Knowledge discovery from users Web-page navigation

Abstract

The authors propose to detect users' navigation paths to the advantage of Web site owners. First, they explain the design and implementation of a profiler which captures a client's selected links and page order, accurate page viewing time and cache references, using a Java based remote agent. The information captured by the profiler is then utilized by a knowledge discovery technique to cluster users with similar interests. They introduce a novel path clustering method based on the similarity of the history of user navigation. This approach is capable of capturing the interests of the user which could persist through several subsequent hypertext link selections. Finally, they evaluate their path clustering technique via a simulation study on a sample WWW site. They show that, depending on the level of inserted noise, they can recover the correct clusters by 10%-27% of average error margin.