User Select A Path-based And Browser Page Clustering Algorithm Research

Mankind has entered the Internet era.The development of network technology offers a new world to the teaching of online education. Web-based teaching system has a vast amount of information.But Teachers lack understanding of the situation on students'learning. It does not meet the needs of individualized learning. With the Use of Web data mining technology, we could learn who are similar with each other from student Internet learning behavior, what is interesting path. It can help teachers adjust teaching plan and update network site structure.This article makes research on Web log mining system. Follow the steps for mining the Web log, we firstly made research on the Web log preprocessing, it divided into six steps:data collection, data cleaning, user identification, session identification, path supplementary, transaction identification.We researched on their theory, algorithms. And on this basis, we improved the transaction recognition algorithm which omitted to add the path. Secondly, we make research on user clustering algorithm. We focused on the clustering algorithm based on hamming distance, which only took the times of the user access into account, ignoring the users'behavior in the URL and residence time. We proposed a user interestingness, which combined the interest of choosing the path with the interest in browsing page. On the basis, we proposed a clustering algorithm, and applied it to user clustering and browsing path.In these studies, we designed the Web Log Mining System based on user interest rate. The system was realized by the JSP, which can help administrators/teachers to understand the behavior of students when they visits the site.It also help to improve the structure of the site.