We propose to detect users navigationpaths to the advantage of web-site owners. First, we explain the design and implementationof a profiler which captures client’s selected links and pages 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. We 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, we evaluate our path clustering technique via a simulation study on a sample WWW-site. We show that depending on the level of inserted noise, we can recover the correct clusters by %10-%27 of average error margin. 1.