Web Log Mining And Adaptive Web Site Development

As the application and the scale of the Web increase fast. It becomes an extremely challenging research direction to apply data mining techniques to the Web. Discovery for useful and important knowledge (including patterns, association rules and visible structures) from the Web log on the server is becoming another important research and application area. The author made in-depth research and analysis in Web log mining. In this paper, by mining Web log,some user browsing patterns are discovered such as association rule, clustering pattern, accessing path and so on. Then those patterns are applied to design Web site and improve Web function. The works that has been done can be stated as below:1. This paper introduces basic conception and classification of Web mining, especially principles and methods. The author also points out the applications of Web mining.2. In order to organize the Web server architecture more logically, Web log mining is needed to analyze user's browsing patterns. This paper studies the data preprocessing phase of Web log mining, which is the key to get good mining results and presents a data preprocessing model including middle steps like data cleaning, user recognition, session recognition, and path supplementation. Also, each step is demonstrated through an example.3. Based on User Session File, which is the result of the preprocessing phase of Web Log Mining, this paper presents a Web Log Mining method for the recognition of user's browse patterns under the Extended Oriented-Tree model. Further more, the method is implemented basically in our lab and tested using real Web log data.4. Recommendation is the kernel of Web personalization. In this paper, we propose an automatic layered recommendation algorithm, which uses page layering to automatically choose the optimal matching granularity and to make recommendation based on frequent navigation paths. The experimental results show that it greatly reduces online cost, and can be successfully applied to Web log mining.5. This paper presents the design of the adaptive Web site, which is an application of the Web Log Mining.