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Compi?gne University of Technology, France
Sept. 19, 2005 to Sept. 22, 2005
ISBN: 0-7695-2415-X
pp: 179-185
I-Hsien Ting , University of York
Chris Kimble , University of York
Daniel Kudenko , University of York
ABSTRACT
This paper describes a novel web usage mining approach to discover patterns in the navigation of websites known as Unexpected Browsing Behaviours (UBBs). By reviewing these UBBs, a website designer can choose to modify the design of their website or redesign the site completely. UBB mining is based on the Continuous Common Subsequence (CCS), a special instance of Common Subsequence (CS), which is used to define a set of expected routes. The predefined expected routes are then treated as rules and stored in a rule base. By using the predefined route and the UBB mining algorithm, interesting browsing behaviours can be discovered. This paper will introduce the format of the expected route and describe the UBB algorithms. The paper also describes a series of experiments designed to evaluate how well UBB mining algorithms work.
INDEX TERMS
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CITATION
I-Hsien Ting, Chris Kimble, Daniel Kudenko, "UBB Mining: Finding Unexpected Browsing Behaviour in Clickstream Data to Improve a Web Site?s Design", WI, 2005, Web Intelligence, IEEE / WIC / ACM International Conference on, Web Intelligence, IEEE / WIC / ACM International Conference on 2005, pp. 179-185, doi:10.1109/WI.2005.153