The research of web users´ behavior analysis based on Web Log Mining

With the coming of the time of Internet, the number of the websites is growing in geometric progression. How can withhold the customers and how to make websites stand out above the rest are the problems in managing the websites. This article aims to analyze the behavior of web users. Firstly log data were collected from the web server, after data preprocesssing, a URL-UserID relevant matrix is set up with URL as row and UserID as column, and each element value as the users´ hits. With the help of fuzzy clustering algorithms and Warshall algorithms, the similar customer groups can be discovered by measuring similarity between column vectors. By using the result of the model, we could analyze the behavior of users and improve the customer relationship management. Finally, Examples of the web log data of Guangdong Industry Technical College prove the validity and effectiveness of the algorithms.