Int. J. of Business Intelligence and Data Mining   »   2012 Vol.7, No.1/2

 

 

Title: Robust framework for recommending restructuring of websites by analysing web usage and web structure data

 

Authors: Mohamad Nagi; Ahmad Elhajj; Omar Addam; Ala Qabaja; Omar Zarour; Tamer Jarada; Shang Gao; Jamal Jida; Ayman Murshed; Iyad Sleiman; Tansel Özyer; Mick Ridley; Reda Alhajj

 

Addresses:
School of Computing, University of Bradford, Bradford, UK.
School of Computing, University of Bradford, Bradford, UK.
Department of Computer Science, University of Calgary, Calgary, Alberta, Canada.
Department of Computer Science, University of Calgary, Calgary, Alberta, Canada.
Department of Computer Science, University of Calgary, Calgary, Alberta, Canada.
Department of Computer Science, University of Calgary, Calgary, Alberta, Canada.
Department of Computer Science, University of Calgary, Calgary, Alberta, Canada.
Department of Informatics, Lebanese University, Tripoli, Lebanon.
Department of Information Technology, Jordan University, Amman, Jordan.
School of Computing, University of Bradford, Bradford, UK.
Department of Computer Engineering, TOBB Economics and Technology University, Ankara, Turkey.
School of Computing, University of Bradford, Bradford, UK.
Department of Computer Science, University of Calgary, Calgary, Alberta, Canada; Department of Computer Science, Global University, Beirut, Lebanon

 

Abstract: The work described in this paper is motivated by the fact that the structure of a website may not satisfy a larger population of the visiting users who may jump between pages of the website before they land on the target page(s); this is at least partially true because access patterns were not known when the website was designed. We developed a robust framework that tackles this problem by considering both web log data and web structure data to suggest a more compact structure that could satisfy a larger user group. The study assumes the trend recorded so far in the web log reflects well the anticipated behaviour of the users in the future. We separately analyse web log and web structure data using three techniques, namely clustering, frequent pattern mining and network analysis. The final outcome from the two stages is reflected on to one of the six models, namely the network of pages to report linking pages by the most appropriate connections.

 

Keywords: web usage mining; web log data; web structure mining; network analysis; clustering; frequent pattern mining; website structures; website restructuring; user behaviour; data mining.

 

DOI: 10.1504/IJBIDM.2012.048725

 

Int. J. of Business Intelligence and Data Mining, 2012 Vol.7, No.1/2, pp.4 - 20

 

Available online: 23 Aug 2012

 

 

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