基于聚类算法的Web日志挖掘

With the rapid development of the Internet, the log information in WWW website is now showing an explosive growth trend. In order to fully exploit the potential effective information in Web logs, this paper presents a log mining method which combines web log mining and clustering algorithm, and applies this method to specific video websites. Firstly, this method performs data cleaning on the obtained log data, user identification, time-based user session recognition, and extracting user interest feature. Then, this paper improves the K-means clustering algorithm to cluster the users and performs the model analysis. Finally, use the personalized recommendation technology to recommend popular movies to users. The experimental results show that the improved K-means algorithm can improve the convergence speed, and the clustering result shows high cohesion.