohamad
Elhajj, Ahmad
Ridley, Mick J.
Alhajj, R.
Publication Date
2013-09
End of Embargo
Supervisor
Rights
Peer-Reviewed
Yes
Open Access status
closedAccess
Accepted for publication
Institution
Department
Awarded
Embargo end date
Additional title
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.
Version
No full-text 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
Link to published version
Link to Version of Record
Type
Article